GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

Abstract

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases.

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

“…The network structure of CBAM not only enables channel and spatial attention at the same time as saving parameters and computational power, but also has the same size input and output guarantee such that it becomes a plug‐and‐play module that can be integrated into any position of the network architecture. Lin et al 35 . used the CBAM module to assign more weight to disease information in grape leaf disease images, as well as less weight to background and noise, thus improving the ability to extract disease information.…”

“…The network structure of CBAM not only enables channel and spatial attention at the same time as saving parameters and computational power, but also has the same size input and output guarantee such that it becomes a plug-and-play module that can be integrated into any position of the network architecture. Lin et al 35 used the CBAM module to assign more weight to disease information in grape leaf disease images, as well as less weight to background and noise, thus improving the ability to extract disease information. Zhang et al 36 introduced the CBAM module in the YOLOXs network to ensure that the network can make full use of the valuable feature information of channel and spatial dimensions, thus improving the model's ability to identify key growth stages of lettuce.…”

Improved YOLOX-Tiny network for detection of tobacco brown spot disease

“…The traditional crop disease detection method mainly relies on hand-designed features. The detection efficiency and detection accuracy of this method are low, which can no longer meet the needs of modern agriculture ( Singh and Misra, 2017 ; Lin et al., 2022b ).…”

JujubeNet: A high-precision lightweight jujube surface defect classification network with an attention mechanism

“…Several scholars have applied it to fruit sorting tasks and achieved successful results ( Yin et al., 2022 ; Lin et al., 2022 ). Some researchers have also carried out relevant studies in the field of surface defect identification of jujube, which will be elaborated in the section “Related Work”.…”