Applications of Drone for Crop Disease Detection and Monitoring: A Review

Abstract

Crop diseases are one of the major threats to global food production. The different crop diseases result in significant yield losses, where their effective monitoring and accurate early identification techniques are considered crucial to ensure stable and reliable crop productivity and food security. Restricting and managing the disease's spread and lowering the cost of pesticides require effective plant pathogen monitoring and detection. If not used in the early stages of pathogenesis, traditional techniques such as molecular and serological methods—which are frequently employed for plant disease detection—are frequently ineffective. Conversely, drone-based remote sensing methods are highly successful in quickly detecting plant diseases in their early stages. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Drones have many potential uses in agriculture, including reducing manual labor and increasing productivity. Recent advances in drones and deep learning-based computer vision algorithms to identify crop diseases, providing early warning thereby allowing farmers to prevent costly crop failures and improve food production.

Plant phenotyping relevance

ドローンによる作物病害の検出・モニタリング手法と深層学習画像解析を主題とするレビューであり、植物の病害状態を推定するフェノタイピング手法が中心です。

Code and data availability

This is a review article on drone-based crop disease detection. It cites prior studies (e.g., Zhang et al., Kerkech et al., Stewart et al.) but presents no original phenotyping measurements, datasets, images, code, or models of its own, and contains no data or code availability statements with author URLs.

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