A Systematic Review on the Detection and Classification of Plant Diseases Using Machine Learning

Abstract

The occurrence of disease in plants might affect the crop production at a large scale, resulting into decline of the economic growth rate of the country. The disease in plants can be detected and treated at an early stage. Machine learning (ML), deep learning (DL), and computer vision-based techniques could play a pivotal role in detecting and classifying the diseases at an early stage. These approaches have even surpassed the human performance, as well as image processing based traditional approaches in the analysis and classification of plant diseases. Over the years, numerous authors have applied various image processing ML and DL techniques for the diagnosis of different ailments in plants that gives great hope to the farmers and landlords to cure the disease at an early stage. In this study, the authors addressed and evaluated the various currently existing state of art methods and techniques based on machine and deep learning. Besides, the authors have also focused on various limitations and challenges of these approaches that can explore greater possibly of these methods about their usability for disease detection in plants.

Plant phenotyping relevance

植物病害を画像処理・機械学習で検出・分類する手法を体系的に評価したレビューであり、植物の病徴・病害状態を対象とするフェノタイピング手法レビューとして中心的です。

Code and data availability

This is a systematic review of prior plant disease detection studies; it presents no paper-specific phenotype datasets, images, code, or models. The only availability statement is 'Data shall be made available on request,' which does not provide a public asset, and the cited IPM Images resource is a generic external (c

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