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.

The multi-agent manufacturing system has emerged as a well-established paradigm in intelligent manufacturing. Presently, challenges such as limited adaptability, elevated maintenance expenses, and complexities in enabling local agent deployment at end devices persist. To address such issues, a deployment model for the multi-agent manufacturing system was proposed, leveraging a cloud-edge collaboration architecture. However, managing agents effectively in this environment to establish resilient services, which are services capable of maintaining high availability, stability, and reliability even in the face of uncertainty, emergencies, or failures, for manufacturing systems remains a critical challenge that requires immediate resolution. In the present study, a cloud-edge-end oriented deployment architecture for multi-agent manufacturing system was proposed, and a real-time mapping method between edge agents and production resources based on the 5th generation mobile communication technology is constructed. At the same time, a resource optimisation method called swarm avian evolutionary algorithm is proposed. This method integrates particle swarm optimisation and meta-heuristics to minimise computation time and enhance system response speed. Finally, the proposed resource optimisation method is compared with the genetic algorithm, particle swarm optimisation, and snake optimiser algorithms. The results demonstrate that the convergence time is significantly reduced, indicating that the proposed method offers superior performance.

Harvesting equipment is closely related to the harvesting quality and efficiency, particularly for the Chinese wolfberry. In this study, a method of arrayed vibration unit was proposed in the mechanized harvesting equipment, according to the agronomy of hedgerow type Chinese wolfberry bilateral fruit hanging planting. The structural design of the arrayed vibration components and the fruit receiving platform was carried out, and then the walking, conveying, sorting and stability analysis of the whole machine were carried out. The arrayed layout parameters of different stubble numbers of Chinese wolfberry Xia Guo in Ningxia were measured. A series of significant paramerers were conducted to determine the vibration unit structure, vibration frequency, vibration angle, the crawler assembly, the transmission structure and driving of horizontal and vertical conveyor belts, the position of fruit receiving basket and lifting conveyor belt and the wind speed of debris sorting. Furthermore, the driving stability of the whole machine was verified on the slope. After that, the performance tests of the whole machine were carried out to evaluate the feasibility and efficiency of the mechanism, according to the test conditions and walking, harvesting and sorting. The selected indicators and the results showed that the maximum running speed of the whole chassis in all conditions was up to 0.252 m/s, the minimum turning radius was limited in 1.86 m, the maximum slope was 32°, the maximum crossing ditch width was no more than 0.5 m, and the maximum crossing step height was 11 cm. All the indicators were verified for the better walking performance, compared with the traditional. The harvesting object was taken as the sixth crop of Chinese wolfberry in Xiaguo, Ningxia Ningqi No.1. The amplitude was fixed at 15 mm. The optimum operating conditions were as follows: the driving speed was 1 300 r/min, vibration time was 5 s, the picking efficiency of mature Chinese wolfberry was 4 037 fruits per minute, the net picking rate of mature Chinese wolfberry was 88.95%, the damage rate of mature Chinese wolfberry was 3.64%, the damage rate of dried mature Chinese wolfberry was 4.17%, the comprehensive false picking rate of immature fruits, olives and flowers was 3.80%, and the efficiency ratio of mechanical picking to human picking was 26.91. All the performance indicators were presented that the Chinese wolfberry fruit of mechanized picking was fully met the requirements of its commercial processing. In addition, the wind speed was determined to be at least 5 m/s, which was beneficial to the separation of fruits and leaves.

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.