Plant Disease Detection Using Deep Learning

Abstract

The early and accurate detection of plant diseases plays a pivotal role in enhancing crop health and ensuring food security in modern agriculture. Traditional disease diagnosis techniques often rely on visual inspection by experts, which may be subjective, time-consuming, and inaccessible to farmers in remote regions. To overcome these limitations, this project introduces an intelligent plant disease detection system that leverages deep learning and image analysis to identify symptoms directly from leaf images. The proposed methodology begins with the collection of a curated dataset comprising various plant species exhibiting both healthy and diseased conditions. The images undergo preprocessing to enhance quality and ensure consistency, followed by feature extraction using Convolutional Neural Networks (CNNs). Transfer learning is applied to improve model generalization and reduce the training time by utilizing pre-trained models. The system is integrated into a Flask-based web application, enabling users to upload leaf images and receive instant disease diagnoses along with treatment suggestions and suitable fertilizers. Evaluation of the model has shown high classification accuracy across multiple disease classes, affirming its potential to support precision agriculture. The solution is designed to be lightweight, user-friendly, and deployable in real-world agricultural settings, aiming to assist farmers with timely and informed interventions to mitigate crop loss and promote sustainable farming practices.

Plant phenotyping relevance

葉画像から植物の病徴を深層学習で抽出・分類するシステム開発が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。

Code and data availability

The paper's only qualifying paper-specific asset is the PlantVillage leaf-image dataset, which the authors explicitly state they used for training and evaluation. No author analysis code, trained model checkpoints, or supplementary data deposit is mentioned with an availability statement or URL.