Detection and identification of tea leaf diseases based on AX-RetinaNet

Abstract

Abstract The accurate detection and identification of tea leaf diseases are conducive to its precise prevention and control. Convolutional neural network (CNN) can automatically extract the features of diseased tea leaves in the images. However, tea leaf images taken in natural environments have problems, such as complex backgrounds, dense leaves, and large-scale changes. The existing CNNs have low accuracy in detecting and identifying tea leaf diseases. This study proposes an improved RetinaNet target detection and identification network, AX-RetinaNet, which is used for the automatic detection and identification of tea leaf diseases in natural scene images. AX-RetinaNet uses an improved multiscale feature fusion module of the X-module and adds a channel attention module, Attention. The feature fusion module of the X-module obtains feature maps with rich information through multiple fusions of multi-scale features. The attention module assigns a network adaptively optimized weight to each feature map channel so that the network can select more effective features and reduce the interference of redundant features. This study also uses data augmentation methods to solve the problem of insufficient samples. Experimental results show the detection and identification accuracy of AX-RetinaNet for tea leaf diseases in natural scene images is better than the existing target detection and identification networks, such as SSD, RetinaNet, YOLO-v3, YOLO-v4, Centernet, M2det, and EfficientNet. The AX-RetinaNet detection and identification results indicated the mAP value of 93.83% and the F1-score value of 0.954. Compared with the original network, the mAP value, recall value, and identification accuracy increased by nearly 4%, by 4%, and by nearly 1.5%, respectively.