Existing pecan mechanized harvesters have been limited to the fruit removal efficiency and adaptability in recent years. In this study, a hydraulically-driven differential vibration was proposed with the dual-drive counter-rotating for pecan harvesting operations. Firstly, the overall structure was realized for the vibration-based shaker of the harvester suitable for the large pecan trees. A picking model was developed to couple rigid-flexible dynamic vibration, in order to simulate the interaction between the clamping-vibration component and the main trunk of the pecan tree. The clamping assembly was treated as a rigid body, while the tree trunk was modeled as a flexible body. A systematic analysis was also performed on their coupled vibratory dynamics. Then, the variation patterns were determined for the excitation force under counter-rotating differential vibration. The influencing factors on the harvesting performance were identified as the base rotational speed for one of the hydraulically driven motors, the differential rotation coefficient between the two hydraulically driven motors, and the vibration duration applied to the pecan tree. Secondly, finite element simulation software was adopted to design the structure and operation parameters of key parts of excitation and clamping, in order to enhance the performance of the system. Specifically, the geometry of the eccentric mass blocks was refined, including their structural configuration, eccentric offset distance, and mass moment of inertia, according to the structural parameters and material selection of the excitation main shaft. Additionally, the parameters of the dual hydraulic-driven motors were established to determine the design value of the clamping preload force. Finally, a pecan picking machine was trial-produced under picking test conditions. A full machine performance test was then conducted to verify the simulation. The results showed that the optimal operating parameters were a base rotational speed of 1 200 r/min, a rotational coefficient of 0.8 based on the base speed, and a vibration duration of 10 s, particularly for the mature pecan trees with trunk diameters of 35-45 cm, the harvester achieved a fruit picking rate of 88.8% in a single shaking pass per tree under this condition. Considering the damage caused by vibration to the tree body and the actual harvesting effect, adjust the optimal vibration time to 7 s, and The comprehensive performance verification test results of the whole machine under this condition showed that the average pecan picking rate was 85.0% in a single shaking pass per tree, without any destructive damage to the tree trunk. Meanwhile, the average harvesting efficiency was 40 trees per hour, which was approximately 16 times that of manual harvesting. The developed harvester fully met the high requirements of efficiency and adaptability during mechanical harvesting of pecan in orchards.

With the nature of the high wind and sand in western China, the Chinese wolfberry recognition shows a strong relationship with the sandy noise and needs a high-accuracy algorithm. To address this issue, this study aimed to develop an algorithm for accurately detecting and recognizing wolfberries. YOLOv8, an algorithm promoted by Ultralytics, supports image classification, object detection, and instance segmentation tasks. To enhance the performance of the original YOLOv8 model, a novel YOLOv8 algorithm incorporating FasterNet, RepBiFPN, and Lightweight Asymmetric Dual-Head was proposed. Firstly, thousands of Chinese wolfberry images were collected from the Ningxia Academy of Agriculture and Forestry Science, China, and random noises were added to simulate the wind and sand conditions typical of spring. Secondly, leveraging the advantages of YOLOv8n, such as its high speed and accuracy, this research innovatively integrated the FasterNet block into the C2f module of YOLOv8 to improve the effective handling of data uncertainty and noise. Additionally, an innovative RepViT+BiFPN, a new detective head, and a Lightweight Asymmetric Dual-Head were introduced to improve the training efficiency of the YOLOv8 network. Finally, to evaluate the effectiveness of improved YOLOv8 for the recognition of wolfberry, the dataset of wolfberry images was divided into a training set, a validation set, and a testing set to assess the performances of different models. Experiment results demonstrate that the YOLOv8-FasterNet+LADH+RepBiFPN model outperforms other models in terms of mAP@0.50-0.95, achieving a 4.5% improvement on the validation set compared to the original YOLOv8n. This research addresses the high-speed and accurate recognition of the Chinese wolfberry under strong winds and sand noise through algorithmic improvements and integration, which can facilitate the automation and intelligence of Chinese wolfberry harvesting and contribute to the advancement of agricultural mechanization.

In order to realize the efficient and high-quality mechanical picking for Chinese wolfberry, firstly, the forced reciprocating vibration picking principle of the Chinese wolfberry branch was studied, and the mechanical model of vibration picking was established based on the simplified cantilever model, and the response analysis and solution of all positions for the branch were carried out. At the same time, the critical mechanical model of fruit detachment under the condition of fruit hanging on branches was established, and the theoretical values of inertia force for each component of the branch were obtained. Secondly, through actual measurement and finite element modeling, the natural frequency and forced vibration response simulation for each component of the branch of Chinese wolfberry terminal branch model were both studied, and the relationship between single-point periodic excitation force and high-quality fruit shedding parameters was obtained. Thirdly, according to the conclusion of the picking model, a test bench with many groups of adjustable parameters was built. Finally, the last branch of fruit-hanging Chinese wolf berry for Ningqi No.1 was taken as the experimental object, a four-level orthogonal experiment was designed with three factors: frequency, amplitude and entrance angle. Meanwhile, the net picking rate, damage rate and false picking rate were taken as the evaluating indicators, referring to the comprehensive scores of the three factors. It was concluded that the primary and secondary relations of factors affecting the picking effect are frequency, amplitude and entrance angle, and the best operation parameters are frequency of 20 Hz, amplitude of 15 mm, and entrance angle of 45°, then, a hand-held vibration picker with setting parameters was trial-produced, and the optimal parameter combination was verified in the Chinese wolfberry planting base of the National Chinese wolfberry Engineering and Technology Research Center. The results showed that the net picking rate of ripe Chinese wolfberry was 96.13%, the damage rate of fruit was 1.13%, and the false picking rate was 3.23%, mechanized picking efficiency was 30.28 kg/h, which is 6.65 times that of manual picking. The experimental results are consistent with the simulation results. The research results can provide an important basis for the creation and operation standards of large-scale Chinese wolfberry vibration harvesting equipment.

In order to find out the matching principle of excitation force and energy consumption of reciprocating vibrating Chinese wolfberry picking device, the energy consumption mechanism of reciprocating vibrating Chinese wolfberry picking device is studied. According to the structural characteristics and operating principle of the picking device, the no-load and load movement and force characteristics of the crank bearing and vibrating component are analyzed, and the theoretical model of energy consumption of the reciprocating vibration picking device is jointly constructed, and the simulation analysis is carried out. The results show that the vibrating component and load mass have a significant influence on torque, the load air resistance phase has a significant effect on torque, and the load air resistance and friction coefficient have no significant influence on torque. Subsequently, by building an AC servo motor torque detection system and a torque sensing detection system, verification experiments are carried out, the maximum torque of the preset system is 1.3 N∙m, the rated power is 400 W, the motor frequency is 20 Hz, the amplitude is 15 mm, and the total mass of the vibrating component is 0.143 kg. Test results show that, the no-load operation, the change trend of detected torque is consistent with simulation, the torque model is verified to be accurate. The maximum torque of simulation and detection are 0.52 N∙m and 0.57 N∙m respectively, and the error between test and simulation is 9.6%. For load operation, the maximum torque of five groups of branch loads of 20 g, 60 g, 100 g, 140 g, 180 g and 220 g are detected to be 0.73 N∙m, 0.74 N∙m, 0.75 N∙m, 0.82 N∙m and 0.83 N∙m, respectively, and the relationship model between load and torque is obtained by fitting. The research results can provide a theoretical basis which can configure a suitable motor in the reciprocating vibration Chinese wolfberry picking device with a certain load limit.