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

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 fie...

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Bibliographic Details
Published in:Cluster computing 2023-04, Vol.26 (2), p.1297-1317
Main Authors: Bouguettaya, Abdelmalek, Zarzour, Hafed, Kechida, Ahmed, Taberkit, Amine Mohammed
Format: Article
Language:English
Subjects:
Agriculture
Algorithms
Artificial intelligence
Computer Communication Networks
Computer Science
Computer vision
Crop diseases
Deep learning
Health services
Machine learning
Operating Systems
Pests
Plant diseases
Processor Architectures
Unmanned aerial vehicles
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Summary: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.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-022-03627-x