PLANT DISEASE PREDICTION USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Plant diseases significantly impact global agriculture, causing economic losses, reduced yields, and food insecurity. Early and precise detection is crucial for effective disease management and sustainable farming. This research introduces a deep learning-based plant disease prediction system using a Convolutional Neural Network (CNN), a powerful image processing algorithm. The CNN model, trained on leaf image datasets, enables automated, real-time disease diagnosis with 99.20% accuracy. By extracting intricate features, it enhances predictive precision, aiding farmers and agricultural experts in early detection, minimizing crop damage, and reducing dependence on chemical treatments, thereby promoting healthier and more sustainable crop production.