AI-Plant Disease Prediction & Cure Recommendation Model

Abstract

Plant diseases pose a serious challenge to global food security as they reduce crop yield, quality, and productivity. The timely detection of plant diseases is crucial to prevent large-scale agricultural losses. In rural and underdeveloped regions, farmers lack access to agricultural experts, leading to incorrect diagnosis and ineffective treatment. This research focuses on developing an AI-powered plant disease detection and cure recommendation system using Convolutional Neural Networks (CNNs). The system processes leaf images, detects diseases, and provides tailored treatment recommendations. Experimental results show that the proposed model achieves high accuracy and demonstrates its applicability for real-world deployment through web and mobile platforms. Plant diseases pose a serious challenge to global food security as they reduce crop yield, quality, and productivity. The timely detection of plant diseases is crucial to prevent large-scale agricultural losses. In rural and underdeveloped regions, farmers lack access to agricultural experts, leading to incorrect diagnosis and ineffective treatment. This research focuses on developing an AI-powered plant disease detection and cure recommendation system using Convolutional Neural Networks (CNNs). The system processes leaf images, detects diseases, and provides tailored treatment recommendations. Experimental results show that the proposed model achieves high accuracy and demonstrates its applicability for real-world deployment through web and mobile platforms.

Plant phenotyping relevance

葉画像から植物病害を検出するCNN手法の開発が研究の中心であり、植物の病徴・病害状態を画像から推定するため、植物フェノタイピング手法として収録する。

Code and data availability

The paper describes a CNN-based plant disease detection system using the PlantVillage dataset, but provides no authors' public code, model checkpoints, or dataset deposit. PlantVillage is cited prior work, not a paper-specific asset.

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