Frontiers in Plant Science (Sep 2023)
Corrigendum: An advanced deep learning models-based plant disease detection: a review of recent research
- Muhammad Shoaib,
- Muhammad Shoaib,
- Babar Shah,
- Shaker EI-Sappagh,
- Shaker EI-Sappagh,
- Akhtar Ali,
- Asad Ullah,
- Fayadh Alenezi,
- Tsanko Gechev,
- Tsanko Gechev,
- Tariq Hussain,
- Farman Ali
Affiliations
- Muhammad Shoaib
- Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan
- Muhammad Shoaib
- Department of Computer Science and Information Technology, Sarhad University of Science and Information Technology, Peshawar, Pakistan
- Babar Shah
- College of Technological Innovation, Zayed University, Dubai, United Arab Emirates
- Shaker EI-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez, Egypt
- Shaker EI-Sappagh
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt
- Akhtar Ali
- Department of Molecular Stress Physiology, Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
- Asad Ullah
- Department of Computer Science and Information Technology, Sarhad University of Science and Information Technology, Peshawar, Pakistan
- Fayadh Alenezi
- Department of Electrical Engineering, College of Engineering, Jouf University, Jouf, Saudi Arabia
- Tsanko Gechev
- Department of Molecular Stress Physiology, Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
- Tsanko Gechev
- Department of Plant Physiology and Molecular Biology, University of Plovdiv, Plovdiv, Bulgaria
- Tariq Hussain
- School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, China
- Farman Ali
- 0Department of Computer Science and Engineering, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea
- DOI
- https://doi.org/10.3389/fpls.2023.1282443
- Journal volume & issue
-
Vol. 14
Abstract
No abstracts available.Keywords
- machine learning
- deep learning
- plant disease detection
- image processing
- convolutional neural networks
- performance evaluation