A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends
Author:
Affiliation:
1. School of Digital Science, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
2. Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia
Funder
Universiti Brunei Darussalam and the Ministry of Education (MoE) Brunei Darussalam
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10147225.pdf?arnumber=10147225
Reference176 articles.
1. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case
2. Recognition and counting of wheat mites in wheat fields by a three-step deep learning method
3. Automated recognition of optical image based potato leaf blight diseases using deep learning
4. Automatic vegetable disease identification approach using individual lesion features
5. Modified ride-NN optimizer for the IoT based plant disease detection
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