Advanced deep transfer learning techniques for efficient detection of cotton plant diseases
During experimentation, it is found that the EfficientNetB3 model outperforms in accuracy, loss, as well as root mean square error by obtaining 99.96%, 0.149, and 0.386 respectively. However, other models also show the good performance in terms of precision, recall, and F1 score, with high scores close to 0.98 or 1.00, except for VGG19. The findings of the paper emphasize the prospective of deep transfer learning as a viable technique for cotton plant disease diagnosis by providing a cost-effective and efficient solution for crop disease monitoring and management. This strategy can also help to improve agricultural practices by ensuring sustainable cotton farming and increased crop output.
Authors
- SeongKi Kim (ORCID: https://orcid.org/0000-0002-2664-3632)
- Prakhar Sharma (ORCID: https://orcid.org/0009-0008-6681-4310)
- Prashant Johri (ORCID: https://orcid.org/0000-0001-8771-5700)
- Jana Shafi (ORCID: https://orcid.org/0000-0001-6859-670X)
- Yogesh Kumar (ORCID: https://orcid.org/0000-0001-7194-5931)
- Barkha Kakkar
- Muhammad Fazal Ijaz (ORCID: https://orcid.org/0000-0002-9381-5801)
- Kumud Dixit
Institutions
- Prince Sattam Bin Abdulaziz University (SA)
- Chosun University (KR)
- Galgotias University (IN)
- Institute of Management Technology (IN)
- Melbourne School of Theology (AU)
- Pandit Deendayal Energy University (IN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2024-12-19
- DOI
- https://doi.org/10.3389/fpls.2024.1441117
- Citations
- 26
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 15.59
Funders
- Prince Sattam bin Abdulaziz University
- Ministry of Science and ICT, South Korea