Techniques of deep learning and image processing in plant leaf disease detection: a review

Abstract

Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.

Plant phenotyping relevance

植物葉の画像から病害の発生・重症度を推定する画像処理・機械学習手法を中心に扱うレビューであり、植物表現型計測手法のレビューに該当する。

Code and data availability

This is a review article tabulating datasets and classifiers from prior studies; it reports no original phenotyping measurements, no author-collected image dataset with availability statement, and no analysis code, models, or supplements. All datasets mentioned (Plant Village, Flavia, PlantClef2015, etc.) belong to the

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