AI-Driven Crop Disease Prediction System

Abstract

The rapid advancement of agricultural technology has created a pressing demand for intelligent systems that can safeguard crop health and ensure sustainable farming practices. This paper presents an AI-Driven Crop Disease Prediction System, a web-based platform that assists farmers in the early detection and prevention of plant diseases through data-driven insights. The system integrates image processing and machine learning techniques to analyze leaf images, identify disease symptoms, and suggest suitable remedies. Convolutional Neural Networks (CNNs) are utilized for image classification, while data analytics modules evaluate environmental parameters such as temperature, humidity, and soil conditions to improve prediction accuracy. The backend framework employs Python Flask with TensorFlow integration and a scalable MySQL database for efficient data storage and retrieval. The proposed system minimizes crop loss, enhances yield quality, and supports timely decision-making for farmers. Future enhancements will include integration of IoT-based sensors, multilingual chatbot support, and a mobile application for real-time field monitoring. This work demonstrates how artificial intelligence can transform traditional agriculture into a smart, predictive, and resilient ecosystem.

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