AI-Enabled Crop Disease Detection

Abstract

Agriculture plays a crucial role in sustaining human life, and plant health directly impacts food security and economic stability. However, plant diseases remain a persistent challenge, leading to significant crop losses and reduced yields. Traditional methods of disease detection, which rely heavily on visual inspection by experts, are often time-consuming, subjective, and inaccessible to many farmers. To address this challenge, this project presents an intelligent, automated system for plant disease identificationandpesticiderecommendationusing ConvolutionalNeuralNetworks(CNNs). Theproposed systemleveragesadeep learning-based CNN model trained on a comprehensive dataset of plant leaf images to accurately classify various plant diseases. Upon identification, the system provides targeted recommendations for organic pesticides to manage and mitigate the diagnosed disease effectively. The application is deployed as a user-friendly web platform, enabling users to upload plant images, receive instant diagnosis, and access curated pesticide suggestions. Through extensive testing, the CNN model achieved 95% accuracy, demonstrating its effectiveness in recognizing diverse plant diseases. The integration of organic pesticide data supports environmentallysustainablefarmingpractices. Usabilitytestswithrealusers, includingfarmersand agriculturalstudents, validated the system's ease of use and practical value in real-world scenarios.

Plant phenotyping relevance

植物葉画像から病害状態をCNNで推定する手法とWebシステムが研究の中心であり、植物病害の表現型状態を直接評価しているため。

Code and data availability

The paper describes a CNN-based plant disease detection system using a labeled plant leaf image dataset, but provides no public dataset URL, code repository, model checkpoint, or supplement with availability language. The dataset is described only generically ('a large collection of labeled plant leaf images'), and no

No evidence-backed public reproduction asset is currently recorded.