A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

作者：Fenyun Zhang, Hongwei Sun, Shuang Xie, Chunwang Dong, You Li, XU Yi-ting, Zhengwei Zhang, Fengnong Chen · 发表于：Frontiers in Plant Science · 年份：2023 · DOI：10.3389/fpls.2023.1199473 · 被引用次数：21 · 研究领域：Smart Agriculture and AI、Advanced Neural Network Applications、Spectroscopy and Chemometric Analyses

Introduction: The identification and localization of tea picking points is a prerequisite for achieving automatic picking of famous tea. However, due to the similarity in color between tea buds and young leaves and old leaves, it is difficult for the human eye to accurately identify them. Methods: To address the problem of segmentation, detection, and localization of tea picking points in the complex environment of mechanical picking of famous tea, this paper proposes a new model called the MDY7-3PTB model, which combines the high-precision segmentation capability of DeepLabv3+ and the rapid detection capability of YOLOv7. This model achieves the process of segmentation first, followed by detection and finally localization of tea buds, resulting in accurate identification of the tea bud picking point. This model replaced the DeepLabv3+ feature extraction network with the more lightweight MobileNetV2 network to improve the model computation speed. In addition, multiple attention mechanisms (CBAM) were fused into the feature extraction and ASPP modules to further optimize model performance. Moreover, to address the problem of class imbalance in the dataset, the Focal Loss function was used to correct data imbalance and improve segmentation, detection, and positioning accuracy. Results and discussion: The MDY7-3PTB model achieved a mean intersection over union (mIoU) of 86.61%, a mean pixel accuracy (mPA) of 93.01%, and a mean recall (mRecall) of 91.78% on the tea bud segmentati...