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A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images
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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.
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received the master’s and Ph.D. degrees in telecommunications from Badji Mokhtar University, Annaba, Algeria, in 2011 and 2017, respectively. He was a member of the “Embedded and Detection” Advanced Systems Division, Laboratoire d’Etude et de Recherche en Instrumentation et en Communication d’Annaba (LERICA Laboratory), Annaba, from 2009 to 2018. He is currently a Senior Researcher with the Research Center in Industrial Technologies (CRTI), Chéraga, Algeria. His current research interests include deep learning, computer vision, intelligent embedded systems, and Unmanned Aerial Vehicles (UAVs).
received the Ph.D. degree in computer science from Annaba University, Annaba, Algeria, in 2013. He is currently an Associate Professor of computer science with the University of Souk Ahras, Souk-Ahras, Algeria. He has published several research articles in international journals and conferences of high repute, including IEEE, Springer, Elsevier, Wiley, ACM, Taylor and Francis, IGI Global, and Inderscience. His research focuses on deep learning, artificial intelligence, and educational technology.
received his M.S. degree in electronics from the Polytechnic Milatary School, Algiers, Algeria, in 2003 and the Ph.D. degree in electronics from Saad Dahleb Blida 1 University, Blida, Algeria, in 2016. He is currently a Senior Researcher with Research Center in Industrial Technologies (CRTI), Algiers. His research interest include signal processing, image analysis, embedded systems for drones, and non-destructive evaluation by ultrasound.
was born in Tlemcen, Algeria, in 1988. He received the master’s degree in electronic instrumentation and the Ph.D. degree from the University of Tlemcen, Tlemcen, in 2012 and 2018, respectively. In 2010, he became an Electronic Engineer at the University of Tlemcen. He was a member of the Research Unit of Materials and Renewable Energy Research (URMER) and a part-time Teacher with the University of Tlemcen from 2013 to 2019. He is currently a Senior Researcher with the Research Center in Industrial Technologies (CRTI), Chéraga, Algiers. His research interests included strain silicon technology, MOSFET transistors, and nanotechnology. His actual research interests include artificial intelligence, intelligent embedded systems, unmanned aerial vehicle, and computer vision.
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