A Real-Time Common Rust Maize Leaf Disease Severity Identifications and Pesticide Dose Recommendation Using Deep Neural Network

Abstract Maize is one of the most widely grown crops in Ethiopia, although common rust maize disease (CRMD) is becoming a serious problem. Conventional CRMD detection and treatment methods are time-consuming, expensive and ineffective. To solve these problems, we propose a real-time deep learning model that provides disease detection and pesticide dosing recommendations. In the model development process, we collect 8000 maize-leaf images experimentally with permission obtained from Haramaya University and apply image pre-processing such as image equalization, augmentation, noise removal, and image enhancement to improve the performance of the proposed model. We then jointly apply batch normalization, dropout, and early stopping during training the proposed algorithm to reduce the overfitting problem. Next, we generate an optimal model that recognizes CRMD and classifies it accurately based on its severity levels. To evaluate the proposed techniques, we compare Resnet50 with other state-of-the-art algorithms, such as VGG19, VGG16, and AlexNet algorithms with similar parameters. The performance of ResNet50 is more than 2% better than that of other algorithms in terms of accuracy. The proposed technique provides promising results for the classification of CRMD and pesticide dosage recommendations based on the severity of the disease type. This study also demonstrates the potential of Resnet50 models for improving maize disease management.