EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 216 (2025) SHS Web Conf., 216 (2025) 01072 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 01072
Number of page(s) 12
Section Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development
DOI https://doi.org/10.1051/shsconf/202521601072
Published online 23 May 2025
  1. C. Lesk, W. Anderson, A. Rigden, O. Coast, J. Jägermeyr, S. McDermid, M. Konar, Compound heat and moisture extreme impacts on global crop yields under climate change. Nature Rev. Earth & Environ. 3, 872–889 (2022). https://doi.org/10.1038/s43017-022-00368-8 [CrossRef] [Google Scholar]
  2. 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]
  3. T. Banerjee, S. Sinha, P. Choudhury, Long term and short-term forecasting of horticultural produce based on the LSTM network model. Appl. Intell. (2022). https://doi.org/10.1007/s10489-021-02845-x [Google Scholar]
  4. 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. Dependable Appl. 14, 45–59 (2023) [Google Scholar]
  5. Z. Wang, G.E. Dahl, K. Swersky, C. Lee, Z. Nado, J. Gilmer, Z. Ghahramani, Pre-trained Gaussian processes for Bayesian optimization. J. Mach. Learn. Res. 25, 1–83 (2024) [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. Bao, L. Dong, W. Wang, N. Yang, S. Piao, F. Wei, Fine-tuning pretrained transformer encoders for sequence-to-sequence learning. Int. J. Mach. Learn. Cybern. 15, 1711–1728 (2024). https://doi.org/10.1007/s13042-023-01992-6 [CrossRef] [Google Scholar]
  8. T.M. Alabi, E.I. Aghimien, F.D. Agbajor, Z. Yang, L. Lu, A.R. Adeoye, B. Gopaluni, A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems. Renew. Energy 194, 822–849 (2022) [CrossRef] [Google Scholar]
  9. J. Shuford, Deep reinforcement learning unleashing the power of AI in decision-making. J. Artif. Intell. Gen. Sci. 1 (2024) [Google Scholar]
  10. T. Rajasivaranjan, A. Anandhi, N.R. Patel, M. Irannezhad, C.V. Srinivas, K. Veluswamy, P. Raja, Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield. Sci. Rep. 12, 16985 (2022). https://doi.org/10.1038/s41598-022-19750-z [CrossRef] [Google Scholar]
  11. I. Farahbakhsh, C.T. Bauch, M. Anand, Modelling coupled human-environment complexity for the future of the biosphere: strengths, gaps and promising directions. Philos. Trans. R. Soc. B 377, 20210382 (2022) [CrossRef] [Google Scholar]
  12. M.A.R. Suleman, S. Shridevi, Short-term weather forecasting using spatial feature attention-based LSTM model. IEEE Access 10, 82456–82468 (2022) [CrossRef] [Google Scholar]
  13. P.P. Ippolito, Hyperparameter Tuning: The Art of Fine-Tuning Machine and Deep Learning Models to Improve Metric Results, in Applied data science in tourism: Interdisciplinary approaches, methodologies, and applications (Springer International Publishing, Cham, 2022), pp. 231–251 [CrossRef] [Google Scholar]
  14. M. Zulfiqar, K.A. Gamage, M. Kamran, M.B. Rasheed, Hyperparameter optimization of bayesian neural network using bayesian optimization and intelligent feature engineering for load forecasting. Sensors 22, 4446 (2022) [CrossRef] [Google Scholar]
  15. M.S. Sheela, S. Gopalakrishnan, I.P. Begum, J.J. Hephzipah, M. Gopianand, D. Harika, Enhancing Energy Efficiency With Smart Building Energy Management System Using Machine Learning and IOT. Babylon. J. Mach. Learn. 2024, 80–88 (2024) [CrossRef] [Google Scholar]
  16. E.K. Ruby, G. Amirthayogam, G. Sasi, T. Chitra, A. Choubey, S. Gopalakrishnan, Advanced Image Processing Techniques for Automated Detection of Healthy and Infected Leaves in Agricultural Systems. Mesopotamian J. Comput. Sci. 2024, 62–70 (2024) [Google Scholar]
  17. M.A. Ahmed, J.L. Gallardo, M.D. Zuniga, M.A. Pedraza, G. Carvajal, N. Jara, R. Carvajal, LoRa based IoT platform for remote monitoring of large-scale agriculture farms in Chile. Sensors 22, 2824 (2022) [CrossRef] [Google Scholar]
  18. J.P. Bharadiya, N.T. Tzenios, M. Reddy, Forecasting of crop yield using remote sensing data, agrarian factors and machine learning approaches. J. Eng. Res. Rep. 24, 29–44 (2023) [CrossRef] [Google Scholar]
  19. T.M. Alabi, E.I. Aghimien, F.D. Agbajor, Z. Yang, L. Lu, A.R. Adeoye, B. Gopaluni, A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems. Renew. Energy 194, 822–849 (2022) [CrossRef] [Google Scholar]
  20. M. Hassan, A. Kowalska, H. Ashraf, Advances in deep learning algorithms for agricultural monitoring and management. Appl. Res. Artif. Intell. Cloud Comput. 6, 68–88 (2023) [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.