Plant Disease Detection

Abstract

Plant diseases significantly impact agricultural productivity and food security, making early and accurate disease detection crucial for effective crop management. Traditional disease identification methods rely on manual inspection, which is time-consuming, labor-intensive, and prone to errors. Recent advancements in artificial intelligence (AI) and computer vision have enabled automated plant disease detection using deep learning techniques. This paper explores various machine learning approaches, including convolutional neural networks (CNNs), to classify and detect plant diseases from leaf images. The PlantVillage dataset of diseased and healthy plant images is used to train and evaluate the model. The proposed system achieves high accuracy in distinguishing different plant diseases, demonstrating its potential for real-time application in precision agriculture. By integrating AI-driven plant disease detection with smartphone applications or IoT-based monitoring systems, farmers can receive instant alerts and take timely corrective actions, ultimately reducing crop losses and improving yield quality. Key Words: artificial intelligence, convolutional neural networks, computer vision, internet of things(IoT), deep learning, machine learning

Plant phenotyping relevance

葉画像から植物病害を分類・検出する画像ベースの植物状態推定法が研究の中心であり、モデルの訓練・評価も行っているため含める。

Code and data availability

The paper trains a CNN on the public PlantVillage dataset, but no author-specific dataset link, code repository, trained model checkpoint, or supplement with a public URL is provided anywhere in the supplied blocks. PlantVillage is a generic third-party benchmark, not a paper-specific asset, and no availability/deposit

No evidence-backed public reproduction asset is currently recorded.