A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

Abstract

The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers' naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

Plant phenotyping relevance

UAV画像と深層学習による作物病害の画像ベース同定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の総説として中心的です。

Code and data availability

This is a survey/review article on deep learning-based plant and crop disease identification from UAV imagery. The supplied blocks contain no authors' phenotype datasets, UAV image collections, analysis code, trained models, or supplements with availability statements. All datasets and models mentioned (e.g., Wiesner-H

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