关键词: 桃树黄叶病, FasterRCNN, 复杂背景, 软性非极大值抑制算法

Abstract: Since the initial symptoms of Peach Tree Yellow Leaf Disease (PTYLD) are not readily apparent, the existing deep learningbased recognition techniques for this disease suffer from issues like inaccurate recognition and limited recognition species. To address this, a recognition model of PTYLD based on FasterRCNN (Regionbased Convolutional Neural Network) is proposed. In order to enhance the recognition accuracy and diversity of PTYLD, RSLoss function is used to replace the crossentropy function in the Region Proposal Network (RPN), and the SoftNMS algorithm is used to replace the original NonMaximum Suppression (NMS) algorithm, so as to improve FasterRCNN. The recognition effect  of the initial and improved version of FasterRCNN models on PTYLD is compared by experiments. The experimental results demonstrate that the improved FasterRCNN achieves a mean average precision (mAP) of 90.56%, recall rate of 94.16%, an accuracy of 92.53% for each category of yellow leaf disease,  and can identify five common PTYLD.

Key words: peach tree yellow leaf disease, FasterRCNN, complex background, SoftNMS