Lightweight convolutional neural network model for field wheat ear disease identification

Abstract

Manual diagnosis of crop diseases has high cost and low efficiency and has become increasingly unsuitable for the needs of modern agricultural production. This study designed a lightweight convolutional neural network (CNN) model called SimpleNet for the automatic identification of wheat ear diseases, such as glume blotch and scab, in natural scene images taken in the field. SimpleNet was constructed using convolution and inverted residual blocks. In this study, Convolutional Block Attention Module (CBAM), which combines spatial attention mechanism and channel attention mechanism, was introduced into inverted residual blocks to improve the representation ability of the model for disease features so that the model pays attention to important features, suppresses unnecessary features, and reduces the influence of complex backgrounds in the images. In addition, this study designed a feature fusion module to concatenate the down-sampled feature maps output by inverted residual blocks and the average pooling features of the feature maps that input inverted residual blocks to realize the fusion between features of different depths to reduce the loss of the detailed features of wheat ear diseases caused by the networks in the down-sampling process and solve the disappearance of disease features in the process of image feature extraction. Experimental results show that the proposed SimpleNet model achieved an identification accuracy of 94.1% on the test data set, which is higher than that of classic CNN models, such as VGG16, ResNet50, and AlexNet, and lightweight CNN models, such as MobileNet V1, V2, and V3. SimpleNet has only 2.13 M parameters, which is less than those of MobileNet V1, V2, and V3-Large. The designed model can be used for the automatic identification of wheat ear diseases on the mobile terminal.