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Tea picking point detection and location based on Mask-RCNN

Information Processing in Agriculture 2023年第2期10卷 267-275页

作者： Tao Wang Kunming Zhang Wu Zhang Ruiqing Wang Shengmin Wan Yuan Rao Zhaohui Jiang Lichuan Gu School of Information and Computer Science Anhui Agricultural UniversityHefei 230036China Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment Anhui Agriculture UniversityHefei 230036China Institute of Intelligent Agriculture Anhui Agricultural UniversityHefei 230036China

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 ***,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 *** output module of the model realizes the functions of classification,regression and ***,through the output mask image and the positioning algorithm the positioning of the picking points of tea buds and leaves is *** hundred tea tree bud and leaf pictures in a complex environment are selected for *** 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.

关键词： Deep learning Mask R-CNN Image processing Buds and leaves picking points

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