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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. [ABSTRACT FROM AUTHOR]

UAV may be limited by its flight height and camera resolution when aerial photography of a tea garden is carried out. The images of the tea garden contain trees and weeds whose vegetation information is similar to tea tree, which will affect tea tree extraction for further agricultural analysis. In order to obtain a high-definition large field-of-view tea garden image that contains tea tree targets, this paper (1) searches for the suture line based on the graph cut method in the image stitching technology; (2) improves the energy function to realize the image stitching of the tea garden; and (3) builds a feature vector to accurately extract tea tree vegetation information and remove unnecessary variables, such as trees and weeds. By comparing this with the manual extraction, the algorithm in this paper can effectively distinguish and eliminate most of the interference information. The IOU in a single mosaic image was more than 80% and the omissions account was 10%. The extraction results in accuracies that range from 84.91% to 93.82% at the different height levels (30 m, 60 m and 100 m height) of single images. Tea tree extraction accuracy rates in the mosaic images are 84.96% at a height of 30 m, and 79.94% at a height of 60 m. [ABSTRACT FROM AUTHOR]

• A smart tea picking fixture that can adjust the posture without damage. • An Openmv smart camera-based tea classification platform with embedded migration learning mobilenet_v2 algorithm. • A new extraction method of tea bud picking points. Famous tea industry which need to harvest tea buds has great economic benefits. However, the harvesting is time-consuming and labor-intensive, especially with the shortage of labor currently, an intelligent tea bud picking robot is urgently needed. The vision system is a precursor to the development of a tea bud picking robot. To resolve such issues, we applied robotics and deep learning technologies to develop a computer vision system for intelligent picking of tea buds. The system was designed to recognize tea buds and extract their picking points. A method for locating the picking points was proposed based on a combination of YOLO-v3 algorithm, semantic segmentation algorithm, skeleton extraction and minimum bounding rectangle. An intelligent tea end-effector based on Personal Computer and microcontroller collaborative control was designed to solve the picking problem like complex shading and easy breakage. Thus, the picking rate of the overall system was improved. Based on Openmv smart camera embedded mobilenet_v2 algorithm as the visual model of the classification device, so that the quality of tea buds was preliminatively classified. Finally, the effects of different shooting angles and shooting methods as well as the accuracy of target detection and semantic segmentation algorithms on the extraction of tea bud picking points were investigated. The results show that the average accuracy of YOLO-v3 for identification of tea buds is 71.96% and the average horizontal positioning error of the robotic arm is 2.4 mm. Also, the average depth positioning error is 4.2 mm and the accuracy of tea bud picking point extraction is 83%. After the test, the successful picking rate of tea buds is 80% by this computer vision system of robot. The results of this study is potential to develop a machine-based tea picking system for industry and would contribute to the development of precision agriculture. [ABSTRACT FROM AUTHOR]