EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 216 (2025) SHS Web Conf., 216 (2025) 01043 References
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 01043
Number of page(s) 7
Section Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development
DOI https://doi.org/10.1051/shsconf/202521601043
Published online 23 May 2025
  1. F.J. Adha, M. Gapar Md Johar, M.H. Alkawaz, A. Iqbal Hajamydeen, L. Raya, IoT based Conceptual Framework for Monitoring Poultry Farms, in Proceedings of the 12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022, 277–282 (2022) [CrossRef] [Google Scholar]
  2. T.G. Dietterich, Ensemble methods in machine learning, in Multiple Classifier Systems, 1–15 (Springer, 2000). https://doi.org/10.1007/3-540-45014-9_1 [Google Scholar]
  3. H.A. Singh, A.M. Singh, A Hybrid Approach of Deep Learning and Optimization for Medical Plant Recognition and Classification. IRJAES. 8, 94 (2023) [Google Scholar]
  4. K.P. Ferentinos, Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018) [CrossRef] [Google Scholar]
  5. A.A. Ahmed, G.H. Reddy, A mobile-based system for detecting plant leaf diseases using deep learning. AgriEngineering 3, 478–493 (2021). https://doi.org/10.3390/agriengineering3030032 [CrossRef] [Google Scholar]
  6. 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]
  7. H.E. Nilsson, Remote sensing and image analysis in plant pathology. Can. J. Plant Pathol. 17, 154–166 (1995) [CrossRef] [Google Scholar]
  8. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015) [CrossRef] [Google Scholar]
  9. 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]
  10. S.A. Rashwan, M.K. Elteir, Plant leaf disease detection using deep learning on mobile devices. Int. J. Comput. Vis. Robot. 12, 156–176 (2022) [CrossRef] [Google Scholar]
  11. S.P. Mohanty, D.P. Hughes, M. Salathé, Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016) [CrossRef] [Google Scholar]
  12. 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]
  13. P. Oyekola, N. Lambrache, A. Mohamed, J. Pumwa, L. Olaru, B. N'Drelan, Design and construction of an unmanned ground vehicle, in Proceedings of the International Conference on Industrial Engineering and Operations Management, Toronto, Canada, October 23-25 (2019) [Google Scholar]
  14. G. Ushadevi, A survey on plant disease prediction using machine learning and deep learning techniques. Inteligencia Artif. 23, 136–154 (2020) [CrossRef] [Google Scholar]
  15. K. Veerasamy, E.J. Thomson Fredrik, Intelligence System towards Identify Weeds in Crops and Vegetables Plantation Using Image Processing and Deep Learning Techniques. J. Wirel. Mob. Netw. Ubiquitous Comput. Depend. Appl. 14, 45–59 (2023) [Google Scholar]
  16. M.L. Buchaillot, J.A. Fernandez-Gallego, H. Mahmoudi, S. Thushar, G. Al Jabri, A.A. Aljanaahi, S.C. Kefauver, Deep leaning for detection of plant disorders on crop leaves: from data collection to framework tools, in Multi-scale and multi-sensor remote sensing in international agricultural development, 125 (2020) [Google Scholar]
  17. Z. Li, R. Paul, T. Ba Tis, A.C. Saville, J.C. Hansel, T. Yu, Q. Wei, Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles. Nat. Plants 5, 856–866 (2019) [CrossRef] [Google Scholar]
  18. 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. Depend. Appl. 11, 77–96 (2020) [Google Scholar]
  19. Y. Camgözlü, Y. Kutlu, Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Nat. Eng. Sci. 8, 214–232 (2023) [Google Scholar]
  20. G. Mohyuddin, M.A. Khan, A. Haseeb, S. Mahpara, M. Waseem, A.M. Saleh, Evaluation of Machine Learning approaches for precision farming in Smart Agriculture System-A comprehensive Review. IEEE Access (2024) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.