CNN-Based Framework for Maize leaf Disease Detection and Crop Monitoring

Abstract: Maize is an agricultural crop but its productivity is significantly affected by diseases that are often hard to notice at an early stage. Traditional ways of identifying diseases depend on people looking at the plants, which can take a lot of time and needs someone who knows a lot about plants. This paper shows a Smart Maize Crop Monitoring and Disease Detection System that uses machine learning and image processing to help with identifying and managing diseases on maize leaves. The system uses a Convolutional Neural Network to sort images of maize leaves into four groups: Healthy, Common Rust, Blight and Gray Leaf Spot. The images are changed in size. Made different in ways to help the model work better when the pictures are in different positions or under different conditions. The model that was trained is part of a web app built with Flask so people can send images of maize leaves. Get predictions about the disease through an easy way to use. Also the system gives information about how to treat the disease advice on what stage the crop's in and tips on watering the plants. Testing showed that the system could correctly say what the disease was 92% of the time which shows that this way of doing things can be a tool, for growing maize. The system is meant to help find diseases stop people from using the wrong treatments and make growing crops better.

Keywords: Maize Disease Detection, Convolutional Neural Network (CNN), Image Processing, Machine Learning, Smart Agriculture, Flask Web Application.