Image Based Areca Nut Disease Detection

Arecanut is an economically important crop that is highly prone to various leaf diseases such as yellow spot, leaf blight, and yellow leaf disease, which affect yield and quality. This project proposes an intelligent disease detection system using Convolutional Neural Networks (CNN) and the YOLO (You Only Look Once) model to identify and classify Arecanut leaf diseases from images. The system categorizes leaves into four classes—healthy, yellow spot, leaf blight, and yellow leaf— based on visual symptoms. By integrating environmental data such as humidity and temperature, the model enhances prediction accuracy. A mobile-friendly interface allows farmers to upload leaf images, receive instant diagnoses, and get treatment recommendations. This solution enables early detection, reduces pesticide usage, and promotes sustainable farming, thereby improving crop health and productivity.