Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review

Abstract

A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.

Plant phenotyping relevance

植物葉の病徴を画像処理・機械学習で検出・分類する手法を中心に扱うレビューであり、植物の病害状態を推定するフェノタイピング方法のレビューに該当します。

Code and data availability

This is a review/survey article on crop leaf disease detection. It describes methods and datasets used by cited prior works (e.g., PlantVillage) but presents no authors' own phenotype datasets, images, code, models, or supplements with availability statements. No paper-specific public assets exist.

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