EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 216 (2025) SHS Web Conf., 216 (2025) 01029 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 01029
Number of page(s) 10
Section Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development
DOI https://doi.org/10.1051/shsconf/202521601029
Published online 23 May 2025
  1. T. Suri, C. Udry, Agricultural technology in Africa. J. Econ. Perspect. 36(1), 33–56 (2022). https://doi.org/10.1257/jep.36.1.33 [CrossRef] [Google Scholar]
  2. V. Sharma, A.K. Tripathi, H. Mittal, Technological revolutions in smart farming: Current trends, challenges & future directions. Comput. Electron. Agric. 201, 107217 (2022). https://doi.org/10.1016/j.compag.2022.107217 [CrossRef] [Google Scholar]
  3. P. Nienkamp, Agricultural Technology. A Companion to American Agricultural History, 161–174 (2022). https://doi.org/10.1002/9781119632214.ch12 [CrossRef] [Google Scholar]
  4. N. Khan, R.L. Ray, G.R. Sargani, M. Ihtisham, M. Khayyam, S. Ismail, Current progress and prospects of agriculture technology: Gateway to sustainable agriculture. Sustainability 13(9), 4883 (2021). https://doi.org/10.3390/su13094883 [CrossRef] [Google Scholar]
  5. S.I. Seneviratne, X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A.D. Luca, R. Allan, Weather and climate extreme events in a changing climate (2021). https://centaur.reading.ac.uk/101846/ [Google Scholar]
  6. K.K. Shah, B. Modi, H.P. Pandey, A. Subedi, G. Aryal, M. Pandey, J. Shrestha, Diversified crop rotation: an approach for sustainable agriculture production. Adv. Agric. 2021(1), 8924087 (2021). https://doi.org/10.1155/2021/8924087 [Google Scholar]
  7. A. Sharma, A. Jain, P. Gupta, V. Chowdary, Machine learning applications for precision agriculture: A comprehensive review. IEEE Access 9, 4843–4873 (2020). https://doi.org/10.1109/ACCESS.2020.3048415 [Google Scholar]
  8. Z.K. Shinwari, S.A. Jan, K. Nakashima, K. Yamaguchi-Shinozaki, Genetic engineering approaches to understanding drought tolerance in plants. Plant Biotechnol. Rep. 14(2), 151–162 (2020). https://doi.org/10.1007/s11816-020-00598-6 [CrossRef] [Google Scholar]
  9. D. Ajiga, P.A. Okeleke, S.O. Folorunsho, C. Ezeigweneme, The role of software automation in improving industrial operations and efficiency. Int. J. Eng. Res. Updates 7(1), 22–35 (2024) [Google Scholar]
  10. Y. Inoue, Satellite-and drone-based remote sensing of crops and soils for smart farming-a review. Soil Sci. Plant Nutr. 66(6), 798–810 (2020). https://doi.org/10.1080/00380768.2020.1738899 [CrossRef] [Google Scholar]
  11. M. Piekutowska, G. Niedbała, T. Piskier, T. Lenartowicz, K. Pilarski, T. Wojciechowski, A. Czechowska-Kosacka, The application of multiple linear regression and artificial neural network models for yield prediction of very early potato cultivars before harvest. Agronomy 11(5), 885 (2021). https://doi.org/10.3390/agronomy11050885 [CrossRef] [Google Scholar]
  12. R.A. Schwalbert, T. Amado, G. Corassa, L.P. Pott, P.V. Prasad, I.A. Ciampitti, Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agric. For. Meteorol. 284, 107886 (2020). https://doi.org/10.1016/j.agrformet.2019.107886 [CrossRef] [Google Scholar]
  13. Y. Ma, Z. Zhang, Y. Kang, M. Özdoğan, Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach. Remote Sens. Environ. 259, 112408 (2021). https://doi.org/10.1016/j.rse.2021.112408 [CrossRef] [Google Scholar]
  14. 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]
  15. N.D. Brenowitz, Y. Cohen, J. Pathak, A. Mahesh, B. Bonev, T. Kurth, M.S. Pritchard, A practical probabilistic benchmark for ai weather models. arXiv preprint arXiv:2401.15305 (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.