Tea picking point detection and location based on Mask-RCNN

• Published in: Information processing in agriculture Vol. 10; no. 2; pp. 267 - 275
• Main Authors: Wang, Tao, Zhang, Kunming, Zhang, Wu, Wang, Ruiqing, Wan, Shengmin, Rao, Yuan, Jiang, Zhaohui, Gu, Lichuan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-06-2023; Elsevier
• Subjects: Buds and leaves; Deep learning; Image processing; Mask R-CNN; Picking points; Deep learning; Picking points; Buds and leaves; Image processing; Mask R-CNN
• ISSN: 2214-3173; 2214-3173
• DOI: 10.1016/j.inpa.2021.12.004

The accurate identification, detection, and segmentation of tea buds and leaves are important factors for realizing intelligent tea picking. A tea picking point location method based on the region-based convolutional neural network(R-CNN) Mask- RCNN is proposed, and a tea bud and leaf and picking point recognition model is established. First, tea buds and leaf pictures are collected in a complex environment, the Resnet50 residual network and a feature pyramid network (FPN) are used to extract bud and leaf features, and preliminary classification and preselection box regression training-performed on the feature maps through a regional proposal network (RPN). Second, the regional feature aggregation method (RoIAlign) is used to eliminate the quantization error, and the feature map of the preselected region of interest (ROI) is converted into a fixed-size feature map. The output module of the model realizes the functions of classification, regression and segmentation. Finally, through the output mask image and the positioning algorithm the positioning of the picking points of tea buds and leaves is determined. One hundred tea tree bud and leaf pictures in a complex environment are selected for testing. The experimental results show that the average detection accuracy rate reaches 93.95% and that the recall rate reaches 92.48%. The tea picking location method presented in this paper is more versatile and robust in complex environments.