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. 2023;26(2):1297-1317.
doi: 10.1007/s10586-022-03627-x. Epub 2022 Aug 3.

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

Abdelmalek Bouguettaya #  1 , Hafed Zarzour #  2 , Ahmed Kechida  1 , Amine Mohammed Taberkit  1
Affiliations

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

Abdelmalek Bouguettaya et al. Cluster Comput. 2023.

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.

Keywords: Computer vision; Convolutional neural network; Deep learning; Plant disease; Precision agriculture; Unmanned Aerial Vehicles.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Search strategy flowchart
Fig. 2
Fig. 2
Different agricultural UAV types
Fig. 3
Fig. 3
Deep learning-based plant disease identification workflow
See this image and copyright information in PMC

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