Arecanut Yellow Leaf Disease Detection Using CNN

Arecanut cultivation plays an important role in the agricultural economy of India, but crop productivity is severely affected by diseases such as Yellow Leaf Disease (YLD). Early identification of this disease is challenging due to gradual symptom development and reliance on manual inspection. This paper presents the implementation of an automated image-based detection system for identifying Yellow Leaf Disease in arecanut plants using Convolutional Neural Networks (CNN). The proposed system processes leaf images through preprocessing, feature extraction, and deep learning–based classification to distinguish between healthy and diseased leaves. The model is trained and evaluated using standard performance metrics, and the final system is deployed through a simple web interface for real-time usage. Experimental results demonstrate that the system provides reliable accuracy and supports early disease detection, helping farmers take timely preventive measures.