Plant Disease Diagnosis Using Deep Learning Based on Aerial Hyperspectral Images: A Review

Abstract

Plant diseases cause considerable economic loss in the global agricultural industry. A current challenge in the agricultural industry is the development of reliable methods for detecting plant diseases and plant stress. Existing disease detection methods mainly involve manually and visually assessing crops for visible disease indicators. The rapid development of unmanned aerial vehicles (UAVs) and hyperspectral imaging technology has created a vast potential for plant disease detection. UAV-borne hyperspectral remote sensing (HRS) systems with high spectral, spatial, and temporal resolutions have replaced conventional manual inspection methods because they allow for more accurate cost-effective crop analyses and vegetation characteristics. This paper aims to provide an overview of the literature on HRS for disease detection based on deep learning algorithms. Prior articles were collected using the keywords “hyperspectral”, “deep learning”, “UAV”, and “plant disease”. This paper presents basic knowledge of hyperspectral imaging, using UAVs for aerial surveys, and deep learning-based classifiers. Generalizations about workflow and methods were derived from existing studies to explore the feasibility of conducting such research. Results from existing studies demonstrate that deep learning models are more accurate than traditional machine learning algorithms. Finally, further challenges and limitations regarding this topic are addressed.

Plant phenotyping relevance

UAVハイパースペクトル画像と深層学習による植物病害・ストレス検出手法を中心に扱うレビューであり、植物状態の観測・推定手法が主題である。

Code and data availability

This is a review article with no original phenotyping measurements or analysis; the Data Availability Statement says 'Not applicable,' and no author code, datasets, models, or supplements are mentioned. All URLs in the text are cited prior works or generic commercial software.

No evidence-backed public reproduction asset is currently recorded.