A descriptive analysis of plant leaf disease detection using machine learning and deep learning models: a systematic review

Abstract

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, involving structured search, screening, and analysis of studies published between 2020 and 2024. We have proposed a taxonomy of PLDD methods that will be useful for experts and researchers working in this exciting research area. The review thoroughly examines techniques for both segmentation and classification of the PLDD workflow. In addition, we examine several public and private datasets that are accessible to study plant diseases and highlight their significance in developing accurate diagnostic models. The paper also presented multiple performance assessment criteria that researchers can use to evaluate PLDD methods at present and in the future. The study also discusses the current challenges in plant leaf disease classification and offers essential insights about upcoming developments and potential enhancements. The research findings from this study provide essential knowledge that helps experts and researchers to develop automated systems to detect and classify plant leaf diseases effectively.

Plant phenotyping relevance

植物葉の病徴を画像から検出・分類する手法を対象とした系統的レビューであり、セグメンテーション、分類、データセット、評価指標を中心に扱うため、植物フェノタイピング手法レビューに該当する。

Code and data availability

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