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 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.