Plant Disease Detection and Classification: A Systematic Literature Review.

Abstract

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.

Plant phenotyping relevance

植物病害の画像ベース検出・分類手法を体系的にレビューしており、植物の病徴・状態を推定するフェノタイピング手法が中心です。

Code and data availability

This is a systematic literature review of plant disease detection methods. It describes datasets used by reviewed studies (e.g., PlantVillage, PlantDoc) but these are cited prior-work resources, not assets produced by this paper. No author code, models, or paper-specific phenotype datasets are reported with public URLs

No evidence-backed public reproduction asset is currently recorded.