Yan Lijie, Wu Kaihua, Lin Jia, Xu Xingang, Zhang Jingcheng, Zhao Xiaohu, Tayor James, Chen Dongmei
School of Automation, Hangzhou Dianzi University, Hangzhou, China.
Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Front Plant Sci. 2022 Aug 12;13:962391. doi: 10.3389/fpls.2022.962391. eCollection 2022.
Tea is one of the most common beverages in the world. In order to reduce the cost of artificial tea picking and improve the competitiveness of tea production, this paper proposes a new model, termed the Mask R-CNN Positioning of Picking Point for Tea Shoots (MR3P-TS) model, for the identification of the contour of each tea shoot and the location of picking points. In this study, a dataset of tender tea shoot images taken in a real, complex scene was constructed. Subsequently, an improved Mask R-CNN model (the MR3P-TS model) was built that extended the mask branch in the network design. By calculating the area of multiple connected domains of the mask, the main part of the shoot was identified. Then, the minimum circumscribed rectangle of the main part is calculated to determine the tea shoot axis, and to finally obtain the position coordinates of the picking point. The MR3P-TS model proposed in this paper achieved an mAP of 0.449 and an 2 value of 0.313 in shoot identification, and achieved a precision of 0.949 and a recall of 0.910 in the localization of the picking points. Compared with the mainstream object detection algorithms YOLOv3 and Faster R-CNN, the MR3P-TS algorithm had a good recognition effect on the overlapping shoots in an unstructured environment, which was stronger in both versatility and robustness. The proposed method can accurately detect and segment tea bud regions in real complex scenes at the pixel level, and provide precise location coordinates of suggested picking points, which should support the further development of automated tea picking machines.
茶是世界上最常见的饮品之一。为降低人工采茶成本，提高茶叶生产竞争力，本文提出一种新模型，即用于茶树嫩梢采摘点定位的Mask R-CNN模型（MR3P-TS模型），用于识别每个茶树嫩梢的轮廓和采摘点位置。本研究构建了一个在真实复杂场景下拍摄的嫩茶树梢图像数据集。随后，构建了一个改进的Mask R-CNN模型（MR3P-TS模型），该模型在网络设计中扩展了掩码分支。通过计算掩码的多个连通域面积，识别出嫩梢的主要部分。然后计算主要部分的最小外接矩形以确定茶树嫩梢轴，最终获得采摘点的位置坐标。本文提出的MR3P-TS模型在嫩梢识别中mAP达到0.449，IoU值达到0.313，在采摘点定位中精度达到0.949,召回率达到0.910。与主流目标检测算法YOLOv3和Faster R-CNN相比，MR3P-TS算法在非结构化环境中对重叠嫩梢具有良好的识别效果，在通用性和鲁棒性方面更强。该方法能够在真实复杂场景下以像素级别准确检测和分割茶芽区域，并提供建议采摘点的精确位置坐标，为自动采茶机的进一步发展提供支持。
