PLANT DISEASE ANALYSIS AND PREDICTION USING DEEP LEARNING

Abstract

At present, The agricultural sector plays a critical role in ensuring food security and supporting rural livelihoods, but plant diseases remain a major obstacle to achieving high crop yield and quality. Early and accurate detection of these diseases is essential for timely treatment and prevention of further damage. In many cases, farmers depend on manual observation or experience-based judgement, which can lead to misdiagnosis, delayed action, and unnecessary pesticide usage. This project introduces a plant disease detection system that applies Machine Learning and image classification techniques to identify diseases from leaf images. The system is built using TensorFlow, with a deep learning model trained on a diverse dataset containing images of healthy and diseased leaves from multiple plant species. By learning to recognize patterns in texture, shape and color variations, the model is capable of accurately classifying plant health conditions. The system is deplyed through a user-friendly desktop website where users can upload images of plant leaves. Once uploaded, the image is processed by the TensorFlow model, and the system outputs the detected disease along with suggested preventive or corrective measures. This eliminates the need of constant expert consultation and ensures quick, data-driven diagonsis. By combining image analysis with an accessible web interface, the solution enables faster and more reliable plant disease detection. It promotes targeted pesticide application, minimizes crop loss, and supports sustainable farming practices, ultimately contributing to improved agricultural productivity and economic stability for farmers.