Revolutionizing crop disease detection with computational deep learning: a comprehensive review
University of KwaZulu-Natal · North-West University · +9 more institutions
Abstract
Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This…
Citation impact
- FWCI
- 71.22
- Percentile
- 100%
- References
- 59
Authors
4- HNHabiba N. Ngugi
University of KwaZulu-Natal
- AEAbsalom E. EzugwuCorresponding
North-West University
- AAAndronicus A. Akinyelu
University of the Free State
- LALaith AbualigahCorresponding
Al-Ahliyya Amman University, Applied Science Private University, Al al-Bayt University, Lebanese American University, University of Tabuk, Sunway University, Middle East University, Yuan Ze University
Topics & keywords
- Artificial intelligence
- Machine learning
- Convolutional neural network
- Computer science
- Deep learning
- Expansive
- Support vector machine
- Artificial neural network
- Industry, innovation and infrastructure