An advanced deep learning models-based plant disease detection: A review of recent research
By Muhammad Ali Shoaib, Babar Shah, Shaker EI-Sappagh, Akhtar Ali, Asad Ullah, Fayadh S. Alenezi, T. Gechev, Tariq Hussain, F. Ali. Frontiers in Plant Science, 2023
476 citations recorded by the source corpus.
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.
Subfields: Analytical Chemistry, Ecology, Plant Science
View Paper: https://www.semanticscholar.org/paper/8c5ea98097aaff87011570e5f86ca1a004135a34
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In Analytical Chemistry
In Plant Science
Citations in the corpus
- Plant disease detection and classification techniques: a comparative study of the performances (2024, background): “The CNN learns channels that are activated after it classifies a particular highlight at some spatial positioning information [19, 21, 24, 29].”