An advanced deep learning models-based plant disease detection: A review of recent research

Frontiers in Plant Science

Plant Science

Babar Shah¹  Farman Ali²  Asad Ullah³  Muhammad Shoaib⁴  Fayadh Alenezi⁵  Akhtar Ali⁶  Tsanko Gechev⁷  Shaker EI-Sappagh⁸  Tariq Hussain⁹ 

[1] College of Technological Innovation, Zayed University, Dubai, United Arab Emirates;Department of Computer Science and Engineering, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea;Department of Computer Science and Information Technology, Sarhad University of Science and Information Technology, Peshawar, Pakistan;Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan;Department of Computer Science and Information Technology, Sarhad University of Science and Information Technology, Peshawar, Pakistan;Department of Electrical Engineering, College of Engineering, Jouf University, Jouf, Saudi Arabia;Department of Molecular Stress Physiology, Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria;Department of Molecular Stress Physiology, Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria;Department of Plant Physiology and Molecular Biology, University of Plovdiv, Plovdiv, Bulgaria;Faculty of Computer Science and Engineering, Galala University, Suez, Egypt;Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt;School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, China;

关键词: machine learning;    deep learning;    plant disease detection;    image processing;    convolutional neural networks;    performance evaluation;    practical applications;   

DOI  :  10.3389/fpls.2023.1158933

 received in 2023-02-04, accepted in 2023-02-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

【 授权许可】

Unknown   
Copyright © 2023 Shoaib, Shah, EI-Sappagh, Ali, Ullah, Alenezi, Gechev, Hussain and Ali