Research Area:  Machine Learning

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.

Keywords:  
plant disease
deep learning
convolutional neural networks (CNN)

Author(s) Name:  Muhammad Hammad Saleem,Johan Potgieter and Khalid Mahmood Arif

Publisher name:  MDPI

DOI:  10.3390/plants8110468

Volume Information:   Volume 8,Issue 11

Paper Link:   https://www.mdpi.com/2223-7747/8/11/468