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

• 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
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-022-03627-x

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.