Maize Leaves Disease Detection and Classification using AlexNet Model

Abstract

Crop diseases are a major cause of reduced production and economic losses in the agricultural industry worldwide. To promote human health, it is important to monitor and control these diseases effectively. In the past, image recognition and classification in this field depended on manually designed features created by researchers rather than relying on automated feature extraction methods. The progress made in deep learning has allowed researchers to achieve a significant improvement in the accuracy of detection and classification. Our study utilized a deep-learning framework to classify diseases in maize. Our dataset contains four categories of maize disease. Common rust, Northern Leaf Blight, Cercospora Leaf Spot Grey, and normal. With a 96% accuracy rate, our model proves to be a highly practical solution for safeguarding maize crops against the diseases mentioned earlier, thus providing farmers with a valuable.