EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 380 (2023) E3S Web Conf., 380 (2023) 01026 References
Open Access
Issue
E3S Web Conf.
Volume 380, 2023
International Conference “Scientific and Technological Development of the Agro-Industrial Complex for the Purposes of Sustainable Development” (STDAIC-2022)
Article Number 01026
Number of page(s) 15
DOI https://doi.org/10.1051/e3sconf/202338001026
Published online 13 April 2023
  1. J. Romero, P. Roncallo, P. Akkiraju, I. Ponzoni, V. Echenique, J. Carballido, Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires Comput. Electron. Agric., 96 (2013), pp. 173–179, https://doi.org/10.1016/j.compag.2013.05.006 [CrossRef] [Google Scholar]
  2. M. Paul, S. Vishwakarma, A. Verma, Analysis of soil behaviour and prediction of crop yield using data mining approach. In: 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, pp. 766–771. https://doi.org/10.1109/CICN.2015.156. [Google Scholar]
  3. J. Jeong, J. Resop, N. Mueller, D. Fleisher, K. Yun, E. Butler, S. Kim, Random forests for global and regional crop yield predictions. PLoS ONE, 11 (6) (2016), https://doi.org/10.1371/journal.pone.0156571 [Google Scholar]
  4. N. Gandhi, O. Petkar, L. Armstrong, A. Tripathy, Rice crop yield prediction in India using support vector machines. In: 2016 13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016. https://doi.org/10.1109/JCSSE.2016.7748856 [Google Scholar]
  5. N. Gandhi, L. Armstrong,. Applying data mining techniques to predict yield of rice in humid subtropical climatic zone of India. In: Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016, 1901-1906. Retrieved from https://ieeexplore.ieee.org/abstract/document/7724597/ [Google Scholar]
  6. R. Sujatha, P. Isakki, A study on crop yield forecasting using classification techniques. In: 2016 International Conference on Computing Technologies and Intelligent Data Engineering, ICCTIDE 2016. https://doi.org/10.1109/TCCTIDE.2016.7725357 [Google Scholar]
  7. H. Cheng, L. Damerow, Y. Sun, M. Blanke, Early yield prediction using image analysis of apple fruit and tree canopy features with neural networks J. Imag., 3 (1) (2017), p. 6, https://doi.org/10.3390/jimaging3010006 [CrossRef] [Google Scholar]
  8. S. Bargoti, J. Underwood, Image segmentation for fruit detection and yield estimation in apple orchards J. Field Rob., 34 (6) (2017), pp. 1039–1060, https://doi.org/10.1002/rob.21699 [CrossRef] [Google Scholar]
  9. A. Shekoofa, Y. Emam, N. Shekoufa, M. Ebrahimi, E. Ebrahimie, Determining the Most Important Physiological and Agronomic Traits Contributing to Maize Grain Yield through Machine Learning Algorithms: A New Avenue in Intelligent Agriculture. PLoS ONE 9(5): e97288. https://doi.org/10.1371/journal.pone.0097288 [Google Scholar]
  10. G. Brown, (2017). “Ensemble learning” in Encyclopedia of Machine Learning and Data Mining. Ed. Sammut, K., Webb, G.I. (Boston, MA: Springer, USA), 393–402. [CrossRef] [Google Scholar]
  11. L. Breiman, Bagging predictors. Machine Learning 24, 123–140 (1996). https://doi.org/10.1007/BF00058655 [Google Scholar]
  12. L. Breiman, Random Forests. Machine Learning 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324 [NASA ADS] [CrossRef] [Google Scholar]
  13. You, J., Li, X., Low, M., Lobell, D., and Ermon, S. (2017). “Deep gaussian process for crop yield prediction based on remote sensing data,” in Thirty-First AAAI Conference on Artificial Intelligence (San Francisco, CA), 4559–4566. [Google Scholar]
  14. G. James, D. Witten, T. Hastie, R. Tibshirani, Introduction to Statistical Learning, Vol. 112, (2013) (New York Heidelberg Dordrecht London: Springer). https://doi.org/10.1007/978-1-4614-7138-7 [CrossRef] [Google Scholar]
  15. Friedman, J., Hastie, T., Tibshirani, R. The Elements of Statistical Learning Springer Series in Statistics, New York (2001). Google Scholar [Google Scholar]
  16. James, G., Witten D., Hastie T., Tibshirani, R. An Introduction to Statistical Learning Springer, New York (2013). Google Scholar [CrossRef] [Google Scholar]
  17. Khaki S. and Wang, L. (2019) Crop Yield Prediction Using Deep Neural Networks. Front. Plant Sci. 10:621. https://doi.org/10.3389/fpls.2019.00621 [CrossRef] [Google Scholar]
  18. Taherei-Ghazvinei P., Hassanpour-Darvishi H., Mosavi A., Yusof K.W., Alizamir M., Shamshirband S., Chau, K. Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network Eng. Appl. Comput. Fluid Mech., 12 (1) (2018), pp. 738–749, https://doi.org/10.1080/19942060.2018.1526119 [Google Scholar]
  19. M. Villanueva, M. Louella, M. Salenga, Bitter Melon Crop Yield Prediction using Machine Learning Algorithm. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9. (2018) Retrieved from www.ijacsa.thesai.org. [Google Scholar]
  20. Gandhi, N., Petkar, O., Armstrong, L.J., Tripathy, A.K., Rice crop yield prediction in India using support vector machines. In: 2016 13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016. [Google Scholar]
  21. Abhishek, K., Singh, M., Ghosh, S., and Anand, A. (2012). Weather forecasting model using artificial neural network. Procedia Technol. 4, 311–318. https://doi.org/10.1016/j.protcy.2012.05.047 [CrossRef] [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.