Recognition of peach tree yellow leaf disease under complex background based on improved Faster-RCNN
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.
peach tree yellow leaf diseaseFaster-RCNNcomplex backgroundSoft-NMS
万方数据
维普
