Plant Disease Detection and Classification by Deep Learning

Saleem, Muhammad Hammad; Potgieter, Johan; Arif, Khalid Mahmood

Abstract

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.

Citation

Saleem, M. H., Potgieter, J., & Arif, K. M. (2019). Plant Disease Detection and Classification by Deep Learning. Plants, 8(11), 468. https://doi.org/10.3390/plants8110468

• Journal Article Type: Review
• Acceptance Date: Oct 29, 2019
• Online Publication Date: Oct 31, 2019
• Publication Date: Oct 31, 2019
• Deposit Date: Feb 17, 2024
• Publicly Available Date: Feb 21, 2024
• Journal: Plants
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 11
• Pages: 468
• DOI: https://doi.org/10.3390/plants8110468
• Keywords: Plant Science; Ecology; Ecology, Evolution, Behavior and Systematics