Plant Disease Detection and Classification by Deep Learning—A Review
By Lili Li, Shujuan Zhang, Bin Wang. IEEE Access, 2021
720 citations recorded by the source corpus.
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.
Subfields: Analytical Chemistry, Plant Science
View Paper: https://www.semanticscholar.org/paper/49eaad7ef73c51f896f2e05c670747a750281658
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In Analytical Chemistry
In Plant Science
Citations in the corpus
- Plant disease detection and classification techniques: a comparative study of the performances (2024, methodology): “The application of DL approaches to detect and classify plant diseases can avoid the disadvantages of artificial selection of disease spot features, making plant disease feature extraction more objective, and increasing research performances and technology transformation speed [167].”