A Unified Lightweight CNN-based Model for Disease Detection and Identification in Corn, Rice, and Wheat

Abstract

Plant diseases are a significant threat to global food security since they directly affect the quality of crops, leading to a decline in agricultural productivity. Several researchers have employed crop-specific deep learning models based on convolutional neural networks (CNN) to identify plant diseases with better accuracy and faster implementation. However, the use of crop-specific models is unreasonable considering the resource-constrained devices and digital literacy rate of farmers. This work proposes a single light-weight CNN model for disease identification in three major crops, namely, Corn, Rice, and Wheat. The proposed model uses convolution layers of variable sizes at the same level to accurately detect the diseases with various sizes of the infected area. The experimentation results reveal that the proposed model outperforms several benchmark CNN models, namely, VGG16, VGG19, ResNet50, ResNet152, ResNet50V2, ResNet152V2, MobileNetV2, DenseNet121, DenseNet201, InceptionV3, and Xception, to achieve an accuracy of 84.4% while using just 387,340 parameters. Moreover, the proposed model validates its efficacy as a multi-functional tool by classifying healthy and infected categories of each crop individually, obtaining accuracies of 99.74%, 82.67%, and 97.5% for Corn, Rice, and Wheat, respectively. The better performance values and light-weight nature of the proposed model make it a viable choice for real-time crop disease detection, even in resource-constrained environments.