In order to address the challenges of inefficiency and insufficient accuracy in the manual identification of young citrus fruits during thinning processes, this study proposes a detection methodology using the you only look once for complex backgrounds of young citrus fruits (YCCB-YOLO) approach. The method first constructs a dataset containing images of young citrus fruits in a real orchard environment. To improve the detection accuracy while maintaining the computational efficiency, the study reconstructs the detection head and backbone network using pointwise convolution (PWonv) lightweight network, which reduces the complexity of the model without affecting the performance. In addition, the ability of the model to accurately detect young citrus fruits in complex backgrounds is enhanced by integrating the fusion attention mechanism. Meanwhile, the simplified spatial pyramid pooling fast-large kernel separated attention (SimSPPF-LSKA) feature pyramid was introduced to further enhance the multi-feature extraction capability of the model. Finally, the Adam optimization function was used to strengthen the nonlinear representation and feature extraction ability of the model. The experimental results show that the model achieves 91.79% precision (P), 92.75% recall (R), and 97.32% mean average precision (mAP)on the test set, which were improved by 1.33%, 2.24%, and 1.73%, respectively, compared with the original model, and the size of the model is only 5.4 MB. This study could meet the performance requirements for citrus fruit identification, which provides technical support for fruit thinning.

Many plug seedling grippers have a complex structure and bulky volume in design. They have poor performance in holding substrate and keeping a high integrity rate. In this paper, a new multi-needle seedling gripper is proposed. Based on the elasto-plastic contact model (ECM) in the EDEM, numerous particles with different material properties are modeled to simulate real seedling substrate and to study the interaction between steel needle and growing substrate. Taking the medium integrity rate as an evaluation index, explore the influence of different needle diameters, insertion depths, and insertion and grabbing speeds on the substrate integrity through the response surface method. The optimized technical parameters of the gripper was obtained through Design-Expert. The results show that the insertion depth of needles has a significant effect on the integrity of the raising medium, and the optimal depth is 40 mm. Insertion and grasping speed and depth have an interaction effect, while other factors have no significant influence on substrate integrity. Optimization results imply that when the diameter of the needle was 3 mm, the insertion depth was 40 mm, and the insertion and grabbing speed was 1 m/s, the growing medium integrity rate was the highest, reaching 89.10%. Under these parameters, prototype tests were conducted, and the highest medium integrity rate was 87.34%, which increased by an average of 7.25% compared with existing studies. The research has reference value for improving the operation quality of the plug seedling transplanter and the design of the seedling gripper. It also shows that solving problems of needle-discrete substrate interaction based on the discrete element method is feasible.

The construction of standardized tea gardens is the main trend in the development of modern agriculture worldwide. As one of the most important economic crops, tea has increasingly stringent requirements placed on its planting capacity and quality. The application of machine vision technology has led to the gradual development of tea production moving towards intelligence and informatization. In recent years, research on tea production based on machine vision technology has received widespread attention, as it can greatly improve production efficiency and reduce labor costs. This article reviews the current application status of machine vision technology in tea pest monitoring, intelligent harvesting, quality evaluation, and classification, and analyzes and discusses specific challenges around machine vision technology in tea production; for example, this technology lacks a standard database and weather interference, training errors in the model, and differences in the hardware computing speed can become a challenge. Based on the current research and application of machine vision technology in various fields, this article looks ahead to the development prospects and future trends of machine vision technology in tea production applications, such as future research to further integrate multiple types of sensors, improvements in the quality and usability of datasets, optimized model algorithms for existing problems, dissemination of research results, and intelligent management of tea production through machine vision technology.

As the global fruit growing area continues to increase and the population aging problem continues to intensify, fruit and vegetable production is constrained by the difficulties of labor shortages and high costs. Single-arm harvesting robots are inefficient, and in order to balance harvesting accuracy and efficiency, research on multi-arm harvesting robots has become a hot topic. This paper summarizes the performance of multi-arm harvesting robots in indoor and outdoor environments from the aspects of automatic navigation technology, fruit and vegetable identification and localization, multi-arm workspace optimization, and multi-arm harvesting task planning and analyzes their advantages and challenges in practical applications. The results show that the lack of application of automatic field navigation for multi-arm harvesting robots, the low harvesting rate in non-structured environments, and the complexity of algorithms for multi-arm harvesting robots’ task planning are the main challenges hindering their wide-scale application. Future studies need to focus on building a standardized growing environment to control the amount of information acquired by the robots and optimize the multi-arm control strategy of these challenges, which is an important direction for research on multi-arm harvesting robots.

The approach of dynamic tracking and counting for obscured citrus based on machine vision is a key element to realizing orchard yield measurement and smart orchard production management. In this study, focusing on citrus images and dynamic videos in a modern planting mode, we proposed the citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, Kalman filter tracking, and the Hungarian algorithm. The YOLOv7-tiny model was used to detect the citrus in the video, and the Kalman filter algorithm was used for the predictive tracking of the detected fruits. In order to realize optimal matching, the Hungarian algorithm was improved in terms of Euclidean distance and overlap matching and the two stages life filter was added; finally, the drawing lines counting strategy was proposed. ln this study, the detection performance, tracking performance, and counting effect of the algorithms are tested respectively; the results showed that the average detection accuracy of the YOLOv7-tiny model reached 97.23%, the detection accuracy in orchard dynamic detection reached 95.12%, the multi-target tracking accuracy and the precision of the improved dynamic counting algorithm reached 67.14% and 74.65% respectively, which were higher than those of the pre-improvement algorithm, and the average counting accuracy of the improved algorithm reached 81.02%. The method was proposed to effectively help fruit farmers grasp the number of citruses and provide a technical reference for the study of yield measurement in modernized citrus orchards and a scientific decision-making basis for the intelligent management of orchards.

The segmentation and positioning of tea buds are the basis for intelligent picking robots to pick tea buds accurately. Tea images were collected in a complex environment, and median filtering was carried out to obtain tea bud images with smooth edges. Four semantic segmentation algorithms, U-Net, high-resolution network (HRNet_W18), fast semantic segmentation network (Fast-SCNN), and Deeplabv3+, were selected for processing images. The centroid of the tea buds and the image center of the minimum external rectangle were calculated. The farthest point from the centroid was extracted from the tea stalk orientation, which was the final picking point for tea buds. The experimental results showed that the mean intersection over union (mIoU) of HRNet_W18 was 0.81, and for a kernel with a median filter size of 3 × 3, the proportion of abnormal tea buds was only 11.6%. The average prediction accuracy of picking points with different tea stalk orientations was 57%. This study proposed a fresh tea bud segmentation and picking point location method based on a high-resolution network model. In addition, the cloud platform can be used for data sharing and real-time calculation of tea bud coordinates, reducing the computational burden of picking robots.