Plant Disease Detection and Classification by Deep Learning

By Muhammad Hammad Saleem, Johan Potgieter, Khalid Mahmood Arif. Plants, 2019

720 citations recorded by the source corpus.

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.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/b5e70b852d9a54319a4f268df1e676b6767a0aa3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Shujuan Zhang
2. Lili Li
3. Bin Wang
4. Wubetu Barud Demilie

In Plant Science

1. Shujuan Zhang
2. Lili Li
3. Bin Wang
4. Wubetu Barud Demilie

Citations in the corpus

• Plant Disease Detection and Classification by Deep Learning—A Review (2021, background): “The article [16] presented many developed/modified DL architectures implemented to detect and classify plant diseases.”