Plant Leaf Disease Detection System Using CNN

Abstract

Abstract: Fruit and vegetable crops experience diminished agricultural production output because of pests along with diseases that rank as major factors worldwide. The correct identification of these issues becomes vital because delayed detection results in decreased quantity and quality of yields which then causes problems for food supply networks and regional economic stability. Farmers traditionally monitor plant diseases through individual observation assisted by expert consultations because they search for clear indicators of leaf damage including discolorations or spotted lesions or deteriors on leaves. The approach fails to meet standards because it often produces unsuitable results and unreliable human involvement. The proposed deep learning-based Disease Recognition Model employs Convolutional Neural Networks (CNNs) for processing leaf disease diagnosis within apple and corn and tomato and potato crops. The system enables automatic disease detection of leaves through image processing which delivers precise results. The training data consists of multiple leaf images which come from healthy subjects and disease-infected samples enabling precise identification of various diseases. The tool aims to become an affordable solution that supports farmers and agronomists and policymakers for better crop health management and minimal chemical usage while ensuring sustainable farming practices . Key Words: The system employs key terms including Leaf disease detection, plant health monitoring, CNN classification, fruit and vegetable crops, automated diagnosis, early disease intervention, sustainable agriculture, precision farming.

Plant phenotyping relevance

葉画像から植物病害状態をCNNで推定する画像ベースの植物フェノタイピング手法が研究の中心であるため。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.