Open Access
| Issue |
SHS Web Conf.
Volume 216, 2025
International Conference on the Impact of Artificial Intelligence on Traditional Economic Sectors (ICIAITES 2025)
|
|
|---|---|---|
| Article Number | 01051 | |
| Number of page(s) | 6 | |
| Section | Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development | |
| DOI | https://doi.org/10.1051/shsconf/202521601051 | |
| Published online | 23 May 2025 | |
- A. Tahir, A. Mohamed, Probing Agricultural Engineering Education in Pakistan a Changing World. J. Education 2, 21 (2015) [Google Scholar]
- Z. Al-Ameen, G. Sulong, M.G.M. Johar, Fast deblurring method for computed tomography medical images using novel kernels set. Int. J. Bio-Sci. Bio-Technol. 4, 9–20 (2012) [Google Scholar]
- M. Srinivasa Rao, S. Praveen Kumar, K. Srinivasa Rao, Classification of Medical Plants Based on Hybridization of Machine Learning Algorithms. Indian J. Inf. Sources Serv. 13, 14–21 (2023) [Google Scholar]
- J.P. Dandois, E.C. Ellis, Crowdsourcing and community-based data collection for agricultural research: Applications in plant disease monitoring. J. Agric. Inform. 12, 112–126 (2021) [Google Scholar]
- K.P. Ferentinos, Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018) [CrossRef] [Google Scholar]
- P.K. Paul, R.R. Sinha, P.S. Aithal, B. Aremu, R. Saavedra, Agricultural Informatics: An Overview of Integration of Agricultural Sciences and Information Science. Indian J. Inf. Sources Serv. 10, 48–55 (2020) [Google Scholar]
- M. Hussain, Y. Geng, X. Zhang, J. Zhang, A review on remote sensing applications for plant disease detection and management. Int. J. Remote Sens. 39, 4314–4344 (2018) [Google Scholar]
- L. Alamer, I.M. Alqahtani, E. Shadadi, Intelligent Health Risk and Disease Prediction Using Optimized Naive Bayes Classifier. J. Internet Serv. Inf> Secur. 13, 01–10 (2023) [Google Scholar]
- Y. Li, Z. Chen, Y. Wang, Mobile-based plant disease detection using deep learning techniques. Sensors 20, 3963 (2020) [Google Scholar]
- A. Radhika, M.S. Masood, Crop Yield Prediction by Integrating Et-DP Dimensionality Reduction and ABP-XGBOOST Technique. J. Internet Serv. Inf. Secur. 12, 177–196 (2022) [Google Scholar]
- M. Mohan, G. Nair, M. Pillai, Advances in coconut tree health monitoring and management. Trop. Agric. Res. 31, 93–108 (2020) [Google Scholar]
- S.R. Reddy, K.S. Rao, R. Kumar, Challenges in managing coconut tree health: A review. J. Agric. Sci. Technol. 21, 1125–1137 (2019) [Google Scholar]
- K. Veerasamy, E.T. Fredrik, Intelligent Farming based on Uncertainty Expert System with Butterfly Optimization Algorithm for Crop Recommendation. J. Internet Serv. Inf. Secur. 13, 158–169 (2023) [Google Scholar]
- R. Sharma, S. Gupta, A. Singh, Comparative analysis of sensor technologies for plant health monitoring. J. Environ. Monit. 23, 579–591 (2021) [Google Scholar]
- S. Varma, M. Bansal, Challenges and limitations in machine learning-based plant disease detection systems. Agric. Technol. 31, 298–307 (2021) [Google Scholar]
- K. Veerasamy, E.J.T. Fredrik, Intelligence System towards Identify Weeds in Crops and Vegetables Plantation Using Image Processing and Deep Learning Techniques. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 14, 45–59 (2023) [Google Scholar]
- H. Yao, J. Zhang, X. Liu, Open-source technologies for plant disease monitoring: Low-cost solutions for developing regions. Comput. Electron. Agric. 194, 106741 (2022) [CrossRef] [Google Scholar]
- P. Angin, M.H. Anisi, F. Göksel, C. Gürsoy, A. Büyükgülcü, Agrilora: a digital twin framework for smart agriculture. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 11, 77–96 (2020) [Google Scholar]
- J. Zhang, X. Wang, M. Xu, Application of drone-based remote sensing for plant health monitoring: A case study on coconut palms. Remote Sens. 12, 2438 (2020) [CrossRef] [Google Scholar]
- Y. Camgözlü, Y. Kutlu, Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Nat. Eng. Sci. 8, 214–232 (2023) [Google Scholar]
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