A Lightweight Convolutional Neural Network-Based Method for Cotton Mosaic Disease Identification

Abstract

Common cotton diseases like cotton leaf bright, leaf curl, and fusarium wilt have a direct impact on the final production and quality of cotton. the reduction of economic losses, and the promotion of the cotton industry may all be achieved via prompt disease prevention and accurate diagnosis. Correct disease detection and treatment may stop the spread of illnesses, minimize financial losses, and guarantee the healthy growth of the cotton sector. To address the issue of the absence of lightweight models for correctly recognizing cotton illnesses in the environment, we propose the CSGNet model for resource-constrained mobile devices, which is based on a tiny CNN architecture (ShuffleNet V2). Shuffle Attention (SA) is added to the model to improve disease feature extraction under complex background conditions. Additionally, GAUSSIAN ERROR LINEAR UNITS (GELU) are chosen to help the propose model better mine pertinent features, and the Adam optimization algorithm is used to enhance the model's generalization capabilities. The public data set of cotton leaf disease photos with a natural backdrop was utilized for experimental simulation training and performance testing. The identification accuracy was 99.1%, which was 1.5 percentage points better than previously, and the model size was just 4.96 MB. In comparison to other classification network models like MobileNet-V3, ResNet-50, and DenseNet-121, it not only achieves a greater recognition accuracy but also has a quicker convergence process and fewer parameters. CSGNet can achieve intelligent detection of cotton leaf diseases based on mobile end, and it complies with the practical application requirements. Even mobile devices with little computing power may use it effectively.