Enhancing plant disease detection using machine learning approaches for improved agricultural productivity

Abstract

India's agricultural sector faces persistent challenges due to the prevalence of plant diseases, which severely impact crop quality and productivity, exacerbating the ongoing food supply crisis. Traditional methods of diagnosing plant diseases are often time-consuming, labor-intensive, and prone to inaccuracies, making it difficult for farmers to implement timely interventions. To address these issues, a forward-looking strategy utilizing artificial intelligence (AI) and machine learning (ML) has been proposed, aiming to revolutionize disease detection and management in agriculture. This involves the development of a comprehensive novel dataset named Leafsnap, which is uniquely sourced directly from real-world agricultural environments. This dataset ensures the authenticity and relevance of the data, reflecting the actual conditions faced by farmers. Leafsnap serves as a foundation for training advanced algorithmic models designed to identify patterns and symptoms indicative of various leaf diseases. The proposed system leverages a combination of cutting-edge AI and ML techniques, including convolutional neural networks (CNN), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost) and logistic regression (LR). By integrating these advanced computational techniques into agricultural practices, the system aims to provide farmers with an efficient, reliable, and scalable solution for disease management. The ultimate goal is to foster agricultural sustainability by minimizing crop losses due to disease, thereby bolstering food security and supporting the livelihoods of farmers across India.

Plant phenotyping relevance

実環境由来の葉画像データセット開発と、植物葉の病徴を画像から検出する機械学習手法が研究の中心であり、植物病害状態のフェノタイピングに該当する。

Code and data availability

The paper describes a curated leaf-disease image dataset (called Leafsnap/CropLeaf Insights) and ML models, but provides no public deposit, availability statement, or URL for the dataset, images, code, or trained models. No paper-specific public asset is actionable.

No evidence-backed public reproduction asset is currently recorded.