When the silage harvester operates in the field, the filling and throwing system has a great influence on the efficiency of the silage harvester. Aiming at the feeding process of silage harvester, this study designs a precise throwing system for silage machine based on rotating target detection in order to realize the adaptive throwing of silage crumbs to the wagon compartment. In this study, a filling rule is proposed through the definition of ideal drop point and actual drop point. At the same time, an improved YOLOv5s (Rotation-YOLOv5, R-YOLOv5) was proposed to further realize the accurate detection of rotating targets using machine vision. The feedback of the target and the actual falling point were then calculated to control the movement of the throwing cylinder. The self-adaptive control was achieved in the throwing cylinder using R-YOLOv5. The network structure of the baseline was also optimized to predict the rotating carriages. A new prediction channel of rotation angle was then added to the Head part of the original YOLOv5. The virtual environment was built using Python3.9. Pytorch1.6 was chosen to train the R-YOLOv5 rotating target detection model on the Pytorch deep learning framework. Once the number of training rounds reached 60, the mAP values were all stable at 0.97, indicating a stable system. The improved R-YOLOv5 was recognized as a rectangle with a rotation angle. The recognition range was synchronized with the angle of the carriages. The training data was highly compatible with the carriages for better recognition. In addition, the throwing control was set under the distance between the actual and target drop position. A rotating carriage identification and a field test were carried out to develop the control flow. The test results show that the improved R-YOLOv5 was better performed on the rotating compartments at different rotating angles with an average of more than 97% before, indicating better detection; When the projectile barrel was rotated in the range of -60°−60°, angular velocity was less than 15°/s. The average accuracy of R-YOLOv5 target detection was higher than 90%; better recognition was achieved in the high accuracy of the actual drop position output, where more material was outflowed in unit time. The material outflow of the throwing cylinder in unit time was mainly related to the cutter's rotational and harvesting walking speed in the silage harvester. The correct rate of 85% or more was obtained to fully meet the harvesting requirements at the cutter rotational speed of 1 200 r/min, forward speed of 6 km/h, and the amount of material out of the unit between the Ⅳ level or more; The visual information was inputted by Arduino controller along with R-YOLOv5. The motion of the throwing cylinder was realized with 0.533 cm/pt, as the ratio of the actual distance to the image pixels. The average error between the theoretical and actual motion trajectory of the throwing cylinder was kept within 4%, which fully met the design requirements. The finding can provide a strong reference to develop the adaptive throwing-filling system in the self-propelled silage harvester.

Alfalfa (Medicago sativa L.) is one of the superior forage crops that is extensively cultivated in China. The surface of alfalfa seed is rich in dense hydrophobic wax-like substances, thus inhibiting water permeation, seed germination, and growth. Plasma treatment can serve as a physical seed treatment to promote seed germination and crop growth while offering environmental and economic advantages. In this study, the optimal conditions of plasma treatment were obtained to promote the growth of alfalfa. An improved model was constructed to clarify the actual effect of plasma treatment on alfalfa seeds. Zhongmu No. 4 was taken as the experimental material. The binary quadratic orthogonal rotation design was used to combine with germination tests. A systematic investigation was also made to explore the effects of plasma treatment power and treatment time on eight indicators of alfalfa seed germination, including seed germination potential, seed germination rate, seed conductivity, water absorption of the seed coat, seedling root length, seedling shoot length, seedling fresh mass, and seedling dry mass. The variance and principal component analysis were conducted to systematically evaluate the vigor of alfalfa seeds before and after plasma treatment. A mathematical model was established to integrate the plasma treatment power and treatment time with the comprehensive germination text index of alfalfa seeds, in order to seek an optimized condition of treatment plan. The results showed that the plasma treatment positively enhanced the vigor of alfalfa seeds; The variance analysis indicated that plasma treatment significantly (P<0.05) increased the water absorption of seed coat (20.93%), seedling shoot length (16.05%), seedling fresh mass (18.57%), and seedling dry mass (18.39%). There was a significant (P<0.05) decrease in the seed conductivity (28.86%), which was beneficial for the quality of alfalfa seeds. Principal component analysis was implemented to extract three main components with a cumulative contribution rate of 93.38%. A comprehensive evaluation model was established to explore the effects of plasma treatment on alfalfa seeds, according to the scores of the three principal components and their corresponding weights. The highest comprehensive score was achieved in the G12 experimental group (treatment power of 120 W, and treatment time of 12 s). Two factors (treatment power and treatment time) shared the threshold effect on the comprehensive germination test index of alfalfa, with the influence of treatment power greater than that of treatment time. There was no significant interactive effect of the two factors on the comprehensive germination test index of alfalfa (P＞0.05), indicating that the two factors were independent of each other, without substitution and counteraction. The processing conditions were optimized for the plasma treatment with a treatment power of 142 W and a treatment time of 16 s. Experimental verification showed that the comprehensive germination test index of alfalfa was 0.973 under the optimized treatment conditions, with a relative error of 2.83%, compared with the theoretical value of 0.955. An acceptable range of experimental error was achieved to further prove the rationality of the model. Therefore, the optimized conditions were obtained by the model for plasma treatment, indicating a quite reliable measurement. In summary, this finding can provide theoretical support to promote and apply the plasma seed treatment technology. A comprehensive evaluation was offered for the seed quality, which was a positive significance to promote the application of plasma in seed pre-sowing treatment.

In response to the weaknesses of traditional agricultural equipment chassis with poor environmental adaptability and inferior mobility, a novel unmanned agricultural machinery chassis has been developed that can operate stably and efficiently under various complex terrain conditions. Initially, a new wheel-legged structure was designed by drawing inspiration from the motion principles of locust hind legs and combining them with pneumatic-hydraulic linkage mechanisms. Kinematic analysis was conducted on this wheel-legged configuration by utilizing the D-H parameter method, which revealed that its end effector has a travel range of 0-450 mm in the X-direction, 0-840 mm in the Y-direction, and 0-770 mm in the Z-direction, thereby providing the structural foundation for features such as independent four-wheel steering, adjustable wheel track, automatic vehicle body elevation adjustment, and maintaining a level body posture on different slopes. Subsequently, theoretical analysis and structural parameter calculations were completed to design each subsystem of the unmanned chassis. Further, kinematic analysis of the wheel-legged unmanned chassis was carried out using RecurDyn, which substantiated the feasibility of achieving functions like slope leveling and autonomous obstacle negotiation. An omnidirectional leveling control system was also established, taking into account factors such as pitch angle, roll angle, virtual leg deployment, and center of gravity height. Joint simulations using Adams and Matlab were performed on the wheel-legged unmanned chassis, comparing its leveling performance with that of a PID control system. The results indicated that the maximum absolute value of leveling error was 1.08° for the pitch angle and 1.19° for the roll angle, while the standard deviations were 0.21647° for the pitch angle and 0.17622° for the roll angle, demonstrating that the wheel-legged unmanned chassis surpassed the PID control system in leveling performance, thus validating the correctness and feasibility of its full-directional body posture leveling control in complex environments. Finally, the wheel-legged unmanned chassis was fabricated, assembled, and subjected to in-place leveling and ground clearance adjustment tests. The experimental outcomes showed that the vehicle was capable of achieving in-place leveling with response speed and leveling accuracy meeting practical operational requirements under the action of the posture control system. Moreover, the adjustable ground clearance proved sufficient to meet the demands of actual obstacle crossing scenarios.