EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 79 (2025) ITM Web Conf., 79 (2025) 01045 References
Error
  • The authentification system partialy failed, sorry for the inconvenience. Please try again later.
Open Access
Issue
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
Article Number 01045
Number of page(s) 7
DOI https://doi.org/10.1051/itmconf/20257901045
Published online 08 October 2025
  1. L.K. Subramaniam, R. Marimuthu, Crop yield prediction using effective deep learning and dimensionality reduction approaches for Indian regional crops. e-Prime Adv. Electr. Eng. Electron. Energy 8, 100611 (2024). https://doi.org/10.1016/j.prime.2024.100611 [Google Scholar]
  2. S. Bregaglio, F. Ginaldi, E. Raparelli, G. Fila, S. Bajocco, Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets. Agric. Syst. 209, 103666 (2023). https://doi.org/10.1016/j.agsy.2023.103666 [Google Scholar]
  3. S.D. Gawade, A. Bhansali, S. Chopade, U. Kulkarni, Optimizing crop yield prediction with R2U-Net-AgriFocus: A deep learning architecture leveraging satellite imagery and agroenvironmental data. Expert Syst. Appl. 296, 128942 (2026). https://doi.org/10.1016/j.eswa.2025.128942 [Google Scholar]
  4. D. Paudel, A. De Wit, H. Boogaard, D. Marcos, S. Osinga, I.N. Athanasiadis, Interpretability of deep learning models for crop yield forecasting. Comput. Electron. Agric. 206, 107663 (2023). https://doi.org/10.1016/j.compag.2023.107663 [Google Scholar]
  5. S. Mahalakshmi, A.J. Anand, P. Partheeban, Soil and crop interaction analysis for yield prediction with satellite imagery and deep learning techniques for the coastal regions. J. Environ. Manag. 380, 125095 (2025). https://doi.org/10.1016/j.jenvman.2025.125095 [Google Scholar]
  6. A. Kaur, P. Goyal, R. Rajhans, L. Agarwal, N. Goyal, Fusion of multivariate time series meteorological and static soil data for multistage crop yield prediction using multi-head self attention network. Expert Syst. Appl. 226, 120098 (2023). https://doi.org/10.1016/j.eswa.2023.120098 [Google Scholar]
  7. U. Nirosha, G. Vennila, Enhancing crop yield prediction for agriculture productivity using federated learning integrating with graph and recurrent neural networks model. Expert Syst. Appl. 289, 128312 (2025). https://doi.org/10.1016/j.eswa.2025.128312 [Google Scholar]
  8. S. Stiller, K. Grahmann, G. Ghazaryan, M. Ryo, Improving spatial transferability of deep learning models for small-field crop yield prediction. ISPRS Open J. Photogramm. Remote Sens. 12, 100064 (2024). https://doi.org/10.1016/j.ophoto.2024.100064 [Google Scholar]
  9. D. Al-Shammari, B.M. Whelan, C. Wang, R.G. Bramley, T.F. Bishop, Assessment of red-edge based vegetation indices for crop yield prediction at the field scale across large regions in Australia. Eur. J. Agron. 164, 127479 (2025). https://doi.org/10.1016/j.eja.2024.127479 [Google Scholar]
  10. H. Demirhan, A deep learning framework for prediction of crop yield in Australia under the impact of climate change. Inf. Process. Agric. 12, 125–138 (2025). https://doi.org/10.1016/j.inpa.2024.04.004 [Google Scholar]
  11. M. Qiao, X. He, X. Cheng, P. Li, Q. Zhao, C. Zhao, Z. Tian, KSTAGE: A knowledge-guided spatial- temporal attention graph learning network for crop yield prediction. Inf. Sci. 619, 19–37 (2023). https://doi.org/10.1016/j.ins.2022.10.112 [Google Scholar]
  12. M. Waqar, Y.W. Kim, Y.C. Byun, A stacking ensemble framework leveraging synthetic data for accurate and stable crop yield forecasting. IEEE Access. 13, 136909–136926 (2025). https://doi.org/10.1109/ACCESS.2025.3591802 [Google Scholar]
  13. F.M. Talaat, Crop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changes. Neural Comput. Appl. 35, 17281–17292 (2023). https://doi.org/10.1007/s00521-023-08619-5 [Google Scholar]
  14. L. Jovanovic, M. Zivkovic, N. Bacanin, M. Dobrojevic, V. Simic, K.K. Sadasivuni, E.B. Tirkolaee, Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction. Neural Comput. Appl. 36, 14727–14756 (2024). https://doi.org/10.1007/s00521-024-09850-4 [Google Scholar]
  15. O.S. Nagesh, R.R. Budaraju, S.S. Kulkarni, M. Vinay, S.S.M. Ajibade, M. Chopra, M. Jawarneh, K. Kaliyaperumal, Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture. Discov. Sustain. 5, 78 (2024). https://doi.org/10.1007/s43621-024-00254-x [Google Scholar]
  16. The Wheat yield prediction dataset link: https://www.kaggle.com/datasets/shaikasif89/wheat-yeild/data [Google Scholar]
  17. The crop yield prediction dataset link: https://www.kaggle.com/datasets/patelris/crop-yield-prediction-dataset [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.