Plant Disease Detection and Classification by Deep Learning-A Review

• Published in: IEEE access Vol. 9; pp. 56683 - 56698
• Main Authors: Li, Lili, Zhang, Shujuan, Wang, Bin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agriculture; Artificial intelligence; Deep learning; Diseases; Feature extraction; hyperspectral imaging; Image color analysis; Image processing; Image recognition; Imaging techniques; Insects; Machine learning; Natural language processing; Pests; Plant diseases; plant leaf disease detection; Plants (biology); Recognition; small sample; Video; visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3069646

Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved.