What is it about?
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.
Photo by Julian Ebert on Unsplash
Why is it important?
Plant disease identification using deep learning and UAVs provides a more efficient and accurate solution compared to traditional methods. With the ability to accurately identify diseases at early stages and covering larger areas in less time, precision agriculture using these technologies can help farmers to improve yields, reduce crop losses, and increase profitability.
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This page is a summary of: A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images, Cluster Computing, August 2022, Springer Science + Business Media,
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