AI DRIVEN CROP DISEASE PREDICTION AND MANAGEMENT SYSTEM

Abstract: Agriculture is an important part of our life and provides food and income to many people. Crop diseases are a major problem for farmers because they can damage plants, reduce crop quality, and decrease production. Early identification of crop diseases is important to prevent further damage. This project presents an AI-Driven Crop Disease Prediction and Management System that identifies possible crop diseases using crop leaf images. In this system, leaf images are collected and processed to extract important features such as colour, texture, and shape. The K-Nearest Neighbors (KNN) algorithm is used to predict the disease by comparing the features of a new leaf image with images available in the dataset. The system also provides basic information about the predicted disease and simple management suggestions. Its performance can be evaluated using accuracy, precision, recall, and F1-score. The proposed system provides a simple and quick method for initial crop disease identification. It can help farmers save time, take early action, and reduce crop damage. Overall, the project demonstrates the usefulness of Artificial Intelligence and Machine Learning in agriculture for better crop disease management.

Keywords: Artificial Intelligence, Machine Learning, Crop Disease Prediction, KNN, Leaf Image Analysis.