PLANT LEAF DISEASE DETECTION WITH DEEP LEARNING

Abstract

Deep learning constitutes a fundamental component of artificial intelligence.Recently, it has gained wide attention from educational and industrial sectors due to its ability for automatic learning and feature extraction.Additionally, it has become a key area of research in agricultural plant protection, particularly in recognizing plant diseases and assessing pest populations.Using deep learning for disease recognition helps overcome the limitations of manually selecting diseaserelated features, making the extraction of plant disease characteristics more objective.This enhances analysis efficiency and accelerates technology advancements.It delves into the current trends and obstacles encountered in the realm of detecting plant leaf diseases through the utilization of deep learning methodologies and sophisticated imaging technologies.Our aim is for this study to offer a beneficial point of reference for researchers engaged in the exploration of plant disease detection and pest control.Furthermore, we highlight certain challenges and concerns that necessitate resolution within this domain.

Plant phenotyping relevance

植物葉の病徴・病害状態を画像と深層学習で検出する方法論レビューであり、植物表現型の取得・推定が中心です。

Code and data availability

This is a review/survey article on plant leaf disease detection with deep learning. It presents no original phenotyping measurements, datasets, images, code, or models of its own; all cited works are prior publications, and no author-deposited assets or availability statements appear.

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