Accurate identification of sheep is of significant importance for modern, intensive sheep farming. Traditionally, herders have used conventional methods to identify individual sheep, which are time-consuming, labor-intensive, and prone to considerable errors. In recent years, researchers have developed sheep face recognition models based on deep learning techniques to identify sheep using facial images. However, existing models suffer from insufficient theoretical research and limited recognition accuracy. To address these issues, this study develops a high-accuracy sheep face recognition model named ResNet-SFR. The core innovation of this model is the deepening of the feature extraction network of the original ResNet50, which enhances the models ability to capture various facial features in sheep images, as well as improving its generalization and stability. Additionally, the Convolutional Block Attention Module (CBAM) attention mechanism is embedded into the original model to further enhance the identification of key features, significantly increasing the accuracy of sheep face recognition. Transfer learning is employed to pre-train the sheep face recognition model, further boosting the accuracy of ResNet-SFR. Experimental results show that on a self-constructed sheep face image dataset, ResNet-SFR achieves a recognition accuracy of 96.6%, demonstrating its superior performance in sheep face recognition tasks. The proposed ResNet-SFR not only offers high recognition accuracy but also exhibits strong applicability, meeting the practical needs of farm identification and showcasing promising application prospects.

Currently, the compaction operations in rice seedling greenhouses are mostly performed manually or with simple machinery, resulting in significant discrepancies between the operational effects and requirements. Moreover, simple compaction machinery towed by small tractors cannot meet the dimensional requirements of rice seedling greenhouses. To address the issues of limited types of rice seedling compaction machinery and inability to meet the technical requirements for seedbed compaction quality, an eccentric compaction mechanism suitable for use in rice seedling greenhouses has been designed to reduce manual labor and improve soil firmness and evenness of seedbeds. Based on eccentric vibration theory, the motion equation of the leveling machine was established, and numerical simulations of the eccentric compaction mechanism were conducted using the RecurDyn-EDEM coupling method. Results indicate that the eccentric compaction mechanism effectively resolves the inability of traditional compaction devices to meet soil firmness requirements. Using a seedbed leveling machine independently developed by Heilongjiang Bayi Agricultural University, parameter optimization experiments were conducted using a second-order orthogonal rotational combination simulation method. The optimized parameter combination was: forward speed of 0.708 m/s, eccentric shaft rotation speed of 63.23 rad/s, and counterweight box mass of 50.99 kg, resulting in soil firmness of 3156.554 kPa and evenness of 0.02573 m. The experimental results were within 5% relative error of the simulation optimization results, indicating that the seedbed soil firmness and evenness meet agronomic requirements and have practical application value.

The study focuses on the self-propelled forage harvester and analyzes various parameters such as the working speed and torque of the cutting table, chopped roll, throwing fan, walking parts, and the output flow rate and pressure of the hydraulic pump of the feeding section. Field experiments show that the fan, walking, chopping roller, and grain crushing roller drive powers account for 7-8%, 7-10%, 24-28%, and 13-21% of the engine power, respectively. The driving power of the inner and outer sides of the cutting table, fan, and walking power account for a relatively stable proportion of the engine output power. The study collects and analyzes operation parameters simultaneously to provide a reference for evaluating the performance and optimizing the design of corn forage harvesters.

To optimize the structure of soybean precision seeder and improve the performance of sowing, a new rotary spoon precision seeder is designed, and the key component structure is designed using numerical calculation methods. Using a combination of bench experiments and field experiments for parameter optimization experiments, a multi factor quadratic orthogonal rotation combination design experiment is adopted. The experimental data is analyzed and processed using Design Expert 8.0.6 software to seek the optimal combination of parameters. The results show that when the working speed is 24.56-33.72 r/min and the forward speed is in the range of 1.31-2.21 m/s, the seeding qualification index is greater than 90% and the coefficient of variation is less than 10%, meeting the requirements of excellent seeding standards. This study uses a rotary spoon seeder to sow soybeans, providing a new idea and reference for the development of precision soybean seeders.

The development of industrial hemp production necessitates the creation of new technical means and methods, and the refinement of technological approaches for cultivating and harvesting this crop. In Ukraine, where the hemp industry is experiencing a period of active growth, there is a particular need for technical solutions that can ensure a high-quality harvesting process and remain affordable for relatively small farms. The article discusses the efficiency of the machine that was developed and manufactured for spring harvesting of hemp stalks. The parameters of the hemp stalks windrow were considered, taking into account the factors affecting its quality. In addition, there were selected the parameters to asses the quality of the resulting windrow. The research was carried out using probability theory and statistical methods. Through experimental studies, the functionality of the developed and manufactured machine was confirmed. It was established that the machine facilitates the formation of a uniform windrow from hemp stalks, with the skew cutting angle not exceeding 17°, which allows it to be collected with a baler, minimizing the risk of clogging.

Cassava is one of the worlds top three tuber crops, and its harvesting mechanization level is low. Digging-pulling cassava harvester is the main research direction of cassava harvesters. However, the soil-loosening components of the existing digging-pulling harvesters have poor loosening effect, high tuber damage rate, and large pulling force of cassava tubers after loosening. The two-sided loosening shovel that digs and loosens the soil on both sides of the tubers has low working resistance and is not easy to damage the tubers, but there are few reports on the impact of its operating performance. Therefore, this study focuses on three common types of two-sided soil-loosening shovels: the offset-wing shovel (OWS), L shovel (LS), and double-wing shovel (DWS). A two-factor, three-level orthogonal experiment is conducted, taking tillage depth (h) and shovel distance (b) as variables, then range analysis and factor impact analysis are carried out. Finally, through comprehensive comparison and optimization, a shovel type with best operational effects and its optimal working conditions are identified. The results show the LS demonstrated optimal performance when the breakage rate and pulling force were minimized. At the optimal combination of h of 0.25 m and b of 0.6 m, the LS has a breakage rate of 7.576% and a pulling force of 291.608 N. This study can provide basis for optimizing the design of loosening parts of digging-pulling cassava harvester.

The skin of Yu-Lu-Xiang pears is brittle and easily damaged during picking and sorting. In order to reduce the secondary damage caused by mechanical automatic sorting of Yu-Lu-Xiang pear after harvest, optimize the sorting process and improve the sorting accuracy. Based on the MobileV2Net model, a lightweight convolutional neural network model EC-MobileV2Net-Fast, which integrated transfer learning and attention mechanism, was proposed to identify skin damage defects of Yu-Lu-Xiang pears. According to the defects of Yu-Lu-Xiang pears with different damage degrees, a dataset containing four characteristics was created. The model accuracy rate, single defect identification accuracy rate, recall, specificity, parameter and so on were taken as evaluation indexes, and the interpretation ability of the model decision was analyzed by Grad-CAM thermal map. Preliminary evaluation results showed that the model produced the highest level of accuracy, underscoring the potential of deep learning algorithms to significantly enhance defect recognition and classification. It can improve sorting efficiency, reduce labor costs and strictly control after-sales quality.

To achieve precise seeding and fertilization operations for wheat and improve the intelligent level of wheat shallow buried drip irrigation and wide-width precision seeder, this article proposes a fusion speed measurement method by integrating ground encoder and satellite positioning module, and designs a control system of electric-drive wheat shallow-buried drip irrigation and wide-width precision seeder. The system includes STM32 main controller, DC servo motor, servo motor reducer, wheel encoder, vehicle navigation positioning module, vehicle control terminal, attitude sensor, voltage conversion module, etc. Based on the VisualTFT platform and equipped with a multi-functional touch screen, by designing the overall functions of terminal including obtaining speed from ground wheel encoder, obtaining speed from vehicle navigation positioning module, automatic switching of seeding mode speed source, motor speed control model, and remote transmission of positioning information, the system achieves the precise seeding and fertilization by adjusting the seeding/fertilization speed according to the forward speed of the tractor. The experimental study investigated the working performance of the control system of electric-drive wheat shallow-buried drip irrigation and wide-width precision seeder. Calibration test results showee that the speed accuracy of the ground wheel encoder was above 97.00%, The average seeding rate and fertilizer application rate were 22.4g/r and 157.4g/r, the standard deviations were 1.1 and 3.3, respectively. Field trial results showed that the seeding rate control error ranged from 3.0% to 5.3%, and the fertilization amount control error ranged from 3.0% to 6.0%. It meets the requirements for precise seeding and fertilization operations of shallow-buried drip irrigation and wide-width precision wheat seeder.

Soybean seed physical properties is important on filling performance during planting. To evaluate the effect of sphericity and variation coefficient of mean diameter on seeding performance in seed metering device, seven soybean genotypes [The Ken Dou 40, Ken Feng 17, Qing Ren Black Soybean, Black Soybean, Hei He 44, Bei Jiang 91 and Dong Nong 52] were tested to measure the length, width, thickness, and calculate these sphericities, mean diameter and variation coefficient of mean diameter, respectively. The model of soybean seed with equal mean diameter was developed and the discrete element method was used to set the different sphericity and variation coefficient of mean diameter. A simulation study was performed in the model of the cell wheel feed followed by mathematical modeling of the experimental data, and the data with response to the surface methodology was analyzed. Results showed that while the sphericity had a significant effect on both single- and empty -seed rates, the variation coefficient of mean diameter had a significant effect on the multi -seed rate. With the increase of sphericity, the single-seed rate increases but empty-seed rate decreases. With the increase of variation coefficient of mean diameter, the multi-seed rate increases. Based on our results, the Ken Feng17, Ken Dou 40, Black Soybean and Qing Ren Black soybeans were only selected for bench test. The relative error between the experimental results and the theoretical values of the regression analysis was small; however, the respond trend of factor index was same. Our study suggested that the effect of sphericity and variation coefficient of mean diameter on seeding performance can be used by using simulation experiment.

The grain depot scenario is critical for grain logistics and transportation, and it is also a key setting for the efficient operation of intelligent grain logistics platform vehicles. A large number of repetitive and specific building structures, along with low-textured walls, characterize the grain depot scene. Loopback detection is an essential module in visual SLAM, and an efficient system can eliminate accumulated errors. While traditional systems rely on manually designed features, which struggle to adapt to the unique grain depot environment, this paper proposes a deep learning-based loopback detection system for grain transfer trucks. Leveraging a custom dataset capturing both grain depot environments and loopback scenarios, the system employs convolutional neural networks for identifying building equipment and door numbers, edge extraction for robust feature matching, and image template matching for efficient loopback verification. Extensive testing on the grain depot loopback dataset demonstrates that the system significantly improves loopback detection accuracy and efficiency, paving the way for reliable autonomous navigation in grain depots.

To preserve the original quality of fresh pork and prolong its shelf life, this study aimed to seek a no ionic residue efficient storage method. The partial freezing with modified atmosphere packaging (PF-MAP, -1°C, 75%O2+20%CO2+5%N2) method was proposed and it was used to preserve fresh pork, and the storage effects with methods of refrigeration with MAP (R-MAP, 4°C), partial freezing with vacuum package (PF-VP, -1°C), refrigeration with vacuum package (R-VP, 4°C), partial freezing (PF, -1°C) and refrigeration (R, 4°C) were compared. The results indicated that after 8 days of storage, the total volatile basic nitrogen (TVB-N) content and total viable count (TVC) of pork under PF-MAP and R-MAP were much below the safety threshold limits, while the TVB-N content and TVC of the pork under PF-VP, R-VP, PF, R exceeded the safety limits. The drip and stewing losses of PF-MAP pork were lowest, which were 0.98% and 27.54%, respectively. The hardness and shear force of PF-MAP pork were 37.78 N and 38.38 N, respectively, which were significantly higher than other methods. The color of PF-MAP pork was bright, with a pH value of 6.08, an intense pork aroma, and perceived freshness. After 12 days of storage, the TNB-N content and TVC of PF-MAP pork remained significantly lower values, while the TVC of R-MAP pork approached the safety limit. After 20 days of storage, the TVB-N content and TVC of PF-MAP pork were 10.92 mg/100 g and 4.84 Ig CFU/g, respectively, significantly lower than the threshold limits. Its drip loss, stewing loss, hardness, shear force, pH, and color (L*, a*, b* values) were all satisfactory, resembling fresh pork in color and aroma. In conclusion, PF-MAP can better maintain the quality of fresh pork.

In this study, a stacked machine learning algorithm was constructed with tomato organ biomass as the research object, taking the geometric morphology data of tomato organs as the variables, utilizing eight classical machine learning algorithms as the base-model, and applying the linear regression algorithm as the stacked meta-model. This algorithm was then utilized to establish a prediction model for tomato biomass at the organ scale, and the biomass models of tomato plant leaves and fruits at the organ scale were constructed. The model has R2=0.86, MAE=0.49, and RMSE=0.81 in predicting leaves, and R2=0.94, MAE=0.33, and RMSE=0.57 in predicting fruits. The model has practical applications in predicting tomato yield and supply, providing market information, and supporting agricultural investment decisions. It also helps to optimize agricultural production and management, guide industrial development and planning, and improve the efficiency and competitiveness of the agricultural sector.

To address the requirements of direct corn kernel harvesting in the Huang-Huai-Hai region of China, this study aimed to rectify issues related to kernel damage and low threshing rates observed in the operation of existing corn kernel direct harvesting machines. Initially, a novel threshing device was designed, incorporating staggered rotary motions for the threshing cylinder and concave grate. Subsequently, the experimental factors such as the speed of threshing drum, the speed of concave grate and the threshing gap were considered on the test bench, and the indexes such as corn grain crushing rate and threshing rate were evaluated.Moreover, orthogonal testing indicated that optimal threshing performance was achieved with a threshing cylinder speed of 287 r/min, a concave grate speed of 106 r/min, and a threshing clearance of 49 mm, resulting in a kernel crushing rate of 4.12% and a threshing rate of 94.18%. These experimental findings confirm the feasibility of the Rotating Concave Screen Threshing Device, underscoring its ability to significantly enhance corn threshing rates while minimizing kernel fragmentation and damage.

To address the high rate of grain loss in the two-stage harvest of buckwheat, a pickup-tooth conveyor belt type buckwheat pickup device was developed to mitigate the grain loss in the buckwheat pickup process. The critical components of the pickup mechanism and conveyor mechanism were designed, and the essential parameters were determined. The operational efficiency of pickup-tooth conveyor belt type buckwheat pickup device was verified through field orthogonal test. The field test results indicate that the loss rate is most significantly affected by the speed of the pickup device, followed by the tilt angle of the pickup-tooth. The forward speed of the machine exerts minimal influence. After the multi-objective parameters of the regression equation model were optimized, the optimal parameters of the factors were obtained: the forward speed of the machine of 0.8 m/s, the pickup speed of 1.1 m/s, as well as the pickup-tooth tilt angle of 0°, Under the above condition, the loss rate of buckwheat grain reached 6.92%.

In order to meet the demand for mechanisation of sunflower segmented harvesting and tray insertion for drying, an intelligent tray insertion dryer was designed and developed. The machine integrates the functions of disc picking, disc flipping, bar clipping, lifting and inserting, and uses SolidWorks for accurate mechanism design and simulation, incorporating the YOLOv5 model for efficient recognition of flower discs and the LeGO-LOAM algorithm for accurate navigation and map building. In the experiment, 81 sunflower samples were collected to analyse data on disc diameter, plant height, rod diameter and stalk diameter, and to verify the recognition accuracy of the YOLOv5 model in different directions. The results showed that the precision of disc recognition was 95.54%, accuracy was 89.94%, recall was 95.54% and F1 value was 0.89. Using the LeGO-LOAM algorithm tested at different path lengths, the root-mean-square error of the navigational build trajectory was 0.15 m, with a standard deviation of 0.10 m. This technological integration improves the operational efficiency and supports the mechanisation of sunflower insertion tray drying.

In order to address the issue of low oxygen stress caused by crops in traditional facility agriculture, this paper designed and optimized a venturi aeration device to enhance the oxygen content in the rhizosphere of crops in facility orchards. With the assistance of Comsol software, visual analysis of the flow field was conducted using Computational Fluid Dynamics (CFD) technology, exploring the impact of throat deviation and air inlet positioning on the suction efficiency of the aerator. The results indicated that the eccentric venturi configuration significantly improved suction efficiency. Moreover, positioning the air inlet on the contracted side of the eccentric venturi throat increased suction efficiency by 12.7%. Analysis of flow field characteristics of various venturi aerator configurations identified key factors influencing suction capacity, including throat flow velocity, vortex morphology in the diffuser section, and distribution of turbulent energy within the aerator. To validate the accuracy of the numerical simulations, an oxygen absorption performance testing apparatus was constructed and simulation results were compared with experimental data. The analysis revealed an error range between the two results of 1.67% to 7.74%, confirming the reliability of the simulations. This study has provided a theoretical foundation and technical methodology for the structural design and optimization of venturi aerators.

The force analysis of the tea leaves on the U-shaped tank of the resistance wire heating groove-type tea de-enzyming and carding machine is conducted, and then the track analysis on SolidWorks based on the force analysis. The coupled steady state thermal analysis is carried out on the heating tank of the two kinds of machinery. The comparative test and content analysis of the three prototypes and gas trough machines are conducted, and the average value of the three tests is considered. The results are presented. The output of the prototype is 3.80 kg h-1, the strip rate is 88.38%, and the surface temperature of the pot groove reaches the requirement of finishing temperature.

The mixing characteristics and movement principles of brown rice particles in two different types of germinated brown rice machine tanks were investigated based on the discrete element method. Through numerical simulation, the effects of different mixing process parameters on the mixing uniformity and energy consumption of brown rice particles were quantitatively analyzed, and the most optimal mixing process parameters were determined: 40.16% filling degree and 20r/min rotational speed for blade-type germinated brown rice machine, and 37.9% filling degree and 20r/min rotational speed for drum-type germinated brown rice machine. In addition, a set of drum type sprouted brown rice machine mixing test platform was designed and built, and the accuracy of the discrete element simulation outcomes was confirmed via the physical tests, which showed that the simulation environment was consistent with the change rule of the movement state and mixing uniformity of brown rice particles under the physical test environment. This paper provides a significant theoretical basis and experimental support for the refinement of the design and the enhancement of the manufacturing process for the germinated brown rice machine.

This study mainly investigates the feasibility of monitoring and estimating the RCC (Relative Chlorophyll Content) and the LAI (Leaf Area Index) of sorghum by coupling integrated learning model with UAV multispectral image, clarifies the quantitative relationship between RCC and LAI of sorghum and the vegetation index based on different spatial resolutions, and constructs a Monitoring and prediction model for the RCC and the LAI of sorghum based on the UAV multispectral image and the vegetation index at different spatial resolutions. The model constructed based on integrated learning, and using the stacking approach had good prediction accuracies at three spatial resolutions, with the stacking model predicting R2=0.87, MAE=18.27, and RMSE=22.23 for the RCC at spatial resolution of 0.017 m; R2=0.86, MAE=17.38, and RMSE=23.21 for RCC at spatial resolution of 0.024 m; R2=0.80, MAE=18.62, and RMSE=24.12 for RCC at spatial resolution of 0.030 m; R2=0.93, MAE=0.34, and RMSE=0.37 for LAI at spatial resolution of 0.017 m; and R2=0.89, MAE=0.44, and RMSE=0.55 for LAI at spatial resolution of 0.024 m. The model established by combining the vegetation index and integrated learning can quickly and accurately monitor and predict RCC and LAI of sorghum, which provides a scientific methodology and theoretical basis for scientific monitoring and predicting RCC and LAI of sorghum in the field.

To gain a comprehensive understanding of the current status of unmanned aerial vehicle (UAV) technology in wheat crop growth monitoring and its development trajectory, this paper quantifies and visualizes the relevant literature published between 2015 and 2024 in the Web of Science database. By conducting a comprehensive analysis of high-frequency keywords, the article presents a summary of the prevailing research topics in this field. This can assist researchers in further familiarizing themselves with the relevant literature and providing a novel perspective on the utilization of UAV technology in wheat crop growth monitoring.

In response to the inefficiency and low accuracy issues of traditional detection algorithms in detecting the tray seeding process of green onions, this paper proposes a machine vision-based quality inspection system for green onion seeding. Considering the color characteristics of green onion seeds and the substrate soil, the original RGB images are converted into HSV images. The HSV color filtering algorithm is employed to separate green onion seeds from complex soil backgrounds. Image noise is removed using erosion-dilation operations and small-area methods. The projection method is utilized to determine the detection area of the tray and the position of the holes. Information about connected regions and their convex hulls is extracted, and eight feature parameters including perimeter, area, shape factor, perimeter ratio, area ratio, shape factor ratio, concave defect distance ratio, and error variance are used to establish a BP neural network for single and adherent seed classification. A concave point segmentation algorithm is used to separate adherent green onion seeds and count the number of green onion seeds in each hole to obtain seeding quality parameters of the seeder. Experimental results show that the average relative error of the system qualification rate is 2.24%, with maximum and minimum relative errors of 3.22% and 1.10%, respectively. The average absolute errors of the reseeding rate and void rate are 1.31% and 0.71%, respectively. The absolute error of the average number of particles is 0.025 particles, and the average processing time per image is 0.91 s. The research results provide reference data for precision seeding operations of green onion seeders.

The real-time images captured by agricultural machinery on-board monitoring equipment have complex backgrounds and different shooting angles. Especially for straw monitoring tasks, the utilization rate of images is relatively low. This paper presents a novel image classification and effective region segmentation method for straw returning in agriculture, leveraging semantic segmentation to enhance the efficiency of agricultural data analysis. The study addresses the challenges of manual straw cover analysis by proposing an automated approach to select images that meet monitoring standards. The methodology employs an encoder-decoder structure model, enriched with residual units, multi-scale convolution, and attention mechanisms. This model classifies images by calculating the pixel proportions of various scene categories and segments farmland areas to be inspected by incorporating distance information. The models design is tailored to handle the complex and variable natural environments typical of vehicular monitoring scenarios, where semantic object boundaries can be fuzzy. The experimental results demonstrate that the proposed method achieves an overall sample classification accuracy of 93% for straw returning image classification and an 85.37% accuracy in dividing areas to be inspected. The method outperforms several mainstream semantic segmentation models, providing a more accurate and efficient means of processing agricultural monitoring images. The integration of distance information proves particularly beneficial in distinguishing the farmland areas under inspection, leading to clearer segmentation and more reliable data for agricultural decision-making. In conclusion, the study contributes to the field of agricultural intelligence by offering a robust method for image analysis that can be applied to optimize the use of straw return monitoring data.

This research aims to develop an automatic precision seeding unit (APSU) for planting seeds in pots inside greenhouses. The study evaluated three seed suction nozzle diameters (0.5, 1.0, and 2.0 mm) and four types of seeds (Armenian cucumber, pepper, turnip, and okra). The key performance indicators involved the number of seeds captured per stroke, total time for seeding one pot, and deviation relative to the pots’ centers. The results showed that a nozzle diameter of 1.0 mm was the most effective for okra and Armenian cucumber seeds, resulting in average yields of 1.0 and 1.46 seed(s) per pot, respectively. The 0.5 mm nozzle showed optimal performance for pepper and turnip seeds, achieving 1.33 and 1.46 seeds per pot, respectively. The average time for seeding one pot, including all three stages, is approximately 35 seconds. These findings endorse the improvement of productivity and accuracy in automated greenhouse seeding, furthering precision agriculture as a state-of-the-art technological strategy.

Pod pepper(Capsicum annuum var. conoides), a common variety of chili pepper, poses a challenge for traditional object detection methods due to its complex morphological features and diverse types. This study focuses on the application of machine vision technology to address the issue of pod pepper object detection. Firstly, a large number of pod pepper sample images were collected, followed by data preprocessing and annotation. Subsequently, YOLOv3, YOLOv5, YOLOv6, and YOLOv8 pod pepper object detection models were established, with YOLOv8 yielding the best detection results with a mean Average Precision (mAP) value of 81.6%. Next, different attention mechanisms were incorporated into the YOLOv8 network structure, with experimental results indicating that the Triplet Attention mechanism performed the best in pod pepper object detection, achieving an mAP value of 82.5%, a 0.9% improvement over YOLOv8. To further optimize the effectiveness of the attention mechanisms, Triplet Attention was added at different positions within the YOLOv8 network. The experiment showed that the location of adding the attention mechanism significantly impacted the pod pepper detection results. When Triplet Attention was added at the 5th layer, the best detection performance was achieved, with an mAP value of 84.1%, a 2.5% improvement over the original YOLOv8. This research provides technical support for intelligent harvesting of pod pepper.

To accurately detect various kinds of locusts in real-time and make locust detection more universal, A locust data set that contains all kinds of locusts was created through the Internet crawler and public dataset IP102, and a locust target detection algorithm YOLOv7-MobileNetV3-CA.was proposed in this paper, Firstly, to reduce the size of model parameters, the backbone of YOLOv7 was replaced by MobileNetV3, Secondly, a CA (Coordinate Attention) attention mechanism was added to further improve the detection accuracy of locusts. after feature enhancement. The experiment showed that the precision of locusts was 95.96%, the recall rate was 92%, the AP was 95.74%, and the F1 was 0.92. Compared with YOLOv7, the model size was reduced by 27%, and the AP was improved by 4.48%. Compared with YOLOv4, YOLOv4 MobileNetV3, YOLOv5, and SSD algorithms, AP has improved by 51.16%, 26.81%, 11.9%, and 11.75%, respectively. Experiments have shown that this algorithm performs well in detecting locusts of different scales, scenes, and types, and can provide reference for real-time locust detection.

The existing equipment for cleaning large tanks has the problems of simple structure and single function. In order to solve this problem, a robot is designed to clean solid residues in tank, which integrates shoveling, crushing, sweeping and suction. And the crushing system of the robot is structurally designed and analyzed. Firstly, the crushing system is simulated and analyzed by using EDEM software. Then, the optimal operating parameters of the crushing device are determined by studying the effects of rotary knife pitch, rotary knife speed and rotary depth on the crushing rate. Finally, the crushing test is carried out on the cleaning robot, and the crushing rate is obtained as 83.6%, and the results show that the robot control system meets the design requirements. The study provides a certain reference for the research and development of the cleaning robot.

This paper focuses on the lack of spherical-like soybean simulation parameters when guiding the optimization and design of agricultural machinery and equipment through discrete element simulation. The spheroidal soybean variety SN29 was used as a study subject; the intrinsic properties and physico-mechanical properties of the spherical soybean were determined through actual measurements and the simulation of spherical-like soybean particles with Hertz-Mindlin (no-slip) as the contact model was established. The collision recovery, static, and rolling friction coefficients of the spherical-like soybean and acrylic sheet material were measured by the natural drop and inclined plane methods, combined with discrete element simulation and bench experiments. They were 0.474, 0.496, and 0.0361, respectively. The relative errors between the measured stacking angles and the simulated stacking angles were used as indicators, and the contact parameters between the particles were used as variables for the design of the steepest climb experiment. The collision recovery coefficient, static friction coefficient, and rolling friction coefficient between spherical-like soybean particles were determined to be 0.35, 0.30, and 0.074, respectively, by orthogonal rotational combination experiment and multi-objective optimization. The relative error between the simulated and measured stacking angles was only 1.09%, as verified by the experiment. This proves that the discrete element simulation parameters of the studied spherical-like soybean can reflect its real characteristics and be used as the parameter basis for discrete element simulation.

With the development of agricultural mechanization and the expansion of fruit tree cultivation, effective harvesting techniques have become crucial for boosting yield and reducing labor costs. This is particularly true in hilly areas with complex terrains where traditional, large-scale harvesting machinery struggles to be effective. This study designed and experimentally validated a portable walnut harvester based on electromagnetic excitation technology, aiming to enhance harvesting efficiency and reduce labor intensity in these areas. The harvester integrates electromagnetic excitation technology with the design of a lightweight, handheld electric cart, optimizing the machines adaptability and operational flexibility across various terrains. Through field testing, we evaluated the vibration effects of the machine under different branch diameters and impact locations, and optimized the impact parameters using Design-Expert software. Experimental results indicate that the machine can operate effectively at maximum pitch angles while delivering powerful impact forces to harvest walnuts efficiently. Furthermore, by optimizing the vibration frequency and impact location for branches of different diameters, we provided recommended parameters for using the equipment. This study not only demonstrates the potential application of electromagnetic excitation technology in agricultural machinery but also offers a viable mechanized solution for orchards in similarly complex terrains.

Abaqus software are used to analyze the seeding wheels stress distribution and displace-ment deformation during the working process , and the seeding wheels stress distri-bution and displacement cloud maps are obtained. Topological optimization analysis was con-ducted on the seeding wheel to obtain the optimized finite element model. Based on the finite ele-ment model, the original 3D model was modified to obtain the topologically optimized 3D model of the seeding wheel. The results show that the optimized seeding wheel reduces its mass by 48.4%, achieving a lightweight design of the seeding wheel structure.

In this study, the Agricultural Research Center of Melkassa examined the performance of a laboratory loop type bean thresher. As a function of different drum speeds (450, 550, and 650 rpm), concave apertures (25, 35, and 45 mm), feed rates (550, 650, and 750 kg/h), and moisture levels (5, 10, and 15%), the extent of grain deterioration, threshing efficiency, and rate of implantation were examined. Utilizing response surface techniques, the experimental design for optimization was developed. The response variables were significantly impacted by each independent variable. With a cylinder speed increase of 7.5 to 10.83 ms-1, the percentages of grain damaged, threshed, and germination decreased from 45.98 to 47.97%, 96.81 to 99.69%, and 85.75 to 55.98%, respectively. Despite an increase in seed germination, damaged grain and threshing efficiency decreased as the moisture content increased. Grain deterioration and threshing efficiency decreased, however seed sprouting improved in tandem with an increase in feed rate and convex aperture. The cylinder speed of 8.25 ms-1, the concave clearance of 37.4 mm, the feed rate of 672 kg/h, and the moisture content of 11.6% (db) were found to be the ideal parameters. In this case, the ideal ranges for seed sprouting, threshing efficiency, and grain impairment were found to be 3, 98.3, and 84.29%, respectively.

Most existing grain flow sensors are designed for paddle-type elevators, with limited focus on applications in auger elevators. This paper addresses the yield monitoring needs during rice harvesting operations, specifically targeting auger-based outlets through experimental research. An array-type differential grain flow sensor was developed and an indoor test bench was constructed to evaluate its performance. The study compares the effectiveness of time-domain and frequency-domain differential processing, alongside various filtering methods, for pre-processing the sensor’s raw signals. Additionally, a grain flow regression model was built using the Random Forest algorithm. Experimental results demonstrated that the monitoring errors during field tests ranged from -6.42% to 8.23%, indicating that the sensor met the requirements for rice yield monitoring. This sensor provides valuable data for feed rate detection, speed regulation, and adjustments to the threshing and cleaning systems in combine harvesters, offering significant practical implications for the promotion and development of precision agriculture.

In this paper, the designing and development of a novel mechanical flower thinning equipment, destined to increase the fruit production in orchards, is presented. The system integrates a ZED 3D camera with a dedicated controller for artificial intelligence running a custom trained YOLO9 algorithm, for real-time flower detection and counting. Based on the flower density data, the rotational speed of the thinning rotor is automatically adjusted to achieve the desired thinning ratio. Laboratory tests were conducted to evaluate the efficiency and adaptability of the YOLO9 algorithm to control the equipment in simulated flower density conditions. Results demonstrated potential improvements in thinning accuracy, contributing to optimized fruit development, and reduced manual labor. The proposed equipment offers an innovative approach to orchard management, ensuring sustainable practices by enhancing flower thinning precision while reducing labor costs.

This study calibrated discrete element parameters for coated wheat seeds through static and dynamic validation tests. Using physical experiments, key parameters were measured, and a discrete element model was created. Optimal parameters were identified via the Plackett-Burman test, steepest ascent experiment, and Box-Behnken design. Validation tests showed that the relative error between the simulated and actual angle of repose was 1.31%, and the relative error in seeding uniformity and seed displacement uniformity was less than 5%. These findings provide theoretical support for optimizing seed dispenser structures in precision wheat sowing.

New technologies are emerging everyday to improve the productivity of food production to meet rising demands. Microgreens have gained popularity nowadays and are known for being nutritious and easy to cultivate. Fogponics is one of the emerging technologies that atomizes the nutrient solutions into fine mist, improving the oxygenation and reduces water usage that lacks from traditional farming methods. The study developed an automated fogponics system for microgreens production using machine learning automation and internet of things monitoring systems. The models evaluation output proves that the system is reliable and capable of predicting an appropriate direction given the datasets acquired from temperature and humidity while the plants are thriving over time. The system has successfully reduced the temperature fluctuation ranging from 26°C-33°C to 27°C-30°C and stabilized humidity levels from 75%-100% to 90%-96%. As a result, the performance of the model effectively yielded the microgreens to flourish in its environmental parameters by incorporating machine learning automation and IoT-based monitoring systems. This study strengthened the importance of contributing a promising alternative for sustainable microgreens production. This prototype represents its significant advancement in agricultural strategies for indoor microgreens cultivation, offering a potential alternative for efficient and scalable production.

To address the limitations of seeding quality monitoring methods under the seeding operation mode of cotton hill-drop planter without grain, this paper designed a seeding quality monitoring system that can be eschews the traditional reliance on seed conductor. The system realizes real-time monitoring based on the differences in the cotton seeds’ absorption of light of different wavelengths, and achieves accurate evaluation of seeding quality by obtaining the seeding quality parameters with multiple types of sensors. Bench tests showed the lowest accuracy of seeding rate monitoring was 97% and the highest accuracy of missed seeding monitoring was 95% while the field tests showed that the highest drop in the accuracy of seeding rate monitoring was 2.03 percentage, but the lowest accuracy of missed seeding monitoring is still above 91%. The system does not require the transformation of the equipment carrier, but has a high degree of equipment adaptability, which can meet the requirements on monitoring of cotton seeding. The monitoring method is effective and feasible, with high accuracy and stability.

Aiming at the problem of traditional external grooved wheel type fertilizer discharger, which discharges fertilizer unevenly, an internal tangent circle external grooved wheel type granular fertilizer discharger is designed. Firstly, the 19-19-19 NPK compound fertilizer produced by Stanley Company in China is selected, and the pulsatility of the straight groove wheel, external tangent circle groove wheel, and internal tangent circle groove wheel is compared and analyzed using the Hertz-Mindlin (no-slip) model in the EDM2020 simulation, then the structural parameters are optimized for the better type of groove wheel. Finally, the Field Oriented Control (FOC) algorithm is used to control the brushless motor to further optimize the coefficient of uniform volatility. The four factors and three levels response surface simulation test was conducted with the groove tooth inclination angle, number of grooves, diameter of the inner tangent circle and angular velocity of groove wheel as test factors. Simulation results showed that when the groove tooth inclination angle was 38.341°, the number of grooves was 9.999, the diameter of the inner tangent circle was 13.154 mm, and the angular velocity of groove wheel was 5.998 rad/s, the coefficient of uniformity of fluctuation of the discharged fertilizer was 4.11%. Based on practical considerations, the groove tooth inclination angle was set to 38°, the number of grooves to 10, the diameter of the inner tangent circle to 13 mm, and the groove wheel angular velocity to 6 rad/s for a bench validation test. The bench test results showed that the optimized internal tangent circle groove wheel fertilizer applicator achieved a uniform fluctuation coefficient of 6.32%, while incorporating FOC algorithm motor control further reduced the coefficient to 4.62%, meeting the design requirements for the fertilizer applicator.

In order to solve the problem of low efficiency and potential damage in the separation of Gentiana roots from soil, a reciprocating adjustable striking-vibration combined device was designed, along with its performance testing. The ranges of working parameters for the vibration mechanism, striking mechanism, and adjustable reciprocating mechanism were determined through dynamic analysis of the mechanisms and materials. The effects of vibration frequency (X1), crank speed (X2), and screw feed speed (X3) on the threshing efficiency (Y1) and damage percentage (Y2) were studied using a ternary quadratic regression orthogonal combination experimental method, combined with response surface analysis to explore the interaction effects of these factors on the indicators. A regression model was established through variance analysis. The significant factors affecting Y1 were X2, X3, and X1 in that order, while the significant factors affecting Y2 were X1, X3, and X2. In the interaction of factors, X1X2 significantly affected both Y1 and Y2; X1X3 had extremely significant impact on both Y1 and Y2; and X2X3 had extremely significant impact on Y1. The optimal working parameters for the root-soil separation device of Gentian were determined to be vibration frequency of 6 Hz, crank speed of 204 r/min, and screw feed speed of 15 mm/s. With this combination of parameters, experimental tests yielded a threshing efficiency of 90.8% and a damage percentage of 5.9%. The relative errors compared with the theoretical optimization results were less than 5%. This study meets requirements for the root-soil separation of Gentiana.

To address the high damage rate and impurity content in corn ear picking, this study proposes the design of a gap-adjustable elastic low damage corn picking header. Theoretical analysis of the adaptive gap adjustment mechanism for the picking plate and the flexible picking mechanism will be conducted. Simulation experiments will be performed considering three factors: the stiffness coefficient of the compression spring, the rotational speed of the stalk-pulling roller, and the thickness of the flexible body. The test results indicate that the minimum collision force on the fruit cluster occurs when the stiffness coefficient of the compression spring is 36 N/mm, the rotational speed of the stalk-pulling roller is 700 r/min, and the thickness of the flexible body is 6 mm. When the header tilt angle was 25 degrees and the working speed was 3 km/h, using the stalk roller speed as the experimental variable, the collision force of the gap-adjustable elastic low-damage corn picking header was compared to that of the ordinary plate-type corn picking header. The results indicated that the collision force of the gap-adjustable elastic low-damage corn picking header was reduced by more than 25 % compared to the ordinary plate-type corn picking header.

The paper presents a general view of the new working surface of the harrow with helical working tools. With the help of SOLID WORKS software package, a computer model of this soil tillage tool with helical working tools for surface tillage has been created. The obtained new analytical dependence made it possible to determine the traction resistance of the harrow, equipped with a spiral working element which, in turn, served as the basis for constructing graphs, showing a change in the traction resistance depending on various values angle of attack of the harrow and the soil resistance.On the basis of a computer model of a helical tillage tool created in the SOLID WORKS program, it was found that when the angle of attack of the tool increases, the drag resistance P increases, as well, and its greatest increase is observed for the maximum diameter of the tool D. It was found that the thickness of the helix ? has a much smaller effect on the traction resistance compared to the parameter D.

The article presents the results of theoretical and experimental laboratory investigations of an improved screw conveyor for bulk agricultural materials that has been created, which, instead of a solid spiral winding, fixed to the drive shaft, uses a spiral winding, formed by separate curvilinear planes (blades), which are also separately fixed to the shaft, yet as a whole form a single spiral. An analytical dependence was obtained for determination of the magnitude of the torque on the drive shaft of this transport working element. The numerical values, obtained as a result of the laboratory experiments, made it possible, when conducting a regression analysis, to derive a new analytical expression in the form of a regression equation. The analysis of the regression equation shows that these factors, which have a significant impact upon the increase in the torque, are the factors: x1 (D) – the diameter of the fixed casing in which the screw is installed and x2 (?) – the filling factor of the conveyor with the transported bulk material. Increasing the value of the factor x3(n), i.e., the rotation speed of the vane working body leads to a decrease in the torque value.

In Indonesia, the use of four-wheel drive (4WD) tractors in paddy fields has been introduced, replacing two-wheel tractors. However, the condition of Indonesian paddy fields is commonly deep due to the absence of hard pan layer that make lower tractive performance of 4WD tractors. This research designed a special cage wheel for a 4WD tractor and compared it to a commonly used rubber wheel. The result showed that the cage wheel could increase the drawbar pull by 39.5% and drawbar power by 66.2%. It could also reduce wheel slippage by 21.9% and decrease sinkage by 15.9%.

In this study, the potato excavator with loosening shovel was designed in order to solve the problems of serious soil obstruction and high excavation resistance during the potato harvest. Using EDEM discrete element simulation software, potato excavation simulation experiments were carried out on soil broken effects and excavation resistance. Through the comparative analysis with loosening shovel and without, the results showed that the increase of soil broken effects was close to 52%, and the excavation resistance decreased by 16.57%. Through the two-factor and three-level field orthogonal experiment with excavation depth and loosening depth as influencing factors, the optimal operating parameters was determined for potato excavators: the excavation depth was 23 cm and the loosening depth was 20 cm. At this time, the excavation potato rate was 98.21%, and the rate of damaged potato was 1.31%. The traction resistance was 1826 N, which met the requirements of relevant industry standards. Through comparative analysis of simulation experiments and field experiment, it was found that when the excavation depth was 23 cm and the loosening depth was 20 cm, the error value of traction resistance was 11.3% between simulation experiments and field experiments. The discrete element simulation analysis can provide a preliminary reference for potato excavation design.

The use of UAV (Unmanned Aerial Vehicle) technology or drones in the monitoring of vineyards and orchards offers multiple benefits, improving the monitoring, management, and productivity of crops. The main goal of this study was to implement a cost-effective integrated UAV technology that includes the necessary hardware and software elements for analyzing the health and growth of agricultural crops in general, with a particular focus on vineyards and orchards. Based on the analysis, agronomists, experts in the field, or ordinary farmers can implement appropriate management measures, such as adjusting the irrigation process, applying fertilizers or phytosanitary treatments, and potentially using shading for the crops. Continuous crop monitoring allows for the evaluation of the effectiveness of the implemented measures and the adjustment of the crop management strategy. Another important objective was the use of high-precision sensors that can be easily attached to a commercial civil drone. The developed system should have a compact size and low energy consumption and even allow for IoT connectivity. To collect and record data from these sensors, a program written in Python is used, containing specific blocks for data acquisition from each sensor to facilitate the monitoring of environmental factors or energy consumption. Experimental tests conducted in the orchard space at the Faculty of Biotechnical Systems Engineering of the National University of Science and Technology Politehnica in Bucharest, Romania, led to the creation of maps showing the health status of the crops based on vegetation indices. The tests demonstrated that UAVs could rapidly cover large areas and collect detailed data without requiring extensive human resources or costly equipment. The results of the analysis of the drones flight performance underscore the considerable potential of UAV technologies in revolutionizing precision agriculture, particularly in orchards, providing farmers with powerful tools to improve the sustainability and productivity of their crops.

Mechanical seeding quality is a key factor affecting the yield of maize in stubble fields. To choose for high-performance seeders and determine the appropriate operating speed, comparative sowing tests were conducted using spoon-wheel seeders, lightweight and heavyweight finger-clip seeders, and air-suction seeders in six districts of Shandong Province. Three working speeds, low speed, medium speed and high speed, were set up to determine the qualified rate of plant spacing, the consistency of plants height and the yields under different working speeds. The results showed that the qualified plant spacing rate, plants height consistency, and yield of the spoon-wheel and lightweight finger-clip seeders were significantly affected by the operating speed, decreasing as the speed increased. At high speeds, the qualified plant spacing rates were less than 80%, and plants height consistency were less than 85%. Heavyweight finger-clip and air-suction seeders qualified plant spacing rates, plants height consistency, and yields were not significant affected by the operating speed, remaining stable across different speeds and significantly higher than those of the spoon-wheel and lightweight finger-clip seeders. The differences in qualified plant spacing rates and plants height consistency were more significant at higher speeds, along with obvious yields increase advantage. The yields increase ranged from 3.50% to 7.84% at low speeds, 4.32% to 9.31% at medium speeds, and 7.64% to 11.65% at high speeds. This study provides a reference for the selection of high-performance planters for maize in stubble fields and the determination of suitable operating speeds.

In response to technical challenges such as the difficulty in cutting long sweet potato vines in ridge furrows, low stem crushing rate, long stubble residues, and sweet high potato damage rate with the direct shredding and returning machine for sweet potato vines, this study conducted research on ridge furrow long vine removal and crushing technology, as well as optimization experiments on operating parameters. A ridge furrow boot-type vine picking knife structure, a "dynamic embedding static" shaft end anti-entanglement mechanism, and a ridge-like crushing chamber structure were designed. A high-efficiency sweet potato vine shredding and returning machine was developed, with ridge surface vine length qualification rate and ridge top stubble height as the main control indicators. The study conducted a 3-factor, 3-level orthogonal experiment in typical regions to analyze the factors of machine forward speed, knife roller speed, and ground clearance height, which significantly influence the performance. The results indicate that the optimal working parameters for the sweet potato vine cutting machine are a knife roller speed of 2000 r/min, a forward speed of 0.6 m s-1, and a ground clearance height of 10 mm. The vine crushing length qualification rate reached 94%, and the stubble height of 5.3 cm exceeded the various indicators, meeting the agricultural requirements for sweet potato harvesting.

The quality detection of eggs based on deep learning faced many problems, such as similar feature colors and low computational efficiency, which resulted in an increased probability of false detection or missed detection. To effectively solve these problems, this paper proposed an egg quality detection method based on YOLOv8n, which integrated the ContextGuideFusionModule, EfficientHead, and SIOU loss functions by improving the backbone network. The recognition rate from the field test was 88.4%, indicating that the algorithm could meet the real-time monitoring requirements, effectively identify the quality status of eggs, and provide support for intelligent poultry house management.

In orchard management, trenching operations are labor-intensive and have low efficiency, and mechanization is of crucial importance for the development of the fruit industry in China. In view of the many deficiencies of the existing chain-type and disc-type trenching machinery, this paper designs a self-propelled multifunctional orchard trenching machine. Its core component, the double-disc trenching disc, is an innovative design. Compared with the traditional single-disc trenching disc, it effectively reduces the trenching power consumption while ensuring the trenching depth and width, and significantly improves the operation efficiency. The trenching machine is composed of a frame, a gearbox, a double-disc trenching disc, a trenching knife, a soil retaining plate, etc. Field tests show that the operation speed reaches 1.2 kilometers per hour, the trenching depth is 31.2 cm, the width is 30.5 cm, and the stability coefficient of the trenching depth is 97.2%. Its power consumption is in a relatively good parameter group. When the cutter roller speed is 400 r/min, the forward speed is 0.8 km/s, and the trenching depth is 0.3 m, the average power consumption value of the trenching component is measured to be approximately 7.63 kW, with an 11.18% reduction in power consumption compared with the current trenching machines. And it meets the requirements of orchard agronomy and industry standards. In terms of improving productivity, its high-efficiency operation performance can greatly shorten the trenching operation time in the orchard, enabling fruit growers to arrange production more reasonably and increase fruit output. In reducing labor costs, mechanization replaces manpower, reduces manpower input, reduces labor cost expenditures, and at the same time avoids the instability of manpower operations. For the sustainability of orchard operations, the precise and stable trenching operation helps to optimize the utilization of land resources, ensure the quality of orchard agronomy, maintain the long-term production capacity of the orchard, promote the sustainable development of the orchard, and inject new impetus into the mechanization process of the fruit industry.

The limited separation efficiency of potato-soil separation equipment in the southern potato planting areas is attributed to the high viscosity of the soil. To enhance the performance of the lifting chain separation device, a concave bar was designed. Structural parameters influencing the efficiency of potato-soil separation by bars were determined through kinetic analysis during the separation and transportation of potato-soil mixtures. Both a potato simulation model and a sticky soil simulation model were developed. Simulation tests indicated that the concave bar outperforms the straight bar in separation efficiency. Key factors investigated include the angle of the concave side, the width of the concave bar, the depth of the concave bar, and the installation angle. Orthogonal simulations were conducted using separation efficiency and the maximum force on potatoes as evaluation metrics. The results demonstrated that with a concave side angle of 15°, a concave bar width of 450 mm, a concave bar depth of 60 mm, and an installation angle of 30°, the separation efficiency of the potato-soil mixture reached 79.7%, with a maximum force on potatoes of 35.218 N, achieving the highest separation efficiency. Based on these results, test devices were constructed, and field tests were performed. The field test results showed a damage rate of 1.58%, a potato epidermal injury rate of 1.03%, and a loss rate of 2.87%. These results comply with national standards and validate the reliability of the simulation findings.

To address the issues of low detection accuracy, slow speed, and large parameter size in detecting fresh table grapes in natural orchard environments, this study proposes an improved grape detection model based on YOLOv8n, termed YOLOGPnet. The model replaces the C2f module with a Squeeze-and-Excitation Network V2 (SENetV2) to enhance gradient flow through more branched cross-layer connections, thereby improving detection accuracy. Additionally, the Spatial Pyramid Pooling with Enhanced Local Attention Network (SPPELAN) substitutes the SPPF module, enhancing its ability to capture multi-scale information of the target fruits. The introduction of the Focaler-IoU loss function, along with different weight adjustment mechanisms, further improves the precision of bounding box regression in object detection. After comparing with multiple algorithms, the experimental results show that YOLOGPnet achieves an accuracy of 93.6% and mAP@0.5 of 96.8%, which represents an improvement of 3.5 and 1.6 percentage points over the baseline model YOLOv8n, respectively. The models computational load, parameter count, and weight file size are 6.8 Gflops, 2.1 M, and 4.36 MB, respectively. The detection time per image is 12.5 ms, showing reductions of 21.84%, 33.13%, 30.79%, and 25.60% compared to YOLOv8n. Additionally, comparisons with YOLOv5n and YOLOv7-tiny in the same parameters reveal accuracy improvements of 0.7% and 1.9%, respectively, with other parameters also showing varying degrees of enhancement. This study offers a solution for accurate and rapid detection of table grapes in natural orchard environments for intelligent grape harvesting equipment.

Self-propelled platforms designed for soil sampling represent a remarkable technological advancement in the field of soil research, providing efficient and precise collection of essential data regarding soil composition. These platforms are equipped with advanced technologies and sophisticated sampling systems, offering researchers the opportunity to obtain detailed data in an automated and replicable manner. In this article, we will delve into the technical features of self-propelled platforms for soil sampling and their significance in advancing soil sciences research. In this paper, a mathematical model is described to aid in the design of the propulsion system of self-propelled platforms intended for soil sample collection under conditions of movement on horizontal or sloping terrain. With this mathematical model, the forces required for the platform to move can be calculated, considering the constraints specific to each project. When the project is completed, and key parameters such as the total weight of the platform, dimensions, payload weight, and transmission gear ratios are known, the minimum power required for the engine to move the platform can be calculated.

Aiming at the problems of high skin-breaking rate and high impurity rate of sweet potato during harvesting operations, a low-damage fresh-eating sweet potato combine harvester based on a two-segment potato-soil separation device was designed by using a “d”-type elevator chain combined with a double-buffer clearing platform technology. The results show that the best working parameters of the harvester are a vibrating shaft frequency of 5.2 Hz, elevator chain speed of 0.37 m/s, and cleaning platform speed of 0.58 m/s, in which the sweet potato skin-breaking rate is 1.09% and the impurity rate is 1.90%, which is in line with standard.

Responding to the problem of over-returning of corn stalk to the field, this article proposes a comprehensive utilization model that returns 30% of corn stalk to the field from above the surface and recycles the remaining 70% as fodder is proposed. For the problems of poor quality of stalk crushing and fast knife wear, the experimental platform for corn stalk crushing with vertical shaft stepped saw disc knife was designed. The effects of different factors on the qualified rate of stalk cutting length and cutting power were investigated. The qualified rate of cutting length was the best 96.57% when the plant spacing was 20 cm. The cutting power was the smallest 322.94 W when the rotational speed of knife roller was 600 r/min. The stalk crushing process was analysed by high-speed camera, and the stalk crushing mechanism was clarified.

Accurately determining the angle of repose for irregular and dispersed agricultural grain materials requires a simulation model that effectively represents the actual grain shapes and utilizes numerical methods to analyze their stacking behavior. This study focuses on "Yongyou 15" rice grains, employing 3D raster scanning technology to obtain precise contour data. Through a reverse modeling process, a detailed 3D geometric model of the grains was developed, resulting in a discrete element model comprising 618 grains of varying diameters, created using granular polymer theory. Discrete element analysis software (EDEM) was integrated with MATLAB image processing to simulate the falling and stacking process of the rice grains within a stainless steel bottomless cylindrical tube. The contour of the grain heap was analyzed using linear fitting, followed by a micro-mechanical investigation of the grain heap structure. The analysis indicated that the pressure depression within the heap is caused by the oblique transmission of contact forces. The simulated angle of repose under experimental conditions was 31.29°±0.41°, differing by only 0.81% from the actual measured angle of 31.04°±0.21° obtained through physical stacking experiments. These results demonstrate that combining numerical simulations with image feature extraction is a reliable and efficient method for assessing the stacking properties of agricultural materials.

Blockchain technology provides a secure and transparent method to improve the traceability and sustainability of personal protective equipment (PPE) in Romanian agriculture. By enabling comprehensive monitoring of PPE across its lifecycle, blockchain addresses gaps in safety and compliance. It ensures adherence to safety standards while reducing risks of counterfeit or defective products. However, several challenges hinder adoption. High costs, limited digital infrastructure, and low digital literacy among farmers create significant barriers. Overcoming these requires targeted investments in rural digital infrastructure and training programs designed to enhance stakeholders skills. Public-private partnerships and pilot projects are essential to demonstrate the technology’s benefits and build trust among users. Gradual implementation, supported by collaboration between public and private sectors, can transform Romanian agriculture. Blockchain’s integration can lead to better productivity, improved safety, and greater sustainability. This study examines blockchain’s potential in PPE management, offering insights into its applications, benefits, and necessary steps for successful adoption in the agricultural sector. Through strategic action, Romanian agriculture can leverage blockchain to achieve safer practices and long-term sustainability.

The treatment of polyethylene film in farmland mainly relies on residual film recycling machinery. However, there is a lack of detailed reports on the characteristics of agricultural residual film recycling machinery and guidance direction for further optimization. This article conducted a detailed literature review, first introducing the hazards of pollution. Then, the operational principles of residual film mechanized recycling equipment were explained from three aspects. Finally, the research direction of residual film pollution control equipment is clarified: it is urgent to reduce the impurity content in residual film and facilitate the resource utilization.

Using hyperspectral imaging technology for rapid, non-destructive detection of soybean grain moisture content provides technical support for high-quality soybean harvesting. A total of 90 samples of soybean grains from different varieties were collected, with hyperspectral images acquired in the wavelength range of 900–1700 nm. The moisture content of each soybean grain sample was determined using the direct drying method as specified in GB 5009.3-2016. The samples were divided into a calibration set and a prediction set based on a 4:1 ratio using the sample partitioning method of Joint X-Y Distance. Eight preprocessing methods were applied to the raw spectral data, including baseline correction, moving average, Savitzky-Golay filtering, normalization, standard normal variate transformation, multiple scatter correction, first derivative, and deconvolution. Feature wavelengths were then extracted using the successive projections algorithm and the competitive adaptive reweighted sampling algorithm. Finally, a partial least squares regression model for predicting the moisture content of soybean grains was developed based on these feature wavelengths. The results show that the correlation coefficient and the root mean square error of the optimal model for the prediction set were 0.92 and 0.2371, respectively. The moisture spectrum inversion model can precisely and rapidly predict the moisture content of soybean grains non-destructively, thereby determining the timing of mechanical soybean harvesting and enhancing the quality of soybean harvesting, storage, and processing.

A multi-unmanned aerial vehicle (UAV) task allocation and path planning model with the maximum endurance constraint was constructed specific to the agricultura l patrol scene. Moreover, an optimized ant colony optimization (ACO) algorithm applicable to grid map environment was proposed given such problems of the traditional ACO algorithm as limited path search direction and field of view, failure to find the shortest path and proneness to deadlock. This method preprocessed the grid map environment, extracted the feature points of obstacles, and selected such feature points as the way-finding access nodes; then, the construction efficiency of the solution was enhanced via the nonuniform pheromone distribution based on ACO algorithm, the guiding function of path search was strengthened using Tent chaotic mapping, and the pheromone evaporation coefficient was dynamically adjusted to prevent the algorithm from too early convergence. The experimental results show that the proposed method more conforms to the operational requirements of rotary-wing UAVs with limited cruising ability in comparison with the existing methods. Besides, the convergence efficiency of the improved ACO algorithm embedded with the niche genetic algorithm is 30.55% higher than that of the traditional ACO algorithm. The experimental results verify the practicability and effectiveness of the proposed method.

The existing obstacle avoidance control algorithms for wheeled agricultural warehouse handling robots are prone to the local optimal solution in the process of path optimization and collision can occur easily occur during multi-robot simultaneous operation. Given this, the obstacle avoidance of wheeled agricultural warehouse handling robots was explored in this study, and an obstacle avoidance path planning algorithm for wheeled agricultural handling robots in warehouse based on improved ACO-DWA algorithm was proposed. Then, the moving trajectory of agricultural warehouse handling robots during handling process was studied, and their spatial kinematics equation was given. Next, the real-time pose of agricultural warehouse handling robots was detected, and their motion path was planned considering the real-time position of obstacles and the target locations of handling. In addition, the obstacle avoidance controlling quantity of agricultural warehouse handling robots was calculated according to the deviation between the pose of robots and the planned path. Supported by a controller, the obstacle avoidance control work of agricultural warehouse handling robots was realized. It was concluded through the effect experiment that compared with the traditional method, the improved ACO-DWA algorithm designed in this study significantly reduced the number of collisions between agricultural robots, and through practical application, the proposed algorithm can meet the needs of improving warehouse logistics management efficiency.

In order to solve the problems of small feeding amount, high manual labor intensity and low work efficiency of the existing straw crusher, a large straw crusher with cylinder feeding hammer is designed. The key parts of the machine are designed and calculated, and the mechanism of corn straw crushing in the crusher is analyzed, and the theoretical factors affecting the working effect of the crusher are obtained. With the rotation speed of crushing shaft, diameter of sieve and clearance of sieve-hammer as test factors, and the productivity, qualification rate of crushing length as performance evaluation indicators, a quadratic regression orthogonal rotation combination test with three factors and five levels was carried out. The regression mathematical model of test factors and performance evaluation indicators was established by using Design-Expert 13.0 software. With the goal of simultaneously maximizing the qualified rate of productivity and crushing length, multi-objective optimization solutions were carried out for the rotation speed of crushing shaft, diameter of sieve and clearance of sieve-hammer, and the optimal parameter combination was determined as follows: The rotation speed of crushing shaft is 1709.24r/min, the diameter of sieve is 22.83mm, the clearance of sieve-hammer is 15.38mm, the verification test shows that the productivity is 9187.98kg/h, and the qualification rate of crushing length is 93.87%. The machine improves the efficiency of crushing operation and can meet the design requirements.

This research aims to solve the problem of material accumulation on the screen surface of the cleaning device of tracked combine harvester and proposes an innovative solution of designing a pre-screening device based on the traditional cleaning device. The problem of material accumulation on the screen surface is improved, which in turn enhances the cleaning efficiency. In this study, CFD-DEM coupled simulation technology is used to simulate and analyse the cleaning device, and the effects of airflow velocity, vibration frequency and amplitude on the cleaning effect are explored, and the impurity content and loss rate are used as evaluation indexes. Through orthogonal experimental analysis, the optimal parameter combination of the cleaning device was determined as airflow velocity 11 m/s, vibration frequency 9 Hz, amplitude 30 mm, impurity content rate 2.10% and loss rate 1.62%. The experimental results show that increasing the pre-screening device can significantly improve the material separation effect, reduce the loss and improve the cleaning efficiency.

This study investigates the effects of Ohmic heating on rice grain hardness and heating uniformity in comparison to conventional cooking methods. A self-developed Ohmic heating system was utilized to cook rice, and the performance was evaluated against that of a traditional electric rice cooker. The experiments were meticulously designed to measure temperature uniformity and rice quality across various water-to-rice ratios, employing a 5-channel temperature acquisition system and texture analyzer to assess hardness. The results demonstrated that Ohmic heating offered superior temperature uniformity and, consequently, more uniform rice texture, with reduced hardness variability compared to conventional methods. The results show that ohmic heating technology has great potential to improve the cooking quality of rice, and provides theoretical basis and technical support for designing more efficient rice cooking equipment.

This paper introduces a cyclone separation sand sampler with diversion flow to enhance soil wind erosion monitoring accuracy. Using CFD simulations and orthogonal testing, the optimal structural parameters for improved sampling efficiency were determined. A response model showed strong predictive capability with a P-value less than 0.0001 and R² of 0.9482. Wind tunnel tests demonstrated that the new sampler achieved a 91.44% efficiency, surpassing the 90.81% of the traditional design. The research provides valuable data for more accurate wind-sand flow monitoring, aiding in desertification prevention and improving wind erosion assessments in grasslands.

To address the issue of mutual influence and coupling between the main shaft speed and feeding amount of the mobile straw compaction molding machine, which is beneficial for the intelligent operation of the compaction molding, this paper designs a PSO-ELM-GPC control model. This model integrates Particle Swarm Optimization (PSO) algorithm, Extreme Learning Machine (ELM), and Generalized Predictive Control (GPC). It uses the ELM optimized by PSO to predict the output of the main shaft speed and feeding amount, and adjusts the input of the GPC controller based on the deviation weight adjustment unit. Field simulation experiments show that the maximum dynamic deviation of the speed is 1.72%, and the deviation from the target value is 1.52%. The maximum dynamic deviation of the feeding amount is 1.22%, and the deviation from the target value is 1.42%. The PSO-ELM-GPC model designed in this paper can promptly correct the uncertainties in speed and feeding amount control caused by disturbances.

Aiming at the digging shovel of the red soil harvester in the cassava planting area, problems such as high digging resistance and difficulty in breaking the soil when harvesting cassava tubers will lead to blockage at the connection between the digging device and the transmission device. Using the rabbits front paws as a bionic prototype, three-dimensional scanning and reverse engineering technologies were employed to extract their unique geometric features. These quantified geometric structural characteristics were then applied to the design of an excavation shovel, aiming to reduce resistance during excavation operations. Based on the discrete element (EDEM) coupled RECURDYN, using the resistance of the excavation shovel and the Bonding key breakage rate as evaluation indicators, the discrete element orthogonal analysis of three factors including the shovel tooth length, shovel tooth width, and shovel edge inclination angle was carried out based on the simulation test results. Test was performed to determine the best parameter combination for drag reduction and crushing rate of the bionic shovel; the working resistance of the shovel was used as an evaluation index to verify the excavation performance of the bionic shovel through field tests. The optimal parameter combination of the bionic shovel based on the discrete element simulation test is the tooth length of 220 mm, the tooth width of 65.1 mm, and the blade inclination 60°. The excavation resistance of this combination is 1733.66 N and the maximum soil fragmentation rate is 92.9%. Through field tests, it can be found that when the excavation depth is 310 mm and the forward speed is 300 mm/s. The Type 1 bionic digging shovel exhibits a reduction in resistance of 6.84%, while the Type 2 bionic digging shovel demonstrates a more significant reduction of 9.21%, compared to the traditional digging shovel. Tests have shown that the bionic shovel type 2 has excellent soil excavation characteristics and can complete cassava excavation operations in tropical red soil areas. It can provide a design reference for reducing drag and saving energy for cassava harvesters.

In view of the problems such as the lack of comprehensive analysis of working parameters and structural parameters and the imperfection of the atomization model in existing studies,a dual-layer centrifugal atomizing nozzle was designed to optimize the droplet volume median diameter and droplet spectrum width. The key influencing factors, including atomizing disc speed, flow rate, and the number of atomizing teeth, were selected for optimization. Both single-factor and three-factor, three-level response surface optimization experiments were conducted to determine the optimal number of atomizing teeth for different combinations of disc speed and flow rate. Furthermore, with the spray width as the objective function, a Box-Behnken experimental design was employed to investigate the effects of atomizing disc speed, flow rate, and spray height on the atomization performance of the centrifugal nozzle. A multiple quadratic response surface regression model for spray width was also developed. The results indicated that both individual factors and their interactions had a significant impact on the droplet volume median diameter, droplet spectrum width, and spray width. The optimized number of atomizing teeth was found to be 50, and the adjusted R-squared values for the above three regression equations were 0.9977, 0.9893, and 0.9485, respectively. This study provides a theoretical basis for optimizing the nozzle structure and improving the quality of plant protection operations.

Millets are one of China s primary traditional food crops, and drought can adversely impact their yield and quality. To quickly detect the degree of drought stress in cereal grains, this study establishes a nondestructive classification model based on hyperspectral imaging technology. The raw spectral data underwent preprocessing using six pretreatment methods and various combinations of these methods. Subsequently, three distinct algorithms were employed for feature wavelength selection. To assess the severity of drought stress on millet, classification models were developed by integrating texture and color features, utilizing Support Vector Machine (SVM), Partial Least Squares Discriminant Analysis (PLS-DA), and Multilayer Perceptron (MLP) algorithms. The results indicate that the D1st-SVM model, based on CARS wavelength selection, exhibits the highest modeling performance when feature wavelengths are fused with significant texture and color variables, achieving an accuracy rate of 93%. These findings suggest that drought identification in millet can be performed quickly and nondestructively by integrating image features through hyperspectral imaging technology.

In order to adapt to the needs of modern intelligent warehousing and reduce the unloading side pressure of grain silo, the influence of curved funnel on the unloading process is designed and analyzed, and this paper uses EDEM simulation software and Hertz Mindlin model for analysis. The results found that compared with the unloading characteristics of the conical funnel, the unloading side pressure is effectively improved; in the unloading process, with the increase of the shrinkage rate, the critical position of the flow transformation increases and the flow velocity of the particle group accelerates. However, the flow velocity of the particle group decreases when it reaches the critical position of 250 mm. Combined with the corresponding analysis, this paper uses the Froude number to quantitatively express the mobility of particles. As the shrinkage rate of the curved funnel and the unloading height increase, the Froude number also increases. This paper analyzes and calibrates the curved funnel for soybean unloading in silos, which will provide useful reference for avoiding arching and clogging during the unloading process of grain particles.

The use of tractors for transporting agricultural produce is a widespread practice in Bulgaria and imposes high requirements for traffic safety. Assessing the braking performance of these vehicles is essential for effective risk management and accident prevention. This study provides a database of experimentally obtained data on the braking deceleration of Zetor Crystal HD 170 and Belarus 952.3 tractors under various braking conditions, gathered using modern equipment under real road conditions. The results offer valuable insights for expert analyses in accident investigations involving these vehicles and contribute to developing strategies for enhancing road safety.

To address the challenges associated with garlic single-seed extraction, including seed damage, blockages, and the inefficiency of directional placement for irregularly shaped garlic seeds, an air-suction seed metering device, integrated with image recognition and orientation technology, was developed. The working principle of the product is expounded, and comprehensive analysis and design of key components—such as the seed dispenser, directional needle, and combined suction block—are presented. A test bench was constructed using Jinxiang hybrid garlic as the experimental material, with the negative pressure in the air chamber, the rotation speed of the seed dispenser, and seed quantity in the seed chamber as the primary experimental factors. The performance indicators included the single seed rate, cavitation rate, and double grain rate. A three-factor, five-level orthogonal design was employed to assess the effects of each factor and their interactions on the single seed rate. The optimal operating parameters for garlic species A, B, and C were identified as follows: the negative pressure in the air chamber was 11.85 kPa, 12.03 kPa, and 13.21 kPa, respectively; the rotation speed of the seed dispenser was 7.13 r/min, 6.85 r/min, and 7.25 r/min, respectively; and the seed quantity in the seed chamber was 138.97 mm, 140.90 mm, and 141.24 mm, respectively. The corresponding single seed rates were 95.98%, 96.50%, and 96.23%, respectively. Under these optimal operating conditions, the study further explored the effects of the positional parameters of garlic seeds of different grades, as well as the positional parameters of the directional needle, on the preliminary directional performance. The optimal positional parameters were subsequently determined. The results of this study offer critical insights and establish conditions for the intelligent orientation of garlic planting machines.

New design of the conic shape screw conveyor is presented in the paper and the results of theoretical investigations of the bulk material kinematic movement in this conveyor were shown. Dependencies for finding kinematic and operating parameters, which provide long-term stability of such mixers were found. Stand equipment for investigation of the transporting efficiency and mixing of the bulk materials have been developed and manufactured. Mechanic screw conveyors’ properties at high frequency range of rotation, under smooth and sharp starting, rotation frequency change and reversing in the automatic regime have been studied taking advantage of the ALTIVAR 71 and multipurpose measuring system with accelerometers for finding dynamic loadings by PC. The results of experimental investigations of the efficiency depending on the screw pitch and every next turn, the screw conic surface inclination and the screw rotation frequency are presented. It will make possible to clear the process of the bulk materials transporting and mixing for every possible combination of the initial parameters. Specifically, increasing the increment of the cargo displacement radius per turn from 1 mm to 5 mm results in a velocity increase ranging from 1.28 times to 2.44 times. Moreover, increasing the inclination angle of the forming cone surface from 1 degree to 2 degrees provides a 1.18-fold increase in productivity, and varying the increment of the screw pitch from 0.004 m to 0.01 m per turn results in a 1.07-fold decrease in productivity. Increasing the rotation frequency of the conical screw a from 200 r/min to 500 r/min leads to a doubling of productivity.

Efficient seed distribution is crucial for maximizing crop yields in agricultural systems, particularly in vineyards and orchards where both large and small seeds are used. This research investigates the integration of EDEM by Altair simulations to enhance the distribution process of these seeds. Utilizing discrete element modeling (DEM), the study provides a comprehensive analysis of seed behavior during dispersal, including interactions with varying terrain and environmental conditions. Through detailed simulations, the research identifies critical parameters that influence seed placement accuracy, such as seed size, distribution patterns, and environmental factors. The results reveal that EDEM simulations can optimize the distribution of seeds, leading to reduced seed wastage and improved crop density uniformity. This integration not only facilitates a more precise sowing process but also offers valuable insights into the dynamics of seed distribution in complex agricultural systems. The findings suggest that adopting this simulation technology can significantly advance precision agriculture practices, offering tangible benefits for the efficiency and productivity of vine and fruit tree cultivation.

Multi-unmanned aerial vehicle (UAV) collaborative task planning and distribution path planning are the core content of agricultural UAV logistics distribution. In this study, the multi-UAV collaborative task planning and the distribution path planning were discussed, and such constraint conditions as UAV load capacity, battery capacity and flight time were comprehensively considered, aiming to reduce the number of UAVs and their power consumption. To ensure the safe and efficient completion of multi-UAV logistics distribution tasks, 3D agricultural ultralow space was subjected to environment modeling, and a bilevel planning model for collaborative planning of UAV distribution route and flight path was constructed. Then, an improved particle swarm optimization (PSO) algorithm with the improved learning factor and inertia coefficient was designed on the basis of PSO framework, and the global optimal solution in the current iteration was improved using variable neighborhood descent search. The feasibility of the proposed algorithm was verified by analyzing a practical case. With the central city area of XX City as the study area, 1 logistics & freight transportation center was taken as the central warehouse (coordinates: 50, 50, unit: km) and 50 intelligent express cabinets as the express cabinets of UAVs. The obtained results were comparatively analyzed with those acquired through the basic PSO algorithm. The results manifest that the proposed algorithm performs better than the compared algorithms. The improved PSO algorithm is superior to the basic PSO algorithm in aspects of total UAV flight distance, number of UAVs used and algorithm convergence time, indicating that the model and algorithm established in this study are feasible and effective.

For achieving more efficient recognition results and deployment on mobile devices, a rice blast recognition model was constructed by making lightweight improvements to YOLOv5s. First, using YOLOv5s as the base, GhostConv was introduced to replace standard convolution in its backbone and neck, and LightC3 module was built to improve the C3 module in the neck. This significantly reduced the computational burden and model size. Furthermore, Concat operator was replaced with BiFPN and SE attention mechanism was integrated to maintain accuracy when reducing model complexity. These modifications enhanced the models ability to capture fine-grained features and multi-scale information. The experimental results showed that the designed model had a 49% decrease in the number of model parameters and a 50% decrease in FLOPs without a decrease in precision on self-built rice blast dataset, compared with the YOLOv5s, achieving the good balance between detection performance and model lightweight. Then, an APP named RiceBlastDetector was built based on the model, achieving accurate detection in the scenario with the different characterization scale disease spots from experiments in the field, which can provide a reference for detecting other crop diseases.

In the context of the development of intelligent systems in the agricultural field, systems that help farmers in agricultural production, the realization of intelligent systems to help them in the commercialization of agricultural products is both actual and necessary. The commercialization of agricultural products directly by the farmer, at the farm gate, requires specially authorized spaces, personnel, connection to utilities, etc., which limits the possibilities for sale of products by farmers, thus losing both the final customer and the farmer, as the product enters the distribution chain, which implies an additional cost and uncertainties regarding the product origin. The article presents the SVIEE-R equipment, an automatic and autonomous system for selling agricultural products at the farm gate, which does not require the presence of a salesperson or connection to electricity and which can store agricultural products that require refrigeration, such as: vegetables, fruits, dairy products, etc. The intelligent SVIEE-R vending machine is a smart system that continuously informs both the farmer and the customer about the stock level of stored products, the storage temperature, and can be placed anywhere due to of the energy independence provided by an array of photovoltaic panels and accumulators.

Given that picking robots are faced with many picking tasks in the field operation environment and the target and obstacles are located at random and uncertain positions, an obstacle avoidance planning method for the picking path of virtual robots based on deep reinforcement learning was proposed to achieve rapid route planning of robots under a lot of uncertain tasks. Next, the random motion strategy of virtual robots was set according to the physical structure of robot bodies. By comparatively analyzing the advantages and disadvantages of the observed values input by different networks, an environmental observation set was established in combination with actual picking behaviors as the network input; then, a reward function was established by introducing the idea of target attraction and obstacle repulsion contained in the artificial potential field method, aiming to evaluate the behavior of virtual robots and increase the success rate of obstacle avoidance. The results of the simulation experiment showed that the success rate obtained by virtual robots in completing the picking task reached 95.5% under obstacles set at different positions. The coverage path length of the deep reinforcement learning algorithm was reduced by 272.79 in compared with that of genetic algorithm, with a reduction rate of 5.09%. The total time consumed by navigation was 1549.24 s, which was 83.15 s shorter than that of the traditional algorithm. The study results manifest that the system can efficiently guide virtual robots to rapidly reach the random picking points on the premise of avoiding obstacles, meet picking task requirements and provide theoretical and technical support for the picking path planning of real robots.

Daylily is widely used in medicine and diet therapy. In order to prolong the preservation period of daylily and make better use of its edible value, most of the daylily on the market are dried vegetables. Aiming at the problems of small size of dried daylily, similar color and texture between dried daylily, and difficulty in grading, this study proposes a method for grading dried daylily based on SSD. In the backbone feature extraction stage, the original backbone network VGG16 is replaced with the residual network model ResNet50 to realize the feature extraction of dried daylily. ResNet50 can deepen the network better and is more suitable for dried daylily feature extraction. Secondly, a feature fusion layer is added to improve the problem of insufficient utilization of shallow features in SSD network, which is more suitable for detail detection and improves the accuracy of dried daylily grading. Finally, the input image size is selected [512,512] to increase the image pixels, so that the network can capture more details of the dried daylily to improve the detection accuracy. The results show that the grading precision of the improved SSD algorithm is significantly improved compared with the traditional SSD, and the mean average precision is increased by 4.17%. At the same time, the same data set was used to test on the YOLOv5 model. Compared with YOLOv5s, YOLOv5s-CA and YOLOv5s-CBAM, the mean average precision was increased by 18.32%, 21.82% and 22.02% respectively, which further verified the precision and feasibility of the method and provided effective technical support for the grading of dried daylily.

The robot s operation in a grape orchard environment is often disrupted by obstacles such as vines and leaves, resulting in low fruit picking efficiency. To achieve stable obstacle avoidance, an improved RRT algorithm based on global adaptive step size and target-biased sampling was developed. First, the kinematic equations of the grape-picking robotic arm were established using the PoE method, and both forward and inverse kinematics calculations were performed to determine the robot s workspace. Then, to address the issues of lack of target orientation and other shortcomings in the traditional RRT algorithm when planning collision-free paths, dynamic updating and global adaptive step size strategies were proposed. Simulation experiments conducted using MATLAB software demonstrated that our improved RRT algorithm, compared to the RRT, RRT_informed, and RRT_star algorithms, offered advantages in terms of lower planning time, fewer sampling points, and shorter path lengths in both 2D and 3D map scenarios. Finally, grape-picking experiments were conducted in both a laboratory setting and a real orchard. The results demonstrated that the average path planning time using the proposed algorithm was shorter compared to baseline algorithms, effectively validating the efficiency and practicality of the algorithm.

The distribution of grain sizes in different soil samples is essential for agriculture and geotechnics, providing high-resolution soil maps crucial for land use planning. Traditional methods for soil texture analysis are reliable but often time-consuming and inconsistent. With that, this study aims to create an efficient predictive model for soil texture classification using deep learning techniques. A dataset of 4,556 images was extensively pre-processed and trained, with a model chosen for validation due to its low MSE value of 1.18. The model s performance, evaluated through Precision, Recall, and F1 Score, showed weighted averages of 88%, 78%, and 74%, respectively, and an overall accuracy of 94.56%. Validation using 456 images revealed high accuracy for Sandy and Clayey Soils but varying results for Loamy and Silty Soils. In Trial 1, the model achieved over 91% accuracy for all soil textures, with 100% accuracy for Sandy Soil. However, Trials 2 and 3 exhibited decreased accuracy for Loamy and Silty Soils, with the lowest accuracies at 61.40% and 65.78%, respectively. These results suggest that while the model is effective for certain soil textures, it requires further refinement and additional diverse training data to consistently match the reliability of traditional methods.

In view of the serious pollution of traditional fuel machinery in facility agriculture production, in order to facilitate the operation of tractors in greenhouses, in this study, a small tracked electric tractor suitable for facility agriculture was designed and analysed. In the early stage of design, the basic frame of pure electric tractor was established, the final chassis layout is determined through force analysis and layout setting; after the three-dimensional modeling of the tractor structure, the finite element analysis using the software Ansys verified that the stiffness and strength of the frame met the design requirements; the dual-motor drive structure was optimized. The maximum deformation of the track chassis frame was 0.685 mm, the maximum equivalent elastic strain of the track chassis frame was 0.0131 mm, and the maximum stress of the track chassis frame was 25.818 MPa, which was less than the yield stress of the selected material of the frame. ADVISOR was used to simulate the electric tractor during rotary ploughing and unladen transport operations. The simulation results showed that the dual-motor drive mode saved more than 20% of energy consumption compared with the single-motor mode and the transmission efficiency was improved by about 17%, which improved the operational efficiency of the tractor, and provided theoretical and data support for the subsequent prototype production.

The research aims to estimate the influence of the main vibration sources in grain harvesters on the combine operator. The study also has a comparative aspect, including two harvesters, a conventional one C 110H and one with an axial flow CASE IH. The main sources of vibration considered were the thresher, chassis, and header for both types of harvesters, with the addition of the shaker as a source for the conventional harvester. The receiver is considered to be the operator s seat. The emission spectra of each source are recorded according to ISO 2631 1 2001 and ISO 2631 5 2018, as well as the spectrum received at the operator s seat. To estimate the influence of vibration sources on the operator s seat, interspectral correlations and influence coefficients were studied. The conclusions are useful for ranking the intensity of vibration sources affecting the operator s comfort and for comparing the comfort level between two grain harvesters with different operational technologies. What the paper introduces as new in the field of estimating the exposure time limit to harvester vibrations is the calculation algorithm for the exposure time limit and vibration intensity estimators.

To meet the demands for rapid and accurate appearance inspection in potato sorting, this study proposes a potato appearance detection algorithm based on an improved version of YOLOv8. MobileNetV4 is employed to replace the YOLOv8 backbone network, and a triple attention mechanism is introduced to the neck network along with the Inner-CIoU loss function to accelerate convergence and enhance the accuracy of potato appearance detection. Experimental results demonstrate that the proposed YOLOv8 model achieves precision, recall, and mean average precision of 91.4%, 87.7%, and 93.7% respectively on the test set. Compared to YOLOv5s, YOLOv7tiny, and the original base network, it exhibits minimal memory usage while improving the mAP@0.5 by 1.1, 0.9, and 0.3 percentage points respectively, providing a reference for potato quality inspection.

The research aims to estimate the influence of the main vibration sources in grain harvesters on the combine operator. The study also has a comparative aspect, including two harvesters, a conventional one C 110H and one with an axial flow CASE IH. The main sources of vibration considered were the thresher, chassis, and header for both types of harvesters, with the addition of the shaker as a source for the conventional harvester. The receiver is considered to be the operator s seat. The emission spectra of each source are recorded according to ISO 2631 1 2001 and ISO 2631 5 2018, as well as the spectrum received at the operator s seat. To estimate the influence of vibration sources on the operator s seat, interspectral correlations and influence coefficients were studied. The conclusions are useful for ranking the intensity of vibration sources affecting the operator s comfort and for comparing the comfort level between two grain harvesters with different operational technologies. What the paper introduces as new in the field of estimating the exposure time limit to harvester vibrations is the calculation algorithm for the exposure time limit and vibration intensity estimators.

This study developed a chain counter-rotating trenching and backfilling device to reduce high tillage resistance in orchard operations. A 3D mathematical model of the trenching blade was created using Cartesian coordinate transformation. Taking curvature (ß), curve angle (a), bend radius (R), and cone residual angle (?) as experimental factors, and soil cutting resistance as the evaluation index. The optimized parameters were ß=95°, a=10°, R=24 mm, ?=39°. Bench tests were performed with the optimal parameters, and the results showed that the optimized ditching blade reduced the resistance by 18.7% compared to the ordinary trenching blades. Field test results showed a 9.64% reduction in furrowing torque.

Grassland farming plays a vital role in sustainable agricultural systems, providing forage resources for livestock production and contributing to environmental conservation. However, the labor-intensive nature of grassland management requires significant challenges for farmers. The adoption of appropriate mechanization technologies can improve efficiency, reduce labor requirements, and enhance overall productivity. This paper investigates the mechanization of grassland farming through technological variants with minimal inputs. The incorporation of sensor technologies and data analytics facilitates real-time monitoring of grass growth, enabling farmers to make decisions regarding grazing rotations and forage quality. Additionally, the utilization of smart sensors for soil moisture and nutrient content allows for targeted application of inputs, reducing waste and optimizing resource utilization. Overall, this article highlights the potential of mechanization and technological variants with minimal inputs to make efficient the grassland farming, improving productivity, sustainability and the livelihoods of farmers.

To address the challenges associated with garlic single-seed extraction, including seed damage, blockages, and the inefficiency of directional placement for irregularly shaped garlic seeds, an air-suction seed metering device, integrated with image recognition and orientation technology, was developed. The working principle of the product is expounded, and comprehensive analysis and design of key components—such as the seed dispenser, directional needle, and combined suction block—are presented. A test bench was constructed using Jinxiang hybrid garlic as the experimental material, with the negative pressure in the air chamber, the rotation speed of the seed dispenser, and seed quantity in the seed chamber as the primary experimental factors. The performance indicators included the single seed rate, cavitation rate, and double grain rate. A three-factor, five-level orthogonal design was employed to assess the effects of each factor and their interactions on the single seed rate. The optimal operating parameters for garlic species A, B, and C were identified as follows: the negative pressure in the air chamber was 11.85 kPa, 12.03 kPa, and 13.21 kPa, respectively; the rotation speed of the seed dispenser was 7.13 r/min, 6.85 r/min, and 7.25 r/min, respectively; and the seed quantity in the seed chamber was 138.97 mm, 140.90 mm, and 141.24 mm, respectively. The corresponding single seed rates were 95.98%, 96.50%, and 96.23%, respectively. Under these optimal operating conditions, the study further explored the effects of the positional parameters of garlic seeds of different grades, as well as the positional parameters of the directional needle, on the preliminary directional performance. The optimal positional parameters were subsequently determined. The results of this study offer critical insights and establish conditions for the intelligent orientation of garlic planting machines.

Mold-ripened cheeses exhibit complex mechanical behavior, influenced by factors such as degree of ripening, water content, fat content, and type of mold (white or blue). This study explored the viscoelastic behavior of four types of mold-ripened cheeses to highlight their physicochemical properties, taking into consideration that the viscoelasticity influences their texture, stability and behavior during storage and processing. Analyzing the local pressures applied to the cheese samples, for a contact surface with the cheese samples of approximately 51.5 mm², the pressure levels vary between 40 kPa (for Roquefort cheese) and 50 kPa (for Dorblu cheese); the pressure for the other two types of cheese falls within this range. The data obtained from this study confirmed that penetration resistance and yield stress values provide valuable information in cheese production, in particular for the texture and firmness of cheeses at various stages of the manufacturing process.

Unmanned Aerial Vehicles (UAVs) are revolutionizing precision agriculture, particularly in the domain of fertilization. Equipped with advanced sensors, mapping tools, and variable-rate application systems, drones enable farmers to precisely distribute fertilizers based on field variability. This targeted approach reduces waste, minimizes environmental impact, and optimizes crop yield. The integration of technologies such as multispectral imaging and AI-driven decision-making systems further enhances efficiency by allowing real-time assessment of soil and crop conditions. Despite their numerous advantages, challenges such as high costs, regulatory limitations, and technical scalability remain key barriers to widespread adoption. This article explores the innovations UAVs bring to precision fertilization, their benefits, and the obstacles hindering their broader application in agriculture

In the context of severe climate change over the past 20 years, which has led to reduced rainfall and reduced crop yields, identifying solutions to meet these challenges has become a priority for agricultural researchers. Thus, conservative and ecological, organic farming practices have emerged, which can mitigate and even improve crop productivity, even in these harsh conditions for agriculture. This paper is a synthesis of 425 papers published worldwide (Europe, North America, South America, Africa, Asia and Australia) and analyzes how conservative and organic farming practices have influenced the increase in soil quality and health through: no-tillage, covering land with agricultural residues, crop rotation etc.

This study introduced a vertical straw shredding device that integrates both cutting and shredding functionalities, with the crucial components of the blade designed in a bionic serrated pattern. In order to explore the interaction mechanism between the vertical cutting blade and straw, a mechanical model of the cutting force has been developed. Based on the ANSYS-LYNA software, a cutting model for straw-bionic blades has been established. Regarding the test factors, i.e., the spindle speed, blade installation angle,forward velocity, and blade tilt angle, single-factor and orthogonal tests were also performed. Furthermore, the maximum equivalent stress experienced by straw was taken as the primary evaluation index.Accordingly, the results revealed the influencing parameters to be ordered as the blade installation angle> spindle speed > forward speed > blade tilt angle. the optimized parameters for the cutting blade were determined to be a spindle speed of 1400 r/min, an installation angle of 55°, a forward speed of 4.0 km/h, and a blade tilt angle of 0°. Similarly, the optimized parameters for the shredding blade were established as a spindle speed of 1400 r/min, an installation angle of 50°, a forward speed of 4.0 km/h, and a blade tilt angle of 0°.

The article addresses the issue of reducing the energy consumption and minimizing the ejection of loosened soil mass from the cultivated zone in gardening tillage rotary machines with a vertical rotation axis. Generally, rotary tilling machines with a horizontal rotation plane are characterized by high energy consumption. Additionally, during the rotary tilling process, there is an undesirable phenomenon of soil mass ejection caused by the blade, resulting in exposed areas and furrows in the already cultivated zone. This results in a disruption of the technological quality of soil cultivation. Considering that both the resistance moment of the rotor and the intensity of soil mass ejection are significantly influenced by several structural-technological and soil physico-mechanical factors, the comprehensive and simultaneous identification of these influences remains a relevant challenge. The solution to the problem is based on the theory of adjusting the angular positioning of rotary tiller blades and the results obtained. The theoretical solution to the issue of soil mass ejection from the cultivated area relies on the model of oblique impact between two bodies under viscous friction conditions. As a result, expressions were derived that allow determining an optimal angular positioning pattern for the tiller blades and an optimal shape of the blade body. These ensure that the technological process is carried out with minimal energy consumption and the least possible soil mass ejection.

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.