In order to improve the seed supply performance of seed-supplying link of high-speed dense planting seeder of maize, an air-assisted spiral seed-supply device was designed and optimized. The kinetic model of maize seeds in the migration zone was established. Based on computational fluid dynamics (CFD) simulation, the pressure and velocity distribution in the axial plane were explored when the blower pressure was 4.0, 4.5, 5.0, 5.5, 6.0 kPa, respectively. A two-factor, five-level central composite design (CCD) experiment was conducted using blower pressure and rotational speed of spiral shaft as test factors and seed supply rate, coefficient of variation of seed supply rate stability and seed breakage rate as seed supply performance indicators. Explored the impact trend of interaction terms of factors on seed supply performance indicators. Based on the multi-objective variable optimization method, the optimal working parameter combination of the air-assisted spiral seed-supply device was determined and verified by bench experiments. The results showed that the optimal combination of working parameters was the blower pressure of 6.0 kPa and the rotational speed of spiral shaft of 80 r/min. Under the verification test, the seed supply rate, the coefficient of variation of seed supply rate stability and the seed breakage rate were 2933.21 g/min, 1.87% and 1.69%, respectively, and the relative error was within 5.5% compared with the optimized results. This study can provide a reference for the optimal design of the seed-supply device of the high-speed dense planting seeder.

To enhance the upright orientation rate of garlic clove bud tips, an air-suction garlic clove directional metering device with a guiding cam was developed. Key parameters influencing garlic clove discharge performance were determined through mechanical analysis. After optimization, the guiding cams thickness (D) was set at 4 mm, with the lead-in and the seeding section tilt angles(at,ß) of 5° and 15°, respectively. Comparative tests were conducted using optimal parameters, focusing on seed discharge disc rotational speed(n) and negative pressure(P) as main variables, with the upright rate of seeds in the receiving hopper serving as the evaluation index. Under conditions of -11.5 kPa negative pressure and 7 rad/s rotational speed, the upright rate reached 97.2%. Results demonstrated that the addition of guiding cams significantly improved the upright rate, increasing it by over 10% compared to directional metering devices without guiding cams.

To address the issue of reduced comfort and operational accuracy in tractors caused by the clutch engagement process in combination with automatic transmissions, dual-clutch transmissions, and hybrid power tractors, a new method is proposed that considers both the slipping process and synchronous instantaneous control at the moment of engagement. In this method, the optimal engagement process model of the clutch considering the clutch sliding process and synchronous instantaneous control was established, and the optimal trajectory of the clutch engagement process was solved based on the pseudo-spectral method. Then, the optimization results were compared with those obtained without considering the synchronous instantaneous. The results show that the proposed method considering the sliding process and synchronous instantaneous constraint can reduce the frictional loss of the clutch by 9%, and suppress the impact to below 10 m/s³. Finally, this method was applied to the control of tractor starting up, gear shifting and hybrid power mode switching processes. Simulation results demonstrate that this method can be effectively applied to these three operating conditions.

Aiming at the complexity and insufficient adaptability of current weeding devices for ridge tillage, this paper presents the design of a clamping-shear weeding device that mimics the hand-grabbing motion. Through the force analysis of the weed root system, the optimal shovel surface inclination angle is determined to be 10°~40°. To ensure the sliding cutting condition, the shovel blade angle is calculated and determined as 30°. Based on the Mohr-Coulomb shear theory, the weed resistance model is developed, and the shovel width is set at 50 mm based on the principle of minimum resistance. A single-factor test was conducted with the soil penetration depth of 40 mm and the clamping-shear speed of 4 cm/s, the results showed that the weed removal rate was over 85% and the crop injury rate was less than 6%, meeting the operational requirements of intra-row weeding. The optimal performance was observed with the shovel inclination angle of 30°.

The lack of effective visualization research methods for the crushing process of grass kneading has restricted the development of grass kneading machine to a certain extent. In this paper, the hay kneading process was numerically simulated by using the discrete element method, the stem movement rule, the mechanical characteristics of the stem particle group and the power consumption of the kneading were analyzed, and the motion and breaking mechanism of the grass in the hay kneading machine were defined. The effects of motor output speed and feed rate on the spinning rate and power consumption were studied. The regression equation between test factors and evaluation indexes was established. With the goal of maximizing the silk rates and minimizing the power consumption, the motor output speed and feeding amount are optimized and solved. The optimal parameter combination is determined as follows: when the output speed of the motor is 282.88r/min and the feeding amount is 1.86kg/s, the knead verification test shows that the silk rates is 92.88% and the power consumption is 3.68kJ. The research results provide a reference for realizing high efficiency and low power consumption of grass kneading and parameter optimization of kneading device.

The peach fruit moth was a fruit-eating pest and one of the major pests of fruit trees in China, Korea, Japan, and Australia. Due to long-term problems such as improper control methods, low technical quality, and untimely treatment, the yield and efficiency of fruit products were greatly affected, which constrained the development of the fruit industry. This paper developed a method for detecting adult peach fruit moths based on an improved YOLOv8m to address the challenging problem of manually detecting peach fruit moths. To increase the Receptive Field of the model, v7Down Sampling was introduced in its backbone network. Then, the channel-prioritized Convolutional Attention Mechanism Module (CPCA), which dynamically allocated the spatial attention weights on each channel, reducing the noise and the algorithms complexity, was incorporated. Finally, the inner-WIoU loss function was introduced to enhance the convergence and generalization of the bounding box. The precision (P) of the improved model increased by 3.4 percentage points compared to YOLOv8m. The recall (R) improved by 2.1 percentage points, and the mAP improved by 1.2 percentage points. The single-category precision (AP) for peach fruit moth detection improved by 2.4 percentage points. Moreover, the weight size, number of model parameters, and computational volume were reduced by 3.6MB, 1.8M, and 1.7G, respectively. This achieved an improvement in the models effectiveness in detecting adult peach fruit moths without increasing the models complexity. The results provided strong technical support for the subsequent real-time monitoring of the peach fruit moth.

Silk is a biomaterial with remarkable properties, used in various fields: the textile industry, aeronautics, medicine, etc. Sericulture is the practice of raising silkworms to obtain silk threads. This activity provides an opportunity to improve economic and social status, especially for rural populations. The high demand for labor, along with technical progress, has necessitated the implementation of technical solutions and modern activities in sericulture farms. This paper presents a brief analysis of the current state of research on some innovative technical solutions applied in sericulture sectors and activities, aimed at increasing silk thread production.

The feed rate is an important index for evaluating the performance of a combine harvester. Determining how to accurately reflect the feed rate during harvesting and establishing a reliable detection model is a major focus of current research and an important basis for the next step of feed rate stable control. This paper provides an overview of feed rate detection methods and stable control techniques for combine harvesters. It reviews methods that estimate the feed rate based on the inclined conveyor extrusion pressure, header power, and threshing unit energy consumption. Additionally, it introduces machine learning-based methods that incorporate multiple influencing factors to predict the feed rate. A comparison of different noise reduction techniques used in feed rate detection is also presented, analyzing their effectiveness. Furthermore, this study examines feed rate control methods in combine harvesters, discussing various control approaches with an emphasis on methods that stabilize the feed rate by adjusting header height and harvester forward speed. In response to the current issues of inadequate detection accuracy in feed rate monitoring, limited adaptability, and instability in control systems, it is pointed out that future research needs to innovate in developing advanced sensor technology, optimizing automatic control algorithms as well as data fusion and analytical methodologies.

Conventional simultaneous localization and mapping (SLAM) systems for agricultural robots rely heavily on static rigidity assumptions, which makes it susceptible to the influence of dynamic target feature points in the environment thus leading to poor localization accuracy and robustness of the system. To address the above issues, this paper proposes a method that utilizes a target detection algorithm to identify and eliminate dynamic target feature points in a farm depot. The method initially employs the YOLOv5 target detection algorithm to recognize dynamic targets in the captured warehouse environment images. The detected targets are then integrated into the feature extraction process at the front end of the visual SLAM. Next, dynamic feature points belonging to the dynamic target part are eliminated from the extracted image feature points using the LK optical flow method. Finally, the remaining feature points are used for location matching, map construction and localization. The final test on the TUM dataset shows that the enhanced vision SLAM system improves the localization accuracy by 91.47% compared to ORB-SLAM2 in highly dynamic scenes. This improvement increases the accuracy and robustness of the system and outperforms some of the best SLAM algorithms while maintaining high real-time performance. These features make it more valuable for mobile devices.

Water quality is a critical factor in shrimp farming, directly influencing the growth, reproduction, and survival of shrimp. pH is one of the key parameters that affect water quality, with deviations from the optimal range (5.5–8.5) leading to stress, weakened immune responses, and potential infections in shrimp. This research presents the development of an automated pH monitoring and forecasting system aimed at improving water quality management in shrimp farms. The system uses a moving average algorithm to predict future pH levels based on real-time data collected by a pH sensor. The predicted and real-time values are transmitted to a cloud database, and farmers receive alerts via the Line application if pH levels deviate from the acceptable range. The systems performance was evaluated through six experiments, using different data collection intervals and durations. The most accurate forecasting results were achieved with 10-minute data collection intervals over a 2-hour period, yielding a mean squared error (MSE) of 0.003050 and a root mean square error (RMSE) of 0.038628. The system also demonstrated its ability to send real-time alerts to the farmer, ensuring prompt corrective action in the event of critical pH values.

To address the challenge of parameter accuracy in discrete element method (DEM) simulations of buckwheat seeds within a seed metering device, this study characterized the physical properties of buckwheat seeds and subsequently calibrated the simulation model parameters. Initially, physical experiments were conducted to determine the triaxial dimensions, angle of repose, and static friction coefficients of buckwheat seeds against stainless steel surfaces. A three-dimensional model of the buckwheat seeds was then generated using Inventor and it was imported into EDEM to simulate the seed stacking process, with the angle of repose quantified via image processing techniques. Employing a Plackett-Burman design, initial parameters were screened, identifying the static friction coefficient between buckwheat seeds, the rolling friction coefficient between buckwheat seeds, and the static friction coefficient between buckwheat seeds and stainless steel as significant factors influencing the angle of repose. The optimal range for these significant parameters was determined through a steepest ascent experiment, and a second-order regression model was developed using Box-Behnken experimental results to optimize the angle of repose and the identified parameters. The optimized parameter set comprised a static friction coefficient between buckwheat seeds and stainless steel of 0.448, a rolling friction coefficient between buckwheat seeds of 0.038, and a static friction coefficient between buckwheat seeds of 0.372. Validation through both simulations and physical experiments revealed a relative error of 1.08%, confirming the reliability of the calibrated parameters for simulating buckwheat seed sowing machinery.

Based on the requirements for hybrid garlic seed sowing, the overall structure of an air-suction garlic seeding unit was designed, and its working principle was described. The adsorption pressure required for the seed metering device was determined to be -5 kPa. One hopper was selected for the bud reversing device, with an angle of 24.5°, and the perimeter of the insertion device measured 600 mm. The optimal combination of parameters for sowing involved a negative pressure of -5 kPa and a forward speed of 1 km/h at the third level. Under these conditions, the qualified rate of individual sowing reached 84.26%, while the missed sowing rate was 7.12%, meeting the agronomic requirements for garlic seed sowing. Field experiments validated the theoretical analysis and bench tests, providing a solid foundation for the future production and promotion of garlic sowing units.

Addressing the issues of water resource wastage, low fertilizer utilization efficiency, and uneven water-fertilizer mixing in current irrigation and fertilization practices, a new type of coaxial counter-rotating agitator was designed. Based on computer numerical simulation technology, this paper simulated and analysed the agitators modes and flow field distribution, establishing the law that governed the variation of velocity distribution within the agitators internal flow field with changes in rotational speed. An orthogonal experimental design was employed, utilizing stirring speed, stirring duration, and submerged depth as the experimental variables, and the outcomes were subsequently analysed utilizing Design-Expert software. The findings indicated that optimal fertilizer solubility was achieved when the stirring speed was 400 r/min, the stirring time was 5 minutes, and the stirring depth was 660 mm. This study provided a theoretical basis for the design and application of the coaxial counter-rotating agitator and aided in guiding parameter selection and optimization for practical applications.

Despite the abundance of agricultural straw as a renewable resource, its low utilization efficiency remains a significant challenge due to inadequate mechanical processing methods. To solve the problem, the ANSYS finite element simulation software was employed to simulate and analyze two screw configurations (parallel and staggered arrangement) and four compression zone designs. The optimal coupling breaking method was determined to be the parallel type with a 2-2-2 compression zone combination for the twin-screw system. Based on the simulation results, a twin-screw straw pulping machine was constructed. The test factors included screw speed, thread pitch, and straw moisture content, with straw breaking rate as the test index. Simulation test was conducted to optimize the structural parameters of the twin-screw straw pulping machine, followed by a verification test. The results indicated that at a screw speed of 120 r/min, a thread pitch of 203 mm, and a straw moisture content of 60%, the straw breaking rate was the highest at 79.9%. The verification test results were consistent with the simulation results, with less than 5% deviation, confirming the reliability of the simulation model and optimization outcomes. This paper provides a basis for the design and optimization of twin-screw pulping machines and offers theoretical support for the high-value utilization of agricultural waste through enhanced mechanical processing technologies.

To investigate the vibration characteristics of the Thinker Agricultural Machinery 4LZ-2.0 crawler-type combine harvester under operational conditions, vibration tests were conducted on the chassis frame at three different engine speeds: 2700 r/min, 1500 r/min, and 800 r/min. A static analysis was initially performed to identify regions of significant stress and deformation on the chassis frame, which guided the placement of measurement points for collecting vibration acceleration data. Subsequent processing of the acceleration data in the frequency domain revealed the variation in vibration velocity at each measurement point on the chassis frame. It was found that as the engine speed increased, the excitation frequencies from the vibrating screen and cutting platform appeared as dominant resonance frequencies at nine out of twelve measurement points on the chassis frame. By referencing the international standard ISO 20816, it was determined that most of these measurement points fell into Class D, indicating a high level of vibration severity. To mitigate the vibration of the chassis frame, two measures were implemented: enhancing the structural rigidity of the frame and reducing the area of the center of mass trajectory of the vibrating screen. Following these modifications, re-testing showed that the average vibration intensity at the three maximum vibration measurement points of the chassis in the high engine speed condition successfully decreased by 34.57% compared to the original structure.

To explore the change law of contact stress distribution in the process of planting seedlings, the greenhouse transplanting equipment was developed to reduce labor intensity and improve the mechanization level of facility horticulture. This paper takes the direct insertion seedling planter as the research object, adopts the pressure distribution measurement system to study the soil stress distribution rule of different cross-section, digging depth and types of seedling planter. Both horizontal and longitudinal sections were obtained to show an increasing and then decreasing trend. The stress distribution is basically consistent at different digging depths, and the peak contact stress is roughly located at the center position. With the increasing of digging depths, the soil disturbance increases by the seedling planter and the hole tray seedlings, and the peak contact stress also shows a gradually increasing trend, leading to a gradual increase in the peak resistance of the seedling planter. The peak contact stress of the four opening seedling plant was higher than that of the three opening seedling plant. The main reason was that the soil disturbance of the four opening seedling plant increased, the peak contact stress distribution increased, and the peak resistance also increased. This study provides a basis for designing the greenhouse transplanters.

Aiming at the problems such as low seeding qualification rate of vegetable precision seeder, this paper designs a kind of air-absorbing precision seeder. The device is mainly composed of chassis part, seed discharge device, power unit, fan, gearbox and transmission device. ADAMS software is used to simulate the kinematics and dynamics of the air-absorbent seed discharger, and the simulation results show the movement and falling process of the seeds inside the seed discharger, and at the same time analyze the movement and force of the whole machine. By building a test bench, the seeding qualified rate under different factors is obtained to meet the requirements, which verifies the rationality of the machine design.

To address the issues of poor boom stability and inconsistent spray quality due to harsh field conditions and varying boom height relative to the target, a five-section planar truss boom capable of height adjustment, overall tilt adjustment, and unilateral tilt adjustment was designed. A contour levelling control system for the boom was developed based on the STM32F103 platform. The levelling system uses ultrasonic distance sensors to detect the distance between the booms ends and the target in real-time. The limit average filtering method is employed to eliminate sudden signal interference and process feedback data. The control system then drives electric actuators to adjust the booms posture in real-time. Field and spray tests were conducted using a small electric self-propelled sprayer as the test platform. The results indicate that the best levelling performance was achieved with overall height adjustment, overall tilt adjustment, and unilateral tilt adjustment thresholds set to 30mm, 30mm, and 40mm, respectively. The field spray tests showed minimal relative errors in droplet deposition coverage and density on both sides. The coefficient of variation CV of deposition along the boom direction was less than 15%, ensuring good spray distribution uniformity. These findings provide valuable references for the design and optimization of sprayer booms.

The mechanical properties of walnuts play an influential role in the process of walnut shell-cracking. To determine the optimal mechanical properties of walnuts, the effect of moisture content (MC) on the mechanical properties of walnuts was investigated. The results showed that Rupture force (F), Rupture displacement (D), and Rupture energy (E) of walnuts decreased proportionally with a reduction in the MC. To select an optimal pre-treatment for enhancing the mechanical properties of walnuts prior to shell-cracking, the effects of radio frequency (RF) and hot air (HA) heating treatment to change the mechanical properties of the walnuts were examined. The results indicated that the heating treatments of walnuts could lead to a brittle and easily breakable shell, with the F decreasing from 231.99±34.31 N to 174.73±24.89 N, the D decreasing from 1.68±0.18 mm to 1.36±0.13 mm, and the E decreasing from 207.31±44.29 mJ to 119.47±25.99 mJ. The mechanical properties of walnut shells are optimized to the best condition with the application of either a 2-minute RF treatment, a 3-minute RF treatment, or an 8-minute HA treatment. Notably, RF heating is significantly more time-efficient compared to the HA treatment. Quality evaluation indicated that there were no significant (p > 0.05) changes in color values, hardness, and brittleness between the heat-treated walnut kernels and the untreated walnut kernels. Overall, the results obtained from this study demonstrate that RF heating treatment is an effective method for optimizing the mechanical properties of walnuts prior to shell-cracking, and the results may provide guidance for the design and improvement of walnuts shell-cracking processes.

This study addresses the issue of grain crushing during the mechanical harvesting of soybeans, systematically analyzing the effect of moisture content on its mechanical crushing characteristics. Single-factor and multi-factor orthogonal experimental methods were employed to record the grain crushing process through compression tests combined with high-speed camera technology, allowing for a quantitative analysis of moisture content, loading speed, compression direction, and their interactions. Field experiments were conducted in five different planting areas, The results confirmed that when the moisture content was controlled within the range of 13%-17%, the grain crushing force could be maintained at a stable level between 137N and 182N, At the same time, the crushing rate was reduced to a minimum value of 2.15%±0.43%, generally remaining within a good range of 1.09% to 3.34%. The research findings provide a necessary theoretical basis for improving the design of key components of harvesting machinery.

The paper develops a regression model to predict the density of Fuel Rolls produced from Agricultural Crop Stem Biomass. The study evaluates the influence of Variable-volume Pressing Chamber pressure (P, MPa), biomass volume per linear meter (V, m³), and flax stem content (m, %) on Fuel Roll density (?). The goal is to optimize these parameters to ensure the desired density of Fuel Roll. A regression analysis combined with response surface methodology was employed. The optimal parameters for Fuel Rolls production include Variable-volume Pressing Chamber pressure of 0.45–0.55 MPa, biomass volumes of 0.65–0.75 m³/m and flax stem content 75%. These technological parameters enable the production of fuel rolls with required density of 110-130 kg/m³. The results show that increasing pressure in the Variable-volume Pressing Chamber enhances Fuel Roll density, while larger biomass volumes lead to lower densities. Additionally, higher flax stem content improves cohesion and compaction, resulting in higher densities. These findings emphasize the importance of fine-tuning technological parameters to optimize Fuel Roll production. Utilizing agricultural crop stems for biofuel production offers significant environmental benefits, including reduced agricultural waste and lower combustion emissions.

The cultivated land in hilly and mountainous areas of China accounts for a high proportion. However, the complex terrain makes it extremely difficult for traditional agricultural machinery to operate. There is a high risk of rollover, and the operation effect is not satisfactory. Achieving agricultural mechanization in these areas faces huge challenges. This study is dedicated to designing a four-wheel adaptive chassis suitable for hilly and mountainous areas to solve the stability problem of agricultural machinery during operation. The research adopts the leveling strategy of tracking the lowest fixed point plane in the four-point leveling method. By constructing the chassis coordinate system and analyzing the coordinate transformation matrix, the motion relationships of each support point are determined, and precise leveling is achieved based on this. In the system design, the hydraulic system is crucial. According to the preset vehicle parameters, various parameters of the hydraulic cylinder are accurately calculated, and a suitable gear pump is selected to ensure stable operation under different working conditions. The control system calculates the height errors of each point based on the body tilt angle data collected by the biaxial sensor, and then controls the action of the hydraulic valve to achieve automatic leveling of the chassis. The MATLAB/Simulink platform is used to simulate different tilt angle conditions, verifying the effectiveness of the control system. The experimental results show that the chassis can achieve rapid leveling within the range of -12° to 12° in the transverse direction and -8° to 8° in the longitudinal direction, and the leveling time is within two seconds. The leveling process is stable, without shaking and insufficient stroke problems. This indicates that the leveling strategy and system design of the chassis are reasonable and effective, which can significantly improve the stability and safety of agricultural machinery during operation in hilly and mountainous areas, providing important technical support for promoting agricultural mechanization in hilly and mountainous areas.

To ensure the stability of seeding depth for a corn planter unit under different soil moisture conditions, a control system was designed to adjust the seeding depth of the unit based on soil moisture content. Using a PLC controller as the control foundation, the system employs a hydraulic system as its actuator. Soil moisture content is detected by moisture sensors, and the PLC controller processes the analog signals from the sensors. The controller then adjusts the forward and reverse rotation and speed of the motor via a variable frequency drive, thereby controlling the vertical displacement of the depth-limiting wheel to regulate the seeding depth. At the same time, a pressure sensor provides feedback to the PLC controller to control the switching time of the solenoid directional valve, driving the hydraulic cylinder stroke. The profiling mechanism connected to the hydraulic system moves the depth-limiting wheel accordingly, achieving stability in the planters seeding depth. The control system is further optimized using the Mamdani fuzzy PID algorithm. Experimental results demonstrate that the designed seeding depth control system is stable and reliable, accurately adjusting the seeding depth according to different soil moisture levels. When the unit operates at speeds of 8 ~10 km/h, the stability of the seeding depth reaches 90%, significantly improving the stability of the units seeding depth.

In response to the current challenges in the harvesting process of hybrid rice, where the threshing gap cannot adequately adapt to the varying threshing requirements, leading to high grain breakage rates, loss rates, and frequent clogging of the drum, a new threshing device for hybrid rice was designed by adjusting the gap of the concave screen. The concave screen was divided into a low-loss threshing section and a low-damage threshing section, with the addition of a hydraulic system to enable the adjustment of the threshing gap. A dynamic analysis of the rice at the spiral feeding inlet and within the threshing drum was conducted, determining that the adjustable range of the threshing gap is between 10 and 20 mm. A prototype of the low-loss threshing device for hybrid rice was built, and bench tests were performed with feed rate, drum speed, and threshing gap as influencing factors, while cleaning rate and breakage rate were used as response indicators in an orthogonal experiment to determine the optimal parameter combination. The optimal threshing performance was achieved at a threshing gap of 14.19 mm, a drum speed of 460.12 r/min, and a feed rate of 2.13 kg/s, resulting in a cleaning rate of 97.67% and a breakage rate of 0.295%. Subsequent verification tests under the optimal combination showed a cleaning rate of 96.2% and a breakage rate of 0.36%, meeting the harvesting standards for hybrid rice. This research provides theoretical support for the development of mechanized harvesting equipment for hybrid rice.

Mold contamination of stored maize can cause significant economic losses, and it is crucial to effectively classify maize kernels without destroying their original structure. But existing studies have found it difficult to distinguish moldy maize. In this paper, a method for non-destructive detection of mold in maize using near-infrared spectral fingerprinting is proposed. The spectral raw data are initially acquired using a handheld near-infrared spectrometer. To enhance the signal quality, preprocessing is conducted, and a classification model is developed for full-band spectral data. In order to further optimize the model and enhance the classification accuracy, the feature wavelengths were extracted from the spectral data with effective preprocessing techniques in the full-band model. Finally, the maize kernel mold classification model is constructed. The classification accuracy of SG+SNV-SVM-ISFLA model can reach up to 97.22%, and the accuracy for the identification of asymptomatic moldy maize is 96.30%, which can realize the accurate grading of moldy accurate classification of maize and can well distinguish asymptomatic moldy maize. This work may significantly control the spread of molds in the food industry while improving storage economics and safety.

To address the high efficiency and precision seeding requirements for rice factory seedling cultivation, a whole tray air-suction dual-station high efficiency seedling cultivation and seeding assembly line was designed. Based on the whole-tray air-suction principle, the study focused on the design of a dual-suction seed tray dual-station precision hole-to-hole seeding device, a transverse seedling tray conveying device, and an automatic seed replenishment device. Nan Jing 46, Nan Jing 5055, and Koshihikari rice seeds were used for seeding performance experiments on the assembly line. The impact of workstation configuration and production efficiency on the empty hole rate, reseeding rate, damage rate, and seeding precision was analyzed. Experimental results showed that the dual-station precision seeding method significantly improved seeding efficiency. When the seeding efficiency reached 2000 trays per hour, the seeding rate of 1-3 seeds per hole was 90.1%, while the damage rate, empty hole rate and reseeding rate were 0.38%, 0.84%, 9.06% respectively. This study shows that combining the whole tray air-suction principle with transverse seedling tray conveying device, dual-station precision hole seeding device, and automatic seed addition device can significantly enhance the seeding efficiency while ensuring the seeding accuracy, achieving a seeding efficiency of over 2000 trays per hour. This study provides an important reference for further improving the seeding accuracy and efficiency of precision rice seedling cultivation and seeding assembly line.

This paper proposes a lightweight spore detection method for millet downy mildew based on an improved YOLOv8s, aiming to enhance the accuracy and efficiency of spore detection. First, the backbone network of the YOLOv8s model was modified by replacing the original backbone with EfficientViT. The substitution of the EfficientViT backbone enables global receptive field and multi-scale learning, which helps to reduce computational costs. While maintaining high performance, this modification significantly improves computational efficiency. Second, a Frequency-Adaptive Dilated Convolution (FADC) module was added to the neck of the model. By adaptively adjusting the receptive field of dilated convolution, the FADC module optimizes the detection of different frequency information. It improves the detection of small objects without adding extra computational burden. Finally, the detection head was optimized to better adapt to the task of detecting millet downy mildew spores, resulting in enhanced detection speed and accuracy. The improved algorithm, named EFP-YOLOv8s, maintains the same mAP50 as the original YOLOv8s model while reducing the number of parameters by 37.8% and computational cost by 58.5%. By balancing high performance with reduced computational resource demands, the model achieves lightweight design, making it more deployable and scalable in practical applications.

In order to clarify the working mechanism of forage crusher, the combined cutting and crushing forage crusher was taken as the research object, the mechanical analysis was carried out on the cutting and crushing process of alfalfa stalk, the dynamic model was established, and the relationship between the shear force and the relevant parameters of the cutting mechanism was defined. It was found that the cutting Angle, the installation Angle and the wedge Angle had important effects on the cutting effect and power consumption. The force of alfalfa stem in the grinding chamber is mainly related to the speed of the grinding shaft, the gap between the hammer and the rolling plate. With the output speed of motor, feeding amount, screen diameter and water content as test factors, productivity, silking rate, power consumption and average crushing length as evaluation indicators, the single factor test was carried out to determine the influence of each test factor on the evaluation indicators. The research results can provide reference for the optimization design of grass crusher.

For the accurate detection of obstacles in complex farmland environments, ResNet50 is adopted as the backbone feature extraction network, feature pyramid network (FPN) is utilized to enhance the multi-scale feature fusion capability, and the region of interest alignment (ROI Align) strategy is introduced to improve the candidate box localization precision. The experimental results show that the precision, recall, and mean accuracy (mAP) of the improved model are 91.6%, 89.7%, and 93.8%, respectively, which are improved by 2.7, 2.3, and 3.1 percentage points compared with the original base network, and provide a technical reference for navigation and obstacle avoidance of unmanned agricultural machinery.

TTo address the issue of increased fuel consumption and reduced efficiency caused by excessive slip of the drive wheels during tractor ploughing operations, this paper considered the time-varying, uncertain, and highly nonlinear characteristics of the tractor-operating unit. A nonlinear dynamic model was constructed and a nonlinear slip control method for the drive wheels was designed using sliding mode variable structure control (SMVSC). The method was validated and tested on both the MATLAB/Simulink platform and a hardware-in-the-loop (HIL) simulation platform based on dSPACE. The HILS results indicated that, compared to the fuzzy PID algorithm, under varying soil specific resistance pulses, the mean absolute deviation of slip rate was reduced by 0.013, and the response time decreased by approximately 1.3 seconds with the SMVSC method. In case of pulse variation in slip rate, the SMVSC method reduced the tracking response time by approximately 0.8 seconds and the average control overshoot by about 0.03. Under both experimental conditions, the SMVSC method demonstrated superior control performance, ensuring more stable tractor operation. These findings provide valuable insights for drive slip control in tractor ploughing operations.

Wheat grains are a common type of cereal variety, and due to their large quantity and high demand, traditional manual quality inspection requires a significant amount of labor with potentially inadequate results. To address this issue, this study focuses on intact, damaged, moldy, and shriveled wheat grains, and establishes a YOLO-wheat automatic wheat grain appearance quality detection model. First, a large number of wheat grain sample images were collected, preprocessed, and annotated. Next, YOLOv5n, YOLOv8n, and YOLOv10n wheat grain object detection models were established, and the optimal model YOLOv8n was selected as the base model for automatic wheat grain appearance quality detection. To further improve wheat grain detection performance, the Dilation-wise Residual (DWR) module was integrated into the YOLOv8n network structure to enhance feature extraction from the expandable receptive field in the higher layers of the network. Additionally, the TripletAttention attention mechanism was introduced, and this improved network was named YOLO-wheat. Experimental results showed that YOLO-wheat achieved an mAP value of 91.3% in wheat grain appearance quality detection, representing a 4.3% improvement compared to the previous version. This study provides technical support for automatic wheat quality detection.

UI design and user interaction optimization of smart agricultural management cloud platforms are key research directions for enhancing the overall value of the system. However, there is still room for improvement in terms of functionality and visibility of the platform interface. This study constructs a UI design evaluation model by combining the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), systematically evaluating and optimizing three UI design schemes of the platform. First, AHP was used to allocate weights to design factors, and then FCE was applied for comprehensive evaluation of each scheme, ultimately selecting the optimal one. Based on user heatmap data, the visual design of high-click areas was further optimized, improving the platforms score from 80.524 to 86.927. The study demonstrates that the combined AHP and FCE method has significant effects on UI design evaluation and optimization, providing scientific evidence and practical guidance for enhancing user experience in other smart agricultural management cloud platforms.

In view of the difficulty and high cost of monitoring the invasion of small aggregations of Spartina alterniflora in coastal wetlands, this study proposes a SA-YOLO detection model. First, by adopting a lightweight cascade attention mechanism as the feature extraction part of the network, the models ability to extract features from Spartina alterniflora images is optimized. Secondly, the convolution layer with an improved adaptive attention mechanism is added to optimize feature extraction, dynamically adjust the weight of the feature map, and reduce the amount of calculation. Thirdly, the improved adaptive convolution network is used to optimize the original neck layer, improve the models ability to integrate Spartina alterniflora image features, and reduce the amount of calculation. Finally, a Spartina alterniflora recognition system is independently built. The system effectively implements the proposed method and realizes the detection and recording of Spartina alterniflora information. This study successfully verifies the effectiveness of the proposed method by conducting experiments on the actual collected Spartina alterniflora dataset. The test results show that the recall rate and accuracy of the proposed SA-YOLO Spartina alterniflora detection model are 94.5% and 92.4%, respectively, both reaching a high level. It can be seen that the model can complete the identification and detection tasks of Spartina alterniflora, providing a solution for the identification and information collection of Spartina alterniflora in coastal areas.

This study explores the use of deep neural networks for detecting and quantifying the cattle population in Mongolia using drone imagery, addressing the limitations of traditional methods that are labor-intensive and time-consuming. A custom dataset of aerial images featuring grazing cattle in Mongolia was developed, focusing on winter and spring seasons, to train and validate a model based on state-of-the-art object detection algorithms. Specifically, the You Only Look Once (YOLOv8) architecture was employed to detect cattle across diverse environmental conditions. Model performance was evaluated using widely accepted metrics, including precision, recall, F1 score, and the mean average precision (mAP). The findings demonstrate the effectiveness of the proposed approach, with the YOLOv8 model achieving a mAP of 97.3% at an IoU threshold of 0.5, highlighting its potential for efficient cattle detection and monitoring in Mongolias unique environmental contexts.

Path planning is crucial for agricultural machinery navigation. To address the issue of operational path planning in fields with obstacles, this paper proposes a method for obstacle avoidance path planning in farmland by combining an improved whale optimization algorithm with Dijkstras algorithm. The population initialization is conducted using Tent mapping and a nonlinear convergence factor a^* is introduced to reduce the oscillation and instability of the traditional whale optimization algorithm. By using the grid method to model the environment of the target field, the field is divided into multiple regular subplots. The improved whale optimization algorithm is employed to determine the optimal traversal order of these subplots. Subsequently, Dijkstras algorithm is applied to find the shortest path connecting the subplots, achieving global obstacle avoidance path planning for farmland. Taking a rectangular plot of land in Jiaolai Town, Jiaozhou City, Qingdao as the target area for this study, the experimental results indicate that this method achieves a coverage rate of 100% in the plot coverage path experiment. Additionally, the path redundancy rate is 4.87%, which represents a reduction of 1.63% compared to traditional algorithms. This research method is applicable to regular plots, but it still has limitations for irregular plots or those with curved boundaries.

The three high-valuable products generated by the pyrolysis process are the biochar, the syngas, and the pyrolysis oil. The proportions of these products are influenced by several factors such as temperature, heating rate, and feedstock type. This research aimed to develop an innovative and improved pyrolysis reactor design, that can enhance both the quality and yield of biochar produced in thermochemical processes. The main objective was to enable the rapid conversion of various organic wastes, such as agricultural byproducts, wood chips, and other plant-based materials, into high-quality biochar. The experimental pyrolyzer model was designed with precise temperature control to optimize biochar properties and an intelligent process control system, which minimize emissions and maximize energy efficiency. This equipment not only contributes to sustainable waste management but also supports soil improvement by enhancing soil fertility and carbon sequestration in agricultural practices. The experimental results showed a high efficiency in obtaining a quality biochar within a short production time. Additionally, the biochar exhibited a water retention potential of up to 150% relative to the pre-treated biomass mass, while the energy consumption was estimated to be up to 15% lower compared to conventional methods.

To address the challenges of high labor intensity and expensive manpower in morel harvesting, a specialized morel picking machine was designed. It is capable of performing the tasks of grasping, cutting, and collecting morels. A three-degree-of-freedom robotic arm was incorporated to ensure precise positioning of the picking claw. The kinematic model of this arm was established to determine the operational radius for effective picking. A three-finger picking claw was developed, with the synchronous opening and closing of the three fingers through a wire-pulling mechanism and a four-bar linkage. Buffer elastic material was installed on the picking fingers, and cutting blades were placed at the fingertips. Simulation analyses were conducted to study the movement of the robotic arm’s end, the forces acted on the picking arm, the hinge between the upper and lower arms, and the hinge at the lower arm’s end, as well as the velocity of the picking claw’s end. Single-factor experiments were employed to investigate the effects of the blade length of the picking finger on the picking success rate, the damage rate of mature morels, and the damage rate of immature morels. The results demonstrated that with a blade length set at 15mm, the picking success rate reached 96.2%, while the damage rates of mature and immature morels were 8.6% and 6.8%, respectively. These findings indicate that the machine can effectively meet the operational requirements for morel picking.

To address the issue of seed filling in mechanical seed meters for different varieties of adzuki beans and to provide a basis for designing the shape and size of seed metering plate holes, a statistical analysis of the physical parameters of seeds from 10 adzuki bean varieties with significant morphological differences was conducted. The three-axis dimensions (length, width, and thickness) and the equivalent diameter of the seeds were found to approximately follow a normal distribution. The distribution and dispersion of each physical parameter were analyzed. A linear correlation analysis was performed between the equivalent diameter and the hundred-seed weight, showing a consistent overall trend. By measuring the hundred-seed weight, the relative equivalent diameter was determined, which was then used to select the appropriate seed metering plate diameter. The linear correlation coefficient analysis of the three-axis dimensions of different varieties indicated that, when modeling adzuki bean seeds for simulation, either the width or thickness could be selected as the primary dimension based on specific conditions.

As an important facility structure in China, the solar greenhouse can provide a suitable environment for plant growth and development during winter, making it a viable structure for cultivating landscape flowers in cold regions. However, the impact of crop spacing in solar greenhouses on the thermal environment had received scant attention from researchers. Therefore, computational fluid dynamics (CFD) software was used to simulate the effect of crops on the thermal environment in solar greenhouses. The results of this study show that: the presence of crops in the greenhouse has a greater influence on the temperature in the solar greenhouse along the horizontal, vertical and longitudinal directions within the ranges of (3000, 8000 mm), (300 mm, at the film) and the distance from the inner surface of the east and west walls within the range of 10,000 mm; the presence of crops in the greenhouse has a greater influence on the internal temperature of the east wall, the west wall and the north wall; The influence of different planting densities of crops in the greenhouse on the internal temperatures of the solar greenhouse in the horizontal, vertical and longitudinal directions was within the ranges of (1000 mm, at the film), (600 mm, at the film), and the distance from the inner surface of the east and west walls was within the range of 6000 mm, respectively. This study can provide a theoretical basis for the cultivation and management of flowers in solar greenhouses in cold regions.

To identify the specific manifestations and influencing factors of the poor straw crushing and spreading effects in Northeast rice-growing areas, this paper focuses on the straw crushing and spreading device widely used on single axial flow harvesters. Experimental and theoretical analyses were conducted. The results indicate that the existing straw crushing and spreading devices, under different operating speeds and with various active blade types, exhibit crushing performance, spreading width, and spreading uniformity significantly below standard values. The primary issues are straw clumping, insufficient straw crushing, lateral bias in spreading, and inadequate spreading width. By establishing a theoretical model, the analysis reveals that the straw crushing effect is related to the characteristics of the straw itself, such as blade rotational speed, cutting angle, and support angle. In contrast, the spreading effect is associated with the motion direction of the straw leaving the threshing drum, straw ejection speed, and the arrangement of deflector plates. This paper clarifies the elements that need improvement in the operational effectiveness of the straw crushing and spreading devices attached to combine harvesters, providing a theoretical basis for subsequent enhancements.

To address the issues of low precision and high cost in pig house environmental monitoring, an Internet of Things (IoT)-based pig house environmental monitoring system is proposed in this study. The system utilizes ESP32 as the main control chip of each sensor node, which is constructed in a star topology structure, realizing data transmission by using wireless communication technology. By incorporating the median filtering function and the Kalman filtering algorithm to perform data fusion of the same type of environmental parameters, the accuracy of the data is ensured. Based on the environmental suitability requirements of pigs, an evaluation index system for pig house environmental suitability is established, and comprehensive weight calculations are carried out by combining the entropy weight method and the improved analytic hierarchy process. Simulation experimental results demonstrate that the error of the temperature data after Kalman filtering fusion is merely 0.12%, fulfilling the monitoring accuracy requirements. The system can precisely evaluate the environmental suitability of pig houses, and it is applicable for monitoring pig house environments.

The calibration of parameters for pelleted rice seeds (PR) is crucial for enhancing research on PR-related machinery and for executing high-speed precision hole sowing with unmanned aerial vehicles. This paper focuses on determining the fundamental physical and contact parameters of PR. A series of tests were conducted, including the Plackett-Burman test, the steepest ascent test, and the Box-Behnken test, using the stacking angle as the primary variable. These tests led to the identification of optimal combinations of simulation parameters. Specifically, the coefficients of rolling friction for the PR-PLA plate and PR-PR were measured at 0.137, while the coefficient of static friction for the PR-PLA plate was 0.336. This study provides a reference for calibrating simulation parameters of pelleted seeds and research on high-speed precision seeding.

Traditional corn threshing devices face issues of high unthreshed grain rate and high breakage rate under conditions of high feeding rates. To address this, a high-throughput double longitudinal axial flow corn threshing device was designed in this study. Based on a stress analysis of the interaction between threshing components and corn ears, an arc-shaped plate tooth structure was developed to progressively increase the squeezing force between the plate teeth and the ears. A combined threshing element, integrating arc-shaped plate teeth and round-headed nail teeth, was designed to improve threshing cleanliness and minimize grain breakage under high feed capacity conditions. The crucial parameters of the threshing cylinder were determined by theoretical analysis. Threshing bench experiments were conducted to investigate the effects of feed rate, threshing cylinder speed, and guide plate angle on the devices grain breakage rate and unthreshed grain rate. Based on the findings, the optimal parameter ranges were identified. An orthogonal test involving three factors at three levels each was conducted to determine the optimal working parameters of the device. The results indicated that the ideal conditions were a feed rate of 16 kg/s, a threshing cylinder speed of 400 r/min, and a guide plate angle of 26°. Under these parameters, the grain breakage rate was 5.02%, and the unthreshed grain rate was 0.171%. The operational performance met the actual harvesting requirements. This research could offer a reference for the design of large-feed-rate threshing devices and related harvesters.

This article presents a preliminary thermodynamic evaluation of a refrigeration system using phase change materials (PCM) for defrosting. The objective of this study is to highlight the potential of heat recovery during the operation of the refrigeration system and its subsequent use in the defrosting process. The system is analyzed energetically, considering both the cooling and defrosting cycles using PCM-RT 35 HC. Input data were experimentally measured on a vapor-compression refrigeration system installed in a freezing chamber located in the university campus. The analysis includes the following refrigerants: R32, R404a, R134a, R290, R600a, R600, R1234yf, and R1234ze.The results indicate that as the defrosting time increases, the refrigerant flow rate required for PCM-based defrosting decreases. Furthermore, it was observed that R600 requires the smallest refrigerant flow rate, while R404a requires the highest to defrost the same mass of ice. The analysis reveals that R32 is the most suitable refrigerant for PCM-based defrosting, followed by wet or dry refrigerants (R404a, R134a, R290) and, finally, isentropic refrigerants (R600, R600a, R1234yf, R1234ze). Additionally, it is noted that as the condensing temperature increases, the recoverable heat increases for R32, R404a, R134a, and R290, but decreases for isentropic refrigerants such as R600a, R600, R1234yf, and R1234ze. This analysis was conducted using a computational model implemented in the Engineering Equation Solver software.

In view of the problems of low efficiency, poor crushing quality and inapplicability to high water content forage, the cutter and crusher rotor of hammer type forage grinder was designed, the cutter and crusher process was analyzed theoretically, and the parameters of cutter and crusher rotor were determined. The modal analysis of the cutting and crushing rotor was carried out by ANSYS Workbench to verify the rationality of the rotor structure. The alfalfa with water content of 65% was taken as the processing object, and the quadratic orthogonal rotation combination test was carried out with the output speed of motor, sieve diameter and feeding amount as the test factors, and the productivity and silking rate as the evaluation indexes. Through the analysis of variance and target optimization of the test results by Design-Expert 12.0 software, the regression equation between the test factors and the evaluation index was obtained. With the goal of maximizing the productivity and silking rate at the same time, the output speed of the motor, the diameter of the sieve and the feeding amount were solved by multi-objective optimization, and the optimal parameter combination was determined as follows: The output speed of the motor is 443.77r/min, the diameter of the sieve is 14mm, and the feeding amount is 1.27kg/s. The verification test shows that the productivity is 5065.98kg/h, and the silk rates is 94.87%. The device has high crushing efficiency, good quality, and can crush high water content forage, which meets the design requirements of forage mill.

To improve the material conveying efficiency in the elbow lifting transportation process under negative pressure airflow and to solve pipeline material blockage issues, a chili cleaning device was used as the core model. CFD-DEM coupled simulation was employed to analyze the lifting process of non-spherical chili materials in the elbow pipe. Key parameter optimization was carried out through experimental design, determining the optimal parameter combination as a pipeline curvature of 2.03 and an air-to-feed ratio of 4.571. This combination achieved a conveying efficiency 4.406 times higher than the lowest efficiency case, and the uniformity of material transport under optimal parameters was verified through simulation. This study lays a solid foundation for the design of pipeline bends and the optimization of material conveying analysis.

Aiming to address the limited space and the inapplicability of large harvesting equipment in Chinese greenhouses, a small electric double-row leek harvester was designed. This machine is capable of cutting, clamping, transporting, and collecting leeks simultaneously. A reciprocating cutting device was employed to accommodate varying row spacings. To make the machine suitable for small greenhouse environments, the parameters of the torsion clamping conveyor belt were optimized to reduce the overall size. Through theoretical analysis of the clamping and conveying system, the cutting mechanism, and the parameters for coordinated clamping-cutting operations, the structural design and working parameters were determined. A prototype was developed, and field experiments were conducted. The results showed that when the machines forward speed was 0.3 m/s, the linear speed of the cutting knife was 0.66 m/s, and the conveyor belt speed was 0.42 m/s, the average damage rate was 4.87%, meeting the requirements for mechanized leek harvesting. This study provides a reference for the design of leek harvesters.

To address the issues of high admixture and loss rates during seed production and harvesting operations with combine harvesters, a low-loss, high-purity cleaning device suitable for wheat seed production and harvesting was designed. Considering the actual conditions of seed production and harvesting operations, the structure and dimensional parameters of key components were optimized. Through theoretical analysis, a motion model of wheat seeds on the cleaning sieve was established, identifying the main factors affecting the cleaning performance as fan speed, sieve amplitude, and vibration frequency. The CFD-DEM coupling method was used to simulate and analyze the sieving process under the influence of airflow within the cleaning chamber. A three-factor, three-level quadratic regression orthogonal combination simulation test was conducted to establish a response surface regression model for seed admixture rate and seed loss rate. Multi-objective comprehensive optimization of the factors indicated that the optimal operating parameters of the cleaning device are a fan speed of 1143 rps, an amplitude of 28 mm, and a vibration frequency of 9.4 Hz. Finally, field trial verification was conducted by setting the working parameters based on the coupled simulation test data. The operational results of the optimized cleaning device showed a seed admixture rate of 1.47% and a seed loss rate of 1.07%, with all results meeting the relevant standards. This study can provide valuable theoretical support for the development of wheat seed production and harvesting machines.

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand.

The efficiency and quality of tobacco leaf harvesting are crucial for the economic performance of the tobacco industry. To enhance harvesting efficiency, a non-destructive tobacco leaf harvesting robot based on machine vision and robotics technology was developed. Experimental evaluations of key components demonstrated that the biomimetic flexible gripper based on the fin ray effect has good stiffness when the clamping force is 2.5 N, ensuring stable subsequent harvesting and collection of tobacco leaves. The introduction of a 6+1-axis robotic arm significantly expands the working range compared to the original 6-axis design, effectively covering the height of the tobacco column. The robotic arms speed notably affects harvesting time (P < 0.001), with 1.2 m/s identified as optimal for balancing recognition efficiency and success rates. Additionally, exposure time plays a critical role in success rates (P < 0.001), achieving peaks of 90.00% in the morning and 83.33% in the afternoon at 40000 µs. These advancements enhance tobacco harvesting technology and provide valuable insights for intelligent crop harvesting.

This study focused on an in-depth analysis of the cam motion process of the seedling clamp within the automatic seedling-picking device for tomato pot seedlings. Key influencing factors, including spring stiffness coefficient, seedling needle holding angle, and seedling-picking frequency, were selected for investigation. Evaluation indicators included substrate loss rate, pot seedling drop rate, and seedling-picking success rate. A three-factor, three-level, second-order rotational orthogonal combination test was conducted. Using Design-Expert V13.0.5 software for data analysis, the theoretical optimal parameter combination was identified: a spring stiffness coefficient of 376.8 N/m, a seedling needle holding angle of 15.6°, and a seedling-picking frequency of 89 plants per minute. Under this parameter combination, the substrate loss rate was reduced to 3.94%, the pot seedling drop rate to 2.01%, and the seedling-picking success rate reached 94.05%. A verification test conducted on the seedling-picking device test bench showed that the experimental results closely aligned with the optimized theoretical values. These findings provide a valuable reference for the structural optimization and performance improvement of seedling-picking devices in fully automatic tomato transplanters and contribute significantly to the advancement of automation in tomato transplanting operations.

The detection of tomatoes for automatic picking is challenging due to the dense distribution of fruit and severe occlusions. To address this, a dataset is developed using tomato images captured in a greenhouse environment, and an enhanced model for tomato fruit maturity detection based on YOLOv8n is proposed, which incorporates the EMA attention mechanism and the C2f-Faster module for multi-scale feature fusion. These additions not only improve detection accuracy but also enhance detection speed, thereby boosting the models robustness and generalization ability. Experimental results demonstrate that the proposed ECF-YOLOv8n model achieves detection accuracies of 93.8%, 94.7%, 92.5% and 94.1% for immature, nearly mature, ripe tomatoes and mean average precision in a greenhouse setting, respectively. The models size is 4.7 MB, with GFLOPs of 6.5G. Compared to advanced models like RT-DETR, YOLOv5 and YOLOv7, the ECF-YOLOv8n model outperforms them in both detection accuracy and speed. This work provides valuable insights for the research, development and optimization of tomato picking robots.

In contemporary agricultural practices, the use of image and video acquisition technologies, such as drones and cameras, has become increasingly common for capturing and monitoring crop growth in agricultural fields. The reliance on visual data for analyzing farm management conditions and facilitating decision-making processes is gaining significant traction. However, in practical applications, image acquisition tools often face challenges in maintaining optimal distance and angle during data capture, which can negatively impact the detection accuracy of existing object detection methods. Semi-supervised learning plays a crucial role in improving object detection. In this study, a semi-supervised algorithm for wheat spike recognition was developed based on an optimized YOLOv8n model. The model incorporates SPDConv and PSA attention modules after the SPPF layer, effectively reducing computational and memory demands while enhancing model performance. The proposed model achieved an accuracy of 94.2%, outperforming YOLOv5s, Efficient Teacher, and the baseline YOLOv8n by 10.9%, 4.5%, and 6.1%, respectively—demonstrating its strong potential for practical agricultural applications.

During the warm season, when ambient temperatures exceed +28 °C, the tunnel ventilation system is predominantly used in poultry facilities. This system effectively removes excess heat from the environment. However, under conditions of high ambient temperatures and high humidity, specialized systems are required to cool the incoming air and create a controlled microclimate within the poultry house. In ventilation systems, various types of cooling methods are employed to reduce the temperature of incoming air during the summer. Most commonly, these involve water spray systems. The core objective of this study is to conduct theoretical research on regulating heat and mass transfer processes in poultry houses, considering both internal dynamics and interactions through external barriers. This study proposes an innovative approach to cooling incoming air in poultry house ventilation systems. The method utilizes water sourced from underground wells and heat exchangers-recovery units (recuperators) to efficiently cool the incoming air. As a result of the numerical modeling, the temperature distribution within the service zone of the poultry house was determined. When heat exchangers are used, the inlet air temperature in the facility is maintained at +20 °C. The temperature increase along the length of the facility is clearly observed in the provided diagrams. The outlet temperature of the cooled air is +27.89 °C, which is attributed to heat generated by the poultry and the warming of the poultry house walls by external air. Thus, the air temperature within this cooling system does not exceed permissible limits. Analyzing the numerical modeling results at a height of 0.7 m from the floor level, it was concluded that no more than 2% of the poultry would experience discomfort under the proposed cooling system. The average air velocity is 0.83 m·s?¹, and the air temperature is +23.64 °C.

The process of slicing is a vital component in the preliminary treatment of licorice roots.The physical properties and shear mechanical attributes of licorice root are of considerable importance for the development and optimization of machinery intended for slicing licorice root. In this research, four-year-old mature licorice roots were selected as the experimental specimens, and shear strength evaluations were conducted on these roots by means of a universal testing machine (Instron 3344 series). The experimental design utilizes a blend of univariate and multivariate orthogonal testing techniques. Within this design, moisture content, shear speed, and shear speed are identified as the independent variables, while the maximum cutting force is defined as the primary assessment criterion.The experimental results reveal that the primary and secondary factors influencing the shear strength of licorice root follow the order: moisture content > shear speed > shear angle. The optimal conditions for the slicing pretreatment process are identified as a moisture content of 50 ± 2%, a shear speed of 0°, and a shear speed of 90 mm·min-1.

Optimizing parameters is a crucial step in designing mechanical structures and a primary means of raising equipment efficiency. This paper proposes a multi-parameter optimization technique that combines an improved genetic algorithm(IGA) and ensemble machine learning(EML)to optimize a licorice harvesters work and structure parameters. The EML model is trained using a small sample dataset built on the coupled DEM-MBD (Multi-body Dynamics Coupled Discrete Element Method) simulation model. The impact of base learner diversity and quantity on the models prediction accuracy is investigated. Using EML and IGA, the parameters of a licorice harvester are optimized. It is also contrasted with conventional response surface model(RSM) parameter optimization techniques. The study results show that the EML with KNN +lightGBM + catBoost as the base learner and linear as the meta-learner has an R2 of 0.959, MAE of 0.048, and RMSE of 0.06. In comparison to the RSM, EML-IGA reduces resistance by 18.16% and specific power consumption by 21.33%; in comparison to the EML and Pre-improvement genetic algorithm(PIGA), it reduces resistance by 11.36% and specific power consumption by 11.19%. It provides a reference for intelligent parameter optimization methods.

Aiming at the problem that there are few studies on the turnover device in the current peanut laying operation equipment and lack of simulation parameters, this study measured the characteristic parameters of upright peanut plants and tested the feasibility of conveying the turnover device to flip the vine. By studying the mechanical properties of peanut plants, the suitable clamping height range of plants was determined. By studying the size, mass parameters and distribution of peanut plants, the possibility of plant flipping and falling was analyzed, and the main structure and working principle of the conveying flipping device were determined.Based on the RECURDYN-EDEM coupling, the joint simulation of the conveying and overturning device was carried out to complete the dynamic analysis of the plant conveying and overturning process, and the simulation parameters were further optimized. The test results show that the device can achieve 95.1 % plant turnover completion and improve the quality of field drying, which can meet the requirements of peanut laying and harvesting operation. It can provide a theoretical basis for the design of two-stage peanut harvesting device in the future.

Rapid and accurate detection of millet ears is essential for yield estimation and phenotypic studies. However, traditional detection methods primarily rely on manual observation, which are both subjective and labor-intensive. To address this issue, this study employed Unmanned Aerial Vehicle (UAV) for image data collection of millet ears and proposed the YOLOX-CBAM-EIoU model to facilitate real-time detection, focusing on challenges such as small millet ears size, dense distribution, and severe occlusion in the dataset. Firstly, the Mosaic data augmentation technique was employed to enhance the diversity of the dataset. Subsequently, the CBAM attention mechanism was incorporated between the Neck and Prediction layers of YOLOX, enabling the reallocation of channel weights to enhance the extraction of fine-grained features and deeper semantic information. Additionally, EIoU Loss was utilized as the loss function for bounding box regression to mitigate missed detections in dense scenes. The improved model achieved an average precision (AP) of 90.30%, a 6.44 percentage point increase over the original YOLOX model, significantly enhancing detection performance for densely distributed millet ears. The improved model also demonstrated a Precision of 91.01%, Recall of 89.45%, and F1-score of 90.22, highlighting strong robustness and generalization capabilities. These findings substantiate the efficacy of the YOLOX-CBAM-EIoU model in improving detection performance under dense distribution and occlusion conditions, providing valuable technical reference for further UAV-based analyses of millet ears phenotypes and yield predictions.

The hilly terrain where sugarcane harvesters travel, the harvest object of rough and tough stalks, and the perennial cultivation property are all different from those of other straw crops. In order to reduce the cutting damage of cane stalks caused by complex excitation in field conditions, a method for optimizing dynamic characteristics of vibration transmission system based on implicit parametric modeling of harvester frame was proposed. First, the impact damage effect of the cutter on the cut section of stalk was analyzed by high-speed images. Accordingly, the cutter amplitude RMS and impact velocity (VI) were proposed as parameters to characterize the damage inducibility. Subsequently, a 5-DOF dynamic model of the whole machine was established covering 21 dynamic parameters. With the measured excitation of road spectrum, cane cutting force and engine, the virtual prototype simulation showed that the frame stiffness was the most sensitive to vibration response. Through topology optimization and implicit parametric modeling of such load-bearing frame, a high-rigidity design was derived to improve the vibration transmission characteristics. Comparison of testing results before and after frame optimization illustrated that the bending stiffness and torsional stiffness were increased by 1.95% and 2.84% respectively, and the RMS of operating frequency-response functions with road and engine as path sources were decreased by 21.7% and 27.2% respectively. As a result, the output amplitude RMS and impact velocity VI were reduced by 35.9% and 5.9% respectively, implying corresponding improvements in the cutting quality of cane stalks. This study provided a reference for the development of harvester dynamic systems based on harvesting quality optimization.

This paper reviews the progress in innovative design and intelligent technology applications of threshing devices in combine harvesters for staple crops. To address the issues of poor adaptability and low intelligence in traditional threshing systems, researchers have significantly improved threshing performance by optimizing threshing components and drum structures. Meanwhile, machine vision and deep learning have achieved important breakthroughs in feed rate monitoring, breakage and impurity rate detection, and intelligent control. This review aims to provide a reference for research and applications in threshing system structural optimization and operational parameter control.

A methane gas detection system has been developed based on the infrared absorption method. The system can be deployed in dry rice field for real-time detection. It consists of self-developed circuits and essential optical parts. A distributed feedback laser has been chosen as the optical source of the system. Hollow-core photonic crystal fibre is also applied as a part of the gas cell. The major circuit boards include laser driver circuit, laser temperature control circuit, digital lock-in amplifier circuit and linear power circuit. The laser diode can be effectively controlled by using the above circuits. The laser driving current step is 1 mA and the temperature fluctuation is less than ± 0.02 ?. Based on the TDLAS technique, spectroscopy test shows that the proposed laser driving circuits has accurate control capability. The detection error is about 2.3% by performing the full-scale detection experiments. Further gas detection experiments using standard gas under 600 ppm also demonstrate the effectiveness and stability of the proposed system. By replacing the optical source and essential driving circuits of the system, the system can be applied to detect other trace gases.

To achieve fast and accurate detection of wheat impurities, this study proposes an improved YOLOv8-based algorithm that targets three typical impurity types: bran, straw, and spike. The original C2f module is replaced with the C2f_UIB structure from MobileNetV4 to reduce model complexity, and a High-level Screening Feature Pyramid Network (HS-FPN) is integrated to enhance multi-scale feature fusion. Additionally, a Generalized IoU loss function is adopted to improve detection robustness in dense impurity scenarios. The optimized model is deployed on an embedded Jetson Nano platform for real-time inference, coupled with an industrial camera and LED lighting system. To validate its practical effectiveness, an indoor experimental setup was constructed to simulate field conditions. A total of 30 wheat samples were tested, and results demonstrate high consistency between system detection and manual annotation, with minimal deviation across all impurity types. The proposed algorithm exhibits excellent accuracy, lightweight characteristics, and strong potential for deployment in intelligent agricultural equipment.

The high-frequency quenching technique can enhance the surface hardness of 65Mn rotary tilling blades, thereby improving their wear resistance. This approach addresses common issues found in conventional rotary tilling blades, such as severe wear failure, short service life, and reduced operational efficiency due to frequent replacements. The quenching position was determined through finite element simulation. Based on orthogonal rotation combination tests using ternary quadratic regression, quenching temperature, tempering temperature, and tempering time were identified as key test factors. Using blade hardness as the evaluation index, the optimal parameters were determined to be a quenching temperature of 852 °C, tempering temperature of 171 °C, and tempering time of 85 minutes. Under these conditions, the blade hardness reached 57.5 HRC, meeting the national standard. Blades treated with these optimal quenching parameters were tested under actual soil conditions over a total operating area of 67 hm2. The results showed that the average wear of the quenched blades was 11.9 g, and their wear resistance was 3.13 times higher than that of blades treated with conventional heat treatment. This represents a significant improvement in abrasion resistance and provides a solid experimental foundation for the reliability assessment of rotary tilling blades.

This research addresses the axisymmetric problem in the theory of granulation of porous bodies, with practical application in calculating the forces involved in the granulation of dispersed bulk materials such as chips, granules, and other agricultural and woodworking waste. For such materials, the shape of the particles (structural elements) is generally irregular and not geometrically well-defined. This characteristic served as the basis for adopting a continuum model of porous media. In this model, the material is treated as a continuous substance that fills all available layers of bulk space, allowing for the mechanical behavior of materials with internal pores or voids to be accurately described. The pores within the material are considerably smaller compared to other characteristic dimensions of the materials properties. In the continuum model, the mechanical characteristics of the material, such as stress, strain, and compaction, are described by mathematical equations that account for the materials physical properties and its behavior under loading. By reducing this model to a two-dimensional spatial form, a closed-form analytical solution was obtained using a general method for solving the differential equations of equilibrium along with the Huber–Mises energy condition for plasticity. The following assumptions were adopted as working hypotheses: radial and tangential stresses are equal, and the lateral pressure coefficient is equal to the proportional granulation density. Given that the problem is solved in a general form, the solution should be regarded as methodological, that is, it can be applied to any loading scheme exhibiting axial symmetry. Transcendental equations were derived to describe the deformation compaction process of a porous body. These equations account for both the ideal granulation process and the influence of contact friction forces. As a result of developing a solution method for these equations, dependencies were obtained for calculating the local characteristics of the stress state during granulation, as well as for integral parameters of the process, such as compaction and deformation work.

Precision agriculture requires accurate and efficient crop yield distribution information. However, both traditional field-based yield measurement methods and existing combine harvester yield monitoring systems face significant limitations. Traditional methods, such as direct weighing or sampling, are time-consuming and inefficient, and they only provide average yield values - insufficient for large-scale farming needs. Meanwhile, current monitoring systems often suffer from high measurement errors, low spatial resolution, and limited generalizability. For this reason, this study designs a new type of grain yield monitoring system, which corrects the photoelectric sensor data through the load cell data, realizes the calibration of the photoelectric sensor, avoids the influence of external factors, and improves the accuracy of measurement. Firstly, tests were carried out at three rotational speeds of 10 Hz, 20 Hz and 25 Hz of the motor inverter setting, respectively, to determine the positive proportionality coefficient between the photoelectric signal and the grain mass, and the overall error of the system was measured to be less than 6.44%. For the load cell, a model of the relationship between tilt angle and weighing accuracy was established and a compensation algorithm was proposed, the weighing error data in different directions and at different tilt angles were measured and analyzed, and a mathematical model between the corrected angle and the weighing error was established. Through the tilting experiment, the feasibility of the modified angle compensation model is verified, and the overall error after compensation is less than 0.25%, and the systematic error of measurement and production after the intervention of the feedback system is less than 0.74%. The experimental results demonstrate that the system significantly enhances the accuracy and stability of yield measurement. It holds substantial potential for widespread application, provides strong support for the advancement of precision agriculture, and is expected to drive agricultural production toward greater efficiency and sustainability.

To enhance real-time detection of corn breakage rate under dim conditions, this study designed a dual-light (top/backlight) sampling system. By comparing four datasets (top-scattered, top-clustered, backlight-scattered, backlight-clustered), the algorithm optimized with backlight-scattered data achieved optimal accuracy (79.6%). A lightweight YOLOv8n_gcd model was proposed, integrating Ghost convolution in the backbone to reduce redundancy, attention mechanisms for feature enhancement, and depthwise separable convolutions in the neck. The optimized model reduced FLOPs by 24% and increased FPS by 165%, offering an efficient, low-cost solution for agricultural quality inspection with theoretical and practical value

To address challenges in developing the rice-wheat harvester cutting platform controller—such as sensitivity to working conditions, long development cycles, and cumbersome performance testing—a semi-physical simulation platform is designed. Based on the functional requirements of the cutting platform, Simulink is used to build a mathematical model of the controller and its I/O hardware model. A hardware-in-the-loop simulation test platform is developed using the TC377ECU controller. By integrating the Whale Optimization PID algorithm, overshoot is reduced by 3.5%, and rise time improves by 0.303 s compared to conventional PID. Testing in both simulation and real environments shows a maximum absolute error of 10.58 mm for cutting height and a correlation coefficient of 0.9474. The rotational speed errors for the reel and auger have expectations of 0.106 rpm and 0.101 rpm, with standard deviations of 0.165 rpm and 0.172 rpm. This validates the controller’s feasibility, shortens development time, and lowers costs.

In order to solve the problems of inconvenient operation and easy loss of control levers in traditional tracked harvesters, this paper designs a hydraulic-driven chassis based on steering wheel control, and the structure and working principle of the chassis are described. The parameters such as track grounding length and drive wheel diameter are calculated; the design of the hydraulic drive system of the chassis is completed, and the driving control strategy is proposed. The test results show that the maximum speed of the chassis is 0.79 m/s, the minimum turning radius is 766 mm, and the failure rate tobacco rod clamping in field tests is only 4.37%. This design meets the needs of field operations, with high operability, stability, and ability, providing theoretical and practical basis for the development of tracked tobacco leaf harvesters.

To address the issues of incomplete threshing, separation loss, and grain breakage during the harvesting of super hybrid rice with conventional threshing and separating units in head-feed combine harvesters, a segmented-differential threshing and separating unit was developed. This unit mainly consists of a coaxial segmented-differential threshing drum and a rotary concave screen. Its structure and working principle are described in detail. A three-factor quadratic regression orthogonal rotation combination test was conducted, with the rotational speed of the segmented-differential drum, the linear velocity of the rotary concave screen, and the clamping chain speed as test factors. The grain loss rate, breakage rate, and impurity rate were taken as performance indicators. The test results were analyzed using Design-Expert 8.0.6 software to establish a mathematical model for the performance indicators and to determine the optimal combination of working parameters. Additionally, a comparative test was carried out between the segmented-differential unit and a conventional single-speed unit. The results showed that when the rotational speed of the low-speed/high-speed drum in the segmented-differential unit was 520/630 r·min-1, the linear velocity of the rotary concave screen was 1.20 m·s-1, and the clamping chain speed was 1.10 m·s-1, the grain loss rate, breakage rate, and impurity rate were 1.95%, 0.20%, and 0.56%, respectively.

To address the lengthy development cycle of grain air-screen cleaning devices, a semi-physical simulation platform for the grain cleaning regulation system was designed. The platform simulated actual operations, conducting open-loop and closed-loop tests with comparative analysis. Open-loop test results showed a speed error expectation of 0.8387 rpm and a fish-scale sieve opening error expectation of -0.0117 mm, while closed-loop tests yielded a speed error expectation of 0.679 rpm. Results confirmed the platforms ability to replicate actual conditions and validate the TC377 controllers effectiveness, enhancing development efficiency.

To address the challenges associated with high interrow weeding difficulty and seedling damage in corn fields, a weed removal machine based on crop spacing recognition was designed. It captures crop images at variable speed intervals, obtains corn seedling centroid coordinates via image stitching and skeleton extraction, calculates actual plant spacing through pixel-to-real coordinate transformation, and enables real-time control of the weeding device. Key components were analyzed for motion trajectories and critical parameters. Field tests revealed optimal performance at 0.45 m/s, namely, a 92.6% weed removal rate with 2.05% seedling damage, meeting operational requirements. This research provides technical and equipment support for interrow weeding.

In response to the current low level of mechanized harvesting for heading vegetables in China and the unsatisfactory harvesting results, this study focuses on the root-cutting operation during the harvesting process. A double-disk root-cutting device was designed to achieve low-power consumption and high-efficiency root cutting. Through LS-DYNA simulation experiments, the root-cutting process of the root-cutting device was simulated, and the cutting force and internal cutting energy of three different cutter combinations are compared. The simulation results show that the maximum cutting force of the double-serrated blade was the smallest, while the combination of a smooth blade and a serrated blade had the lowest internal cutting energy. Compared to smooth blades, serrated blades exhibited better clamping effects on the roots of head-forming vegetables. Considering both cutting force and internal cutting energy, the combination of a serrated blade and a smooth blade was found to be optimal. The quadratic rotary orthogonal combination test method was used to analyze the relationship between the main influencing factors of the root cutting device performance (rotational speed of the cutter, conveying speed, inclination angle of the cutter, and overlapping amount of the cutter) and the performance index (root cutting power) of the root cutting device. The bench test program was designed by applying regression analysis, response surface and multi-objective variable optimization methods. The bench test results show that the optimal parameter combination of the designed root cutting device were: cutter speed of 200 rpm, conveying speed of 0.3 m/s, cutter inclination angle of 10°, and cutter overlap of 20 mm. The predicted cutting power of the model was 51.19 W.

This study aimed to solve the inefficiency and high-energy-consumption problems of current agricultural stone pickers. It introduced a novel spiral cutter tooth design. Dynamic and kinematic analyses determined the key components parameters and performance-influencing factors. With EDEM software, discrete element simulations using a three-factor, five-level quadratic regression orthogonal design were carried out. Stone-picking efficiency and power consumption were the evaluation metrics. Regression analysis and significance tests clarified the impact of forward speed, drum speed, and tilt angle. Multi - objective optimization of the regression model found the optimal parameters: 0.18 m/s forward speed, 260 rpm drum speed, and 30° tilt angle. Field tests with this setup achieved a 93.71% stone-picking rate and 4.63 kW stable power, validating the designs effectiveness. These results offer a theoretical basis and reference for stone picker design and optimization

This study aims to accurately calibrate the interaction between cotton stalks and rubber belts in agricultural machinery using the Discrete Element Method (DEM). Through physical experiments, key parameters such as the collision recovery coefficient, static friction, and rolling friction were measured and validated through simulations in EDEM. Optimal values were identified as 0.446, 1.146, and 0.0194, respectively. Full-factorial analysis revealed significant effects on repose angle. Repeated trials confirmed a deviation of only 0.72% from experimental results, validating the calibration method. These findings provide a foundation for improving cotton stalk harvesting and transportation efficiency.

Basal Stem Rot (BSR) disease attacks in oil palm plantations are still the most significant cause of losses in oil palm plantations. The leading cause of BSR disease in oil palm plants is the Ganoderma Boninense fungus. The spread of BSR in an oil palm area can be massive due to transmission through root contact, airborne, and sporophores spread on the soil and in dead plant debris. The application of advanced technologies to mitigate and prevent the spread of BSR disease can be carried out considering that the nature of the spread and characteristics of this disease infection are well known. Advanced technologies such as the Internet of Things (IoT) are suitable for real-time monitoring of large areas. The key to successfully detecting BSR disease in oil palm plants is the selection of sensor technologies for monitoring and machine learning models used for segmenting and classifying infected plant characteristics. This paper comprehensively summarizes the spread of BSR disease and then describes various technologies and machine learning models for monitoring and preventing BSR disease in oil palm plantations. Hopefully, this paper can complement and provide a basis for developing technology to prevent the spread of BSR disease.

To address the challenges of high moisture content and the difficulty of peeling corn cobs in certain regions during harvest, a novel peeling device was developed. The device uses directional friction to peel the cob after introducing scratches on the bracts. Mechanical and kinematic analyses were conducted to study the peeling process, along with the design of the scratching mechanism. High-speed camera technology was employed to observe the peeling process, confirming that the peeling rollers effectively gripped the bracts at the scratched points. A three-factor, three-level response surface optimization experiment was carried out, using peeling roller speed, pressing wheel speed, and the distance between the pressing wheel and the peeling roller as independent variables, with bract peeling rate and grain shedding rate as the response indicators. The results showed that at a peeling roller speed of 353.2 r·min?¹, a pressing wheel speed of 81.42 r·min?¹, and a distance of 37.16 mm between the pressing wheel and the peeling roller, the bract peeling rate reached 95.67% with a grain shedding rate of 1.45%. Validation tests under these conditions yielded a bract peeling rate of 93.33% and a grain shedding rate of 1.56%, meeting the operational requirements for efficient corn peeling.

This study aims to construct digital fruit trees with high-precision geolocation and high-quality canopy phenotypic details, supporting the development of digital fruit tree technology and the establishment of smart orchards. The Neural Radiance Fields (NeRF) theory was integrated with georeferencing technology. Firstly, multiple ground control points were placed around the tree, and their WGS-84 coordinates were recorded using an RTK surveying instrument. Next, a drone captured multi-view images of the fruit tree, recording the camera poses during the image acquisition. The multi-view fruit tree images undergo ray casting, hierarchical sampling, and high-frequency position encoding before being input into a Multilayer Perceptron (MLP). The MLP was then supervised through volume rendering to obtain a convergent radiance field that reflects the true form of the fruit tree, resulting in the generation of a fruit tree point cloud. Finally, by establishing correspondences between the points in the fruit tree point cloud and the ground control points in the real world, a rigid transformation matrix was computed to convert the point cloud from a local coordinate system to WGS-84 coordinates, yielding a geographically informed digital fruit tree. The experiments demonstrate that the constructed digital fruit tree exhibits excellent phenotypic details and accurately represents multi-scale characteristics. The accuracy of tree morphology indicators, such as tree height, crown length, and width, reached 99.12%, 99.34%, and 99.22%, respectively. Compared to point clouds generated by traditional Structure from Motion-Multi View Stereo (SFM-MVS) methods, the root mean square errors were reduced by 61.24%, 73.48%, and 62.32%, respectively. Additionally, the georeferencing accuracy achieved millimeter-level precision, with registration errors generally below 2 mm. The proposed method can construct digital fruit trees with high geolocation accuracy, detailed phenotypic information, and scale consistency, overcoming key barriers in the development of digital fruit tree technology. It can provide comprehensive data for various production operations in smart orchards.

One of the essentials of intelligent prosthetics design is to recognize the wearers movement intention, to provide the wearer with the corresponding control strategy and movement assistance. The 11 independent gait patterns and 5 transformed gait patterns are recognized by the self-designed human lower limb motion data measurement system. The human gait pattern is classified by the linear discriminant analysis (LDA) classifier, and the recognition accuracy is evaluated by K-fold Cross Validation(K-CV). The average recognition accuracy of independent gait patterns is 90.91%. In the independent gait pattern, the lowest recognition accuracy of DS1 gait phase is 90.53%, and the highest recognition accuracy of SS2 gait phase is 91.36%. The overall average recognition accuracy of the transformed gait pattern is 92.67%, the lowest recognition accuracy of DS1 gait phase is 91.93%, and the highest recognition accuracy of SS1 gait phase is 93.31%. The main reason affecting the recognition accuracy is that some gait patterns have similar motion characteristics. The method proposed in this study can accurately predict the wearer s locomotion mode and serves as a reference for gait pattern recognition, prediction, and control strategies in intelligent prosthetic devices.

In this paper, an improved intelligent sorting algorithm for YOLOv8 white tea fresh leaves is proposed to solve the problems of unclear tea grades and uneven product levels caused by mechanical picking. The algorithm introduces the Dynamic Snake Convolution module (DSConv) for feature enhancement and adds an attention mechanism module, the Multi-Head Self-Attention mechanism (MHSA). Experiments show that the YOLOv8-DsConv-MHSA algorithm has an average accuracy of 96.4% and an average detection rate of 126.6 FPS per second, which is the best algorithm for white tea fresh leaf sorting in the comprehensive comparison. After deploying the proposed YOLOv8-DsConv-MHSA algorithm onto the developed tea sorting machine and conducting experimental comparisons with existing tea sorting machines, it is evident that the screening rate has been enhanced by 10.7%, and the operational efficiency has increased by 20%.

Ensuring the reliability and efficiency of agricultural machinery is critical for modern farming operations. Traditional maintenance strategies, including corrective and preventive approaches, often lead to unexpected downtime or excessive servicing costs. This study explores the application of machine learning-based predictive maintenance for agricultural equipment, focusing on the hydraulic system of a Massey Ferguson 7700 S tractor. Real-time sensor data was collected, with hydraulic pressure selected as the primary diagnostic metric for detecting early signs of mechanical degradation. A predictive maintenance framework was developed using seven machine learning models: Isolation Forest, One-Class SVM, KMeans, DBSCAN, Autoencoders, Convolutional Neural Networks (CNNs), and XGBoost. These models were individually applied to identify pressure anomalies indicative of potential failures. To enhance detection accuracy, a "Council of the Wise" ensemble approach was introduced, where an anomaly was validated only if at least four of the seven models agreed on its presence. This consensus-based method reduced false positives and improved fault identification reliability. Results demonstrated that integrating multiple models effectively distinguished significant anomalies from noise, capturing both transient mechanical instabilities and gradual wear-related failures. The findings highlight the potential of machine learning-driven predictive maintenance in optimizing maintenance schedules, reducing unplanned downtime, and extending equipment lifespan. This study establishes a scalable, data-driven maintenance approach that enhances the operational resilience of agricultural machinery, ensuring greater efficiency and sustainability in farming operations.

This study addressed the practical problems of complex picking environments, difficult image recognition, and low picking efficiency in apple harvesting, combined with Chinas agricultural requirements and picking systems. An improved apple fruit recognition method based on attention mechanisms and YOLOv5 was proposed. A dataset was created by collecting 3,600 apple images under front-light, side-light, and backlight conditions at different coloring stages in natural environments. The SENet and CBAM attention mechanisms were used to enhance YOLOv5s feature extraction network, and the model was trained to improve detection accuracy. Experimental verification showed that the YOLOv5x model embedded with the CBAM module achieved the highest mean average precision (mAP) of 98.3%. The CBAM module outperformed the SENet module. Actual tests of the apple-picking robots vision system prototype showed that when the IOU threshold was set at 0.5 and 0.3, the average detection accuracy was over 85% in both cases. The results demonstrated that the improved YOLOv5 model exhibited robustness to light intensity variations. This approach provides a technical reference for developing apple picking robot vision systems.

To address the issues of high damage rate and low harvesting efficiency during the tobacco leaf picking process, this study analyzed the separation mechanics of tobacco stems and leaves. A low-damage bionic picking device was designed by imitating the manual method of harvesting tobacco leaves. Based on theoretical analysis of the device and its key components, the structure and parameters of the complete system were determined. The picking process was simulated using ADAMS software, focusing on the contact force between the rigid and flexible components at various picking rod speeds. This analysis yielded the optimal combination of structural and operational parameters. Field experiments were subsequently conducted, and a response surface mathematical model was established using Design-Expert software to evaluate the relationship between key factors and performance indicators. The optimal parameter combination was found to be: a picking rod speed of 0.8 m/s, a device inclination angle of 30°, a picking rod spacing of 90 mm, and a forward speed of 0.69 m/s. Under these conditions, the tobacco leaf damage rate was minimized, meeting the requirements for low-damage harvesting. Further experimental validation showed consistency with simulation results, confirming the models reliability and demonstrating the practical feasibility of the device for tobacco field operations. This provides a valuable reference for the development of low-damage tobacco leaf harvesting equipment.

Plowing is recognized as an essential agricultural task that cannot yet easily be substituted with alternative soil processing methods due to its significance. However, it is also one of the most fuel-intensive operations. The main objective of this paper is to determine the optimal operational parameters (working speed and working width) of a plowing aggregate composed of a 180 HP tractor and a 5-moldboard plow, which ensures the full utilization of the tractor engines power and the use of the aggregate at its maximum working capacity, respectively, achieving an optimal power balance.

Aiming to address the issues of low accuracy and slow response in tomato leaf disease recognition models, an enhanced lightweight model for tomato leaf disease recognition was proposed. The SE attention module in the MobileNetV3-Large model was substituted with a CA attention module, and dilated convolution was incorporated to improve the models recognition accuracy and response speed. The CA attention module enhances the perception and feature extraction capabilities of spatial coordinate information in images. Dilated convolution was introduced into the deep network architecture to expand the models receptive field. The model was trained using a transfer learning approach that partially froze specific convolutional layers. Experimental results on a dataset comprising 10 common tomato leaf disease images and healthy leaf images demonstrated that the unimproved model achieved a recognition accuracy of 90.11% and an F1 score of 89.98%. After replacing the SE attention module with the CA attention module, the models accuracy increased to 91.15%, with the F1 score rising to 91.08%. Furthermore, introducing the dilated convolution model improved the accuracy to 94.33% and the F1 score to 94.22%, while maintaining a parameter count of 2.79×106 and a validation set operation time of 11.76 seconds. Compared to other traditional lightweight models, this model exhibits significant advantages. The DC-CA-MobileNetV3 tomato leaf disease recognition model proposed in this study can accurately and efficiently identify tomato leaf diseases, featuring a small number of parameters and ease of deployment in embedded systems.

Searching for and identifying methods to reduce grain cleaning loss, grain impurity, and improve cleaning efficiency is crucial for processing threshed rapeseed outputs in uneven conditions. Leveling a single-degree-of-freedom cleaning sieve becomes particularly challenging under terrain undulations and limited cleaning space. Based on spiral theory and mechanical design principles, this study explores a multi-degree-of-freedom cleaning sieve capable of vertical movement along the horizontal plane and rotation around its center. This design allows the sieve to adjust its relative angle, enabling a more uniform distribution of threshed outputs. A CFD-DEM coupling method was used to simulate the movement of these outputs. The cleaning sieve inclined forward and to the right served as the experimental group, while a horizontal cleaning sieve functioned as the control group. Compared to the control, the experimental group showed a 14.6% reduction in grain loss and a 2.3% decrease in impurities. Furthermore, the centroid motion of the rapeseed was enhanced prior to sieving, facilitating more effective separation, while the movement speed of impurities outside the cleaning shoe increased, aiding impurity removal.

To improve the utilization rate of fertilizers by peanut seeds, this paper aims at the synchronous hole fertilization and directly-above seeding of peanuts and designs a pneumatic horizontal disc hole fertilization device. A theoretical analysis of the ditching process of the fertilization furrow opener was carried out, and the height of the hole fertilization device from the ground was determined to be 40 mm. Through the analysis of the hole fertilization and directly-above seeding operation process, the time difference between seed metering and fertilizer metering was determined. Combining with theoretical analysis, EDEM - FLUENT coupling simulation tests and field tests were carried out on the device. The field test results show that the error of the fertilization hole spacing is 2.3% - 4.5%, the error of the hole fertilization depth is 3.2% - 5.1%, the seed - fertilizer distance is 64.8 mm - 67.2 mm, and the fertilizer distribution length is 99.3 mm - 107.5 mm. The test results meet the requirements specified in the sowing and fertilization standards.

This study addresses the issue of corn grain damage, which limits the efficiency of mechanized corn harvesting. The characteristics and distribution of corn grain surface damage resistance (CDDRS-SCK) were investigated. The "vulnerable" surface of the corn grain was selected for damage testing. The results revealed significant variations in damage resistance across different surfaces and locations of the corn grains. A regression model for damage resistance strength, based on surface position, was developed. Additionally, the impact of grain damage resistance on threshing damage was compared and analyzed for different threshing devices, in accordance with their working principles. This research provides theoretical insights and data support for the development of corn mechanized threshing technology and equipment.

To solve the problems of slow system response speed and poor uniformity of seeding and fertilizer application, this paper designs a control system for synchronized corn seeding and precision hole fertilization. A sliding mode control method with integral variable structure and disturbance observer composite (ISMDO-SMC) is proposed. Furthermore, a three-factor, five-level quadratic orthogonal rotation combination experiment was conducted to develop a mathematical model for parameter optimization using a multi-objective variable optimization method. Simulation results from four algorithms were compared, revealing a regulation time of 0.42 seconds, a recovery time to steady state of 0.13 seconds, and a descending rotational speed of 2.5 r/min, which demonstrates the strongest dynamic response and stability. Moreover, the optimal parameter combination was determined to be the forward speed of 2.8 km/h, the fertilizer discharge shaft speed of 42 r/min, and the fertilizer discharger opening of 5.5 mm, resulting in the fertilizer application error of 1.7 g and the seed-fertilizer spacing error of 2.2 mm. The results of this study provide a theoretical basis for achieving efficient and stable seeding and fertilization operations.

This review explores the multiple directions of industrial hemp (Cannabis sativa L.) utilization at a global level, highlighting the importance of this crop as a renewable, sustainable, and environmentally friendly resource. While hemp has traditionally been employed for the production of fibers, seeds, and medicinal products, recent studies have expanded its applications into innovative fields such as eco-friendly construction materials (e.g., hempcrete and composite materials), automotive and aerospace industries, cosmetics, biofuels, and others. The remarkable properties of hemp, including low density, superior mechanical strength, carbon sequestration capability, and moisture regulation, help reduce energy consumption and enhance the performance of construction materials, thereby providing competitive advantages in sustainability-oriented sectors. Moreover, hemp extracts and oils, rich in bioactive compounds (essential fatty acids, vitamins, antioxidants, and cannabinoids), have demonstrated therapeutic potential, reinforcing the role of hemp in the development of food, cosmetic, and pharmaceutical products. Additionally, hemp significantly contributes to phytoremediation by absorbing heavy metals and contaminants from the soil, and hemp fibers stand out for their durability and resistance, being valued in the textile industry for their ecological characteristics and superior performance. Industrial hemp stands out for its high versatility, and its valuable properties along with its favorable environmental impact, support its integration into a wide range of sectors, opening promising perspectives for a more sustainable and environmentally responsible future.

Aiming at the problem of differentiated cultivation strategies for different grape varieties, the AF-Swin Transformer model is proposed in this study. Firstly, Focal Loss is used to effectively tackle data imbalance in grape leaves. Secondly, the AdamW optimizer is selected to better control model complexity and improve generalization. The results show that the training accuracy of AF-Swin Transformer model is 7.87 percentage points higher than that of the original Swin Transformer model. Precision and recall improved by 4.4 and 4.3 percentage points, respectively. This study enables accurate automated variety monitoring within vineyard cultivation systems, assisting growers in implementing targeted cultivation strategies.

This study addresses the inefficiency, high crop damage, and poor adaptability of traditional cornfield weeding machinery. A High-Clearance Inter-Row Weeding Robot Chassis was developed and tested through simulations and experiments. With a refined suspension and floating wheels, it achieves 800 mm ground clearance for dual-row weeding. Simulations show maximum chassis stresses of 124.7 MPa and 134.88 MPa under sharp turns and braking. Stability assessments indicate theoretical climb angles of 26.5° longitudinally and 35° transversely, with experimental test results of 22° and 32°, respectively. The robot operates at speeds exceeding 0.8 m/s, overcomes obstacles of up to 370 mm, and traverses trenches narrower than 350 mm or wider than 600 mm. Results confirm its stability, obstacle-crossing ability, and precision, offering a viable solution for intelligent weeding in complex fields.

This paper provides an overview of the recent developments on small wind turbines in terms of their distinguishing characteristics, experimental research and structural and operational development of small vertical axis wind turbines. Emphasizing their decentralized generation capability, cost savings, and sustainability, the first part discusses the characteristics of small wind turbines. Then the review paper goes through a synthesis process of experimental research work on small wind turbines to evaluate their performance, technological advancements evolved in line with actual world problems encountered. The paper also describes what was achieved by way of small vertical-axis wind turbine design, material problems, and aerodynamic theories that control their operation. Finally, the study provides a review of experimental research studies that were conducted on the performance of small vertical axis wind turbines. The study also shows the functioning methods of small vertical axis wind turbines examined by means of experimental research, which investigate their efficiency under different environmental circumstances and where they may be optimized. Emphasizing the interdependence between theory and practice, this paper examines answers wind turbine researchers have already looked at. A small part of international research data seeking to improve the efficiency and design of small wind turbines is collated here

Root pruning is a widely adopted practice in modern orchard management, with the primary goal of stimulating regeneration and optimizing annual fruit production. There are numerous researches on the architecture of the root system of trees, the volume and depth of root development, as well as the importance of cutting them in fruit growing. Also, the specialized literature presents different types of equipment intended for cutting roots, but there are few studies that address aspects related to the resistance forces encountered during this work. This paper presents research conducted using a specialized root-cutting implement equipped with a chisel-type blade. The study outlines the technical characteristics and performance indicators of the equipment, which was tested on various soil types to evaluate its working parameters. As a result of the experiments, the following were determined: the root cutting resistance at different working depths and across three soil categories - sandy, clayey, and loamy - as well as the corresponding working speeds required to maintain efficient operation under maximum tractor power conditions. Using the experimentally determined values, the variation diagrams of these indices were plotted, such as: the stress distribution diagram and the speed variation depending on the cutting resistance, the maximum total cutting resistances were identified, at the maximum depth of 50 cm, at the most unfavorable blade inclination angle of 45°. The maximum values determined for these were 768.5 daN for sandy soil, 1185.5 daN for clay soil and 1602.5 daN for loamy soil. These results highlight the influence of soil type and working parameters on the mechanical stresses of the blade during root pruning work in fruit plantations.

To address the challenges of low automation, insufficient precision in seed-powder-liquid ratio control, and suboptimal coating quality in current domestic small-sized forage grass seed pelletization coating equipment, this study designed and developed a fully automated pelletization coating system. The system employs a microcontroller unit (MCU) as the core controller, integrates an ATFC101 serial touchscreen for human-machine interaction, and combines programming and electronic control technologies to achieve precise regulation of material and liquid supply, enabling fully automated and quantized coating operations. The hardware of the feeding system adopts a bidirectional thyristor (Triac) circuit, while the software implements a dual-mode control strategy to ensure high precision and stable material delivery. Experimental trials using Caragana seeds and pelletization powder demonstrated that the integrated electro-control system achieved full automation, with seed dispensing deviation controlled within 1.2 g and powder deviation within 0.32 g, significantly improving dosing accuracy. This research provides critical theoretical and technical insights for optimizing automated pelletization coating equipment for small-sized forage grass seeds.