Publisher
Springer Nature Switzerland
Reference29 articles.
1. Kale, S.S., Patil, P.S.: A machine learning approach to predict crop yield and success rate. In: 2019 IEEE Pune Sect. Int. Conf. PuneCon 2019, pp. 1–5 (2019). doi: https://doi.org/10.1109/PuneCon46936.2019.9105741
2. van Klompenburg, T., Kassahun, A., et al.: Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177, 105709 (2020). https://doi.org/10.1016/j.compag.2020.105709
3. Deepa, N., N, S. K., Srinivasan, K., Chang, C.Y., Bashir, A.K.: An Efficient ensemble VTOPES multi-criteria decision-making model for sustainable sugarcane farms. Sustainability 11(16) (2019). https://doi.org/10.3390/su11164288
4. Gonzalez-Sanchez, A., Frausto-Solis, J., Ojeda-Bustamante, W.: Predictive ability of machine learning methods for massive crop yield prediction. Spanish J. Agric. Res. 12(2), 313–328 (2014). https://doi.org/10.5424/sjar/2014122-4439
5. Groenendyk, D., Thorp, K., Ferré, T., Crow, W., Hunsaker, D.: A k-means clustering approach to assess wheat yield prediction uncertainty with a HYDRUS-1D coupled crop model. In: Proc. - 7th Int. Congr. Environ. Model. Softw. Bold Visions Environ. Model, iEMSs 2014, vol. 3, pp. 1326–1333 (2014)
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Optimizing Fertilizer Usage using Machine Learning Techniques;2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon);2024-04-25