An AI-Driven Framework for Crop Disease Detection and Management Using Deep Learning and Leaf Image Analysis

Abstract

Abstract Agriculture is a key sector that supports the economy and food supply. However, plant diseases reduce crop yield and quality, creating major challenges for farmers. Early detection of plant diseases is important to prevent large agricultural losses. This project presents an AI-Driven Crop Disease Prediction and Management System that uses machine learning and image processing techniques to detect diseases in crop leaves. The system analyzes leaf images to identify disease patterns and provide appropriate management suggestions. By using artificial intelligence, farmers can easily detect diseases without relying on manual inspection. The system is designed to be efficient, scalable, and accessible through mobile applications, making it a useful tool for modern smart farming. Keywords : Artificial Intelligence, Deep Learning, Crop Disease Detection, Image Processing, Convolutional Neural Networks (CNN), Plant Disease Classification, Smart Agriculture, Precision Farming.

Plant phenotyping relevance

葉画像から植物病害パターンを抽出・分類する画像解析手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として収録する。

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