Open Access
Issue
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
Article Number 01024
Number of page(s) 11
DOI https://doi.org/10.1051/epjconf/202532801024
Published online 18 June 2025
  1. Mavi, H., Upadhyay, S.K., Srivastava, N., Sharma, R., & Bhargava, R. (2024). Crop Recommendation System Based on Soil Quality and Environmental Factors Using Machine Learning. 507–512. https://doi.org/10.1109/innocomp63224.2024.00089 [Google Scholar]
  2. Ghosh, A., Mohapatra, S.K., Pattanaik, P., Dash, P.K., & Chakravarty, S. (2024). A Comprehensive Crop Recommendation System Integrating Machine Learning and Deep Learning Models. 1–6. https://doi.org/10.1109/ic-cgu58078.2024.10530724 [Google Scholar]
  3. Begum, S., Gadagkar, A.V., & Rameesa, K. (2024). Precision Agriculture Revolution: Enhancing Crop Recommendations with Machine Learning Algorithms for Optimal Yield and Environmental Sustainability. 1–7. https://doi.org/10.1109/icait61638.2024.10690764 [Google Scholar]
  4. Agarwal, A., Sharma, H., & Maan, A. (2024). Crop Recommendation System Using Machine Learning. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2024.62058 [Google Scholar]
  5. Liakos, K.G.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D. Machine learning in agriculture: A review. Sensors 2018, 18, 2674 [CrossRef] [Google Scholar]
  6. Fei S, Hassan MA, Xiao Y, Su X, Chen Z, Cheng, Q., Duan, F., Chen, R., Ma, Y. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precis Agric. 2022. https://doi.org/10.1007/s11119-022-09938-8. [Google Scholar]
  7. Jansson, C., Faiola, C., Wingler, A., Zhu, X.G., Kravchenko, A., De Graaff, M.A., et al. (2021). Crops for carbon farming. Front. Plant Science. 12, 636–709. DOI: 10.3389/fpls.2021.636709 [CrossRef] [Google Scholar]
  8. Zhang, Z., Jin, Y., Chen, B., Brown, P. (2019). California almond yield prediction at the orchard level with a machine learning approach. Front. Plant science. 10, 809. DOI: 10.3389/fpls.2019.00809 [CrossRef] [Google Scholar]
  9. Jayalakshmi, M., Gomathi, V. (2020). Sensor-cloud based precision agriculture approach for intelligent water management. Int. J. Plant Production. 14 (2), 177–186. DOI: 10.1007/s42106-019-00077-1 [CrossRef] [Google Scholar]
  10. Ahmed, M., Hayat, R., Ahmad, M., Kheir, A., Shaheen, F.A., Raza, M.A., et al. (2022). Impact of climate change on dryland agricultural systems: A review of current status, potentials, and further work need. Int. J. Plant Production. p, 1–23. DOI: 10.1007/s42106-022-00197-1 [Google Scholar]
  11. Sharma, R., Kamble, S.S., Gunasekaran, A., Kumar, V., Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Operations Res. 119, 104–926. DOI: 10.1016/j.cor.2020.104926 [CrossRef] [Google Scholar]
  12. Cravero, A., Sepulveda, S. (2021). Use and adaptations of machine learning in big data- Applications in real cases in agriculture. Electronics. 10 (5), 552. DOI: 10.3390/electronics10050552 [CrossRef] [Google Scholar]
  13. Shehadeh, A., Alshboul, O., Al Mamlook, R.E., Hamedat, O. (2021). Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation Construction. 129, 103–827. DOI: 10.1016/j.autcon.2021.103827 [CrossRef] [Google Scholar]
  14. Lee, H., Moon, A. (2014). "Development of yield prediction system based on real-time agricultural meteorological information," in In: 16th international conference on advanced communication technology. 1292–1295 (IEEE). DOI: 10.1109/ICACT.2014.6779168 [CrossRef] [Google Scholar]
  15. Chakrobarty, T., Al Galib, M.A., Islam, M.Z., Rahman, M.A. (2021). Adoption and adaptability of modern aman rice cultivars in faridpur region-Bangladesh. Sabrao J. Breed. Genet. 53 (4), 659–672. DOI: 10.54910/sabrao2021.53.4.9 [CrossRef] [Google Scholar]
  16. Sujjaviriyasup, T., Pitiruek, K. (2013). Agricultural product forecasting using machine learning approach. Int. J. Math Analysis. 7 (38), 1869–1875. DOI: 10.12988/ijma.2013.35113 [CrossRef] [Google Scholar]
  17. Young, L.J. (2019). Agricultural crop forecasting for large geographical areas. Annu. Rev. Stat its application. 6, 173–196. DOI: 10.1146/annurev-statistics-030718-105002 [CrossRef] [Google Scholar]
  18. Prasad, N., Patel, N., Danodia, A. (2021). Crop yield prediction in cotton for regional level using random forest approach. Spatial Inf. Res. 29 (2), 195–206. DOI: 10.1007/s41324-020-00346-6 [CrossRef] [Google Scholar]
  19. Minghua, W., Qiaolin, Z., Zhijian, Y., Jingui, Z. (2012). "Prediction model of agricultural product's price based on the improved BP neural network," in 2012 7th International Conference on Computer Science & Education (ICCSE). 613–617 (IEEE). DOI: 10.1109/ICCSE.2012.6295150 [CrossRef] [Google Scholar]
  20. Stekhoven, D.J., Buhlmann, P. (2012). MissForest-non-parametric missing value imputation for mixed-type data. Bioinformatics. 28 (1), 112–118. DOI: 10.1093/bioinformatics/btr597 [CrossRef] [PubMed] [Google Scholar]
  21. Benjelloun, O., Garcia-Molina, H., Gong, H., Kawai, H., Larson, T.E., Menestrina, D., et al. (2007). "D-swoosh: A family of algorithms for generic, distributed entity resolution," in 27th International Conference on Distributed Computing Systems (ICDCS'07). 37–37 (IEEE). DOI: 10.1109/ICDCS.2007.96 [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.