Plant Disease Detection and Classification by Deep Learning.

Abstract

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.

Plant phenotyping relevance

植物病害症状の可視化・検出・分類に用いる深層学習手法を体系的に扱うレビューであり、植物の病害状態を画像から推定する方法が中心です。

Code and data availability

This is a review article summarizing prior deep-learning plant disease detection studies; it presents no original phenotyping measurements, datasets, images, models, or analysis code of its own. The PlantVillage dataset and all cited architectures belong to prior works, and no author code or data availability statement

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