AI-Based Plant Disease Detection using Image Processing

Abstract

Crop diseases represent a critical threat to agricultural productivity and global food supply, contributing substantially to yield losses every year. Conventional diagnostic approaches rely on visual inspection by trained personnel, which is inherently slow, inconsistent, and labor-intensive. This work introduces an automated plant leaf disease detection framework built upon image processing and deep learning techniques. The developed system employs convolutional neural networks (CNNs) trained on the widely used PlantVillage benchmark, enabling accurate identification of diseases such as early blight, late blight, and leaf spot. Experimental evaluation confirms strong model performance, with training accuracy surpassing 95% and validation accuracy converging near 91%. Designed for real-time inference, the system offers farmers an affordable and scalable tool aligned with the goals of precision agriculture.