Camellia oleifera fruits are randomly distributed in an orchard, and the fruits are easily blocked or covered by leaves. In addition, the colors of leaves and fruits are alike, and flowers and fruits grow at the same time, presenting many ambiguities. The large shock force will cause flowers to fall and affect the yield. As a result, accurate positioning becomes a difficult problem for robot picking. Therefore, studying target recognition and localization of Camellia oleifera fruits in complex environments has many difficulties. In this paper, a fusion method of deep learning based on visual perception and image processing is proposed to adaptively and actively locate fruit recognition and picking points for Camellia oleifera fruits. First, to adapt to the target classification and recognition of complex scenes in the field, the parameters of the You Only Live Once v7 (YOLOv7) model were optimized and selected to achieve Camellia oleifera fruits’ detection and determine the center point of the fruit recognition frame. Then, image processing and a geometric algorithm are used to process the image, segment, and determine the morphology of the fruit, extract the centroid of the outline of Camellia oleifera fruit, and then analyze the position deviation of its centroid point and the center point in the YOLO recognition frame. The frontlighting, backlight, partial occlusion, and other test conditions for the perceptual recognition processing were validated with several experiments. The results demonstrate that the precision of YOLOv7 is close to that of YOLOv5s, and the mean average precision of YOLOv7 is higher than that of YOLOv5s. For some occluded Camellia oleifera fruits, the YOLOv7 algorithm is better than the YOLOv5s algorithm, which improves the detection accuracy of Camellia oleifera fruits. The contour of Camellia oleifera fruits can be extracted entirely via image processing. The average position deviation between the centroid point of the image extraction and the center point of the YOLO recognition frame is 2.86 pixels; thus, the center point of the YOLO recognition frame is approximately considered to be consistent with the centroid point of the image extraction.

To address the problems of high damage rate, low seeding accuracy, and poor seeding generally in the seeding process, a general-purpose seeding device was designed in this study based on the principle of mechanical pneumatic combined seeding. The air-blowing-type cleaning and seed unloading of the device laid the conditions for precise seeding and flexible seeding. In addition, single-factor experiments were performed on seeds (e.g., soybeans, corn, and rape-seeds) with different particle sizes and shapes to verify the general properties of the seed metering device. A multi-factor response surface optimization experiment was performed by applying soybean seeds as the test object to achieve the optimal performance parameters of the seed metering device. At a seed-clearing air velocity of 16.7 m/s, a seed feeding drum speed of 13.7 r/min, and a hole cone angle of 35.6°, corresponding to the optimal performance index, the qualified index, the replay index, and the missed index reached 97.94%, 0.03%, and 2.03%, respectively. The verification test results are consistent with the optimized ones. As indicated from the results, the seed metering device exhibits good general properties, low damage rate, great precision, and high efficiency; it is capable of meeting general seeding operations of different crop seeds and technically supporting for the reliability and versatility of the seeder.

In agricultural engineering, the discrete element simulation of the operational structure, object of movement, and force has become a standard method of modern agricultural equipment design. The selection and development of an appropriate contact model are critical factors affecting the accuracy of the process of the simulation calculation of the movement and force. Understanding how to choose or establish suitable contact models according to different research fields, objects, and purposes has become the focus of present research. This paper gives an overview of contact models for discrete element simulation, summarizes and analyzes the simulation calculation basis of different contact models, and focuses on the application status and scenarios of different models at this stage. It analyzes and summarizes the selection basis and application fields of contact models. The next direction in the development of discrete element simulation contact models should be the hybrid application of multicontact models and the precise development of specialized contact models. It is necessary to establish a standardized parameter-calibration process for different contact models to guarantee the accuracy of the models, to improve the application of computer arithmetic, and to establish an efficient and accurate simulation contact model selection and application in the field of agricultural engineering. Efficient and accurate simulation contact model selection, design theory, and calculation processes will improve the efficiency of modern agricultural machinery design.

Timely and effective drying of agricultural products is crucial for ensuring the quality and yield of grains. Biomass drying enhances energy utilization and reduces energy pressure. To this end, a novel multi-channel circulating biomass hot air furnace was designed to provide precise control of the heat source for grain drying, thereby improving the efficiency and quality of the drying process. The combustion process utilizes a multi-channel combined air supply to ensure complete combustion of biomass pellet fuel. During the heat exchange process, heat exchange plates isolate hot and cold areas, discharging combustion exhaust, while ensuring a pure air output. Using rapeseed as the drying subject, a temperature controller based on adaptive fuzzy PID was designed, targeting the biomass hot air furnace’s heat exchange system for modeling and verifying the model with the step response method. Model simulations were conducted in Matlab’s Simulink module using both adaptive fuzzy PID and traditional PID controllers, for a given signal. The settling times for the conventional PID and fuzzy PID were 445 s and 364 s, respectively, with overshoots of 20.1% and 6.3%, showing that the fuzzy PID controller performed better in terms of control performance. The validation tests showed that both control methods could maintain the temperature within ±5 °C. Compared to traditional PID control, the adaptive fuzzy PID control achieved a precision of ±3 °C. At the target temperature of 90 °C, the error was reduced to 3.7%, with a stabilization time of 1014 s. The use of fuzzy PID control exhibited better dynamic response characteristics, meeting the drying needs of rapeseed. This study provides a theoretical basis for the structural design and control system design of biomass hot air furnaces.

In the simulation analysis of the lily harvesting process, the intrinsic parameters of the lily bulb and the contact parameters between the lily bulb and the lily mechanized harvesting equipment (Q235 steel) are deficient. Thus, the three-axis size, density, moisture content, Poisson’s ratio, elastic modulus, and other parameters of lily bulbs are measured in this paper with lily bulbs as the research object. Moreover, the discrete element model of the lily bulb was established using 3D scanning technology. The contact parameters between the lily bulb and Q235 steel were calibrated through bench test and simulation parameter test. The relative error between the measured value of the lily bulb accumulation angle and the simulated value is taken as response value to calibrate three parameters (collision recovery coefficient, static friction coefficient, and dynamic friction coefficient between lily bulbs). A regression model of the relative error of the stacking angle and three parameters is established, and the response surface is optimized. The results demonstrate that collision recovery coefficient, static friction coefficient, and dynamic friction coefficient between lily bulb and Q235 steel are 0.301, 0.423, and 0.063, respectively; these coefficients between lily bulbs are 0.455, 0.425, and 0.158, respectively. Additionally, a better combination of parameters is adopted to perform the simulation stacking test. The measured stacking angle is 32.31°, which is 0.34% in error with the stacking angle obtained by the physical stacking test. The test results suggest that the discrete element model and contact parameters of the lily bulb can be used in the discrete element simulation test. Furthermore, these research results could provide references for simulation tests, such as mechanized harvesting and post-harvest processing, of lily.

Existing calibration methods for contact parameters of soil discrete elements are primarily based on a single physical property of soil (e.g., natural accumulation angle and shear characteristics). To solve this limitation, a calibration method based on the area difference method was proposed in accordance with the actual operation effect of ridge and furrow opener. The typical clay loam in the middle and lower reaches of the Yangtze River (moisture content of 20.66% ± 1%) was used as the research object. The Hertz–Mindlin with JKR Cohesion contact model was selected, and the static friction factor and rolling friction factor between soil and 65Mn steel were determined by inclined plane mechanics method. Soil tank ridge and furrow opening experiments and quadratic orthogonal rotation combined tests were carried out. MATLAB software was used to carry out polynomial fitting and the constant integral solution to the ridge and the cross-sectional contour data of the furrow obtained from the measured and simulated ridge and furrow opening experiments to establish the regression model of area difference. The optimization function in Design-Expert software was adopted. The optimal parameter combination was obtained by optimizing the regression model with the minimum area difference as the target. Based on the above parameter combination, the relative error of cross-sectional area of the furrow that was obtained by simulation test and soil tank test was 9.15%. Finally, the comparison of the traction resistance of the simulation and the soil tank test at the stable working stage indicated that the relative error between the simulated value and the measured value was 10.4%, which is less than the acceptable error of 15% in general statistics and within a reasonable range. The results showed that the particle movement and mechanical properties of the soil simulation model were basically consistent with the actual soil after parameter optimization and calibration. In this study, a precise discrete element simulation model of the typical clay loam in the middle and lower reaches of the Yangtze River based on the area difference method was constructed, which lays a theoretical basis for later exploration of the interaction mechanism between the typical clay loam and the soil contact components in the middle and lower reaches of the Yangtze River.