Employment of machine learning techniques for crop yield forecasting based on climate parameters

  • 1 Faculty of Mechanical Engineering - Skopje, Ss. Cyril and Methodius University in Skopje, North Macedonia
  • 2 Faculty of Agricultural Sciences and Food, Ss. Cyril and Methodius University in Skopje, North Macedonia
  • 3 Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, North Macedonia


The ability to forecast the annual crop production is of crucial benefit for any country by providing the capability to define their import and export policies, as well as to estimate the economic gain of their agriculture planning. The weather conditions during the year significantly influence the growth of the crop, and the crop yield quantity is highly affected by the climate conditions in the different development cycles of the plant. Recently, the availability of historical climate data benefits the studies in the sector of agricultural sciences and food, and in particular the use of Artificial Intelligence methods in the big data analysis offers a significant opportunity to provide practicable information and actions. The present work aims to develop Machine Learning (ML) model to forecast the wheat yield based on historical climate data in a specific time frame in the Pelagonia valley in North Macedonia, as one of the most important regions for wheat production in the country. After pre-processing and selecting the input features, LS Boost regression model was employed as a ML method for estimation of the wheat yield from climate data, which resulted in high accuracy of wheat yield prediction even with limited dataset, both on the training and on the testing dataset. The research study proved the feasibility of using ML methods to complement the existing models for accurate wheat yield forecasting, providing significant advantage due to the ease of calibrating the ML model parameters.



  1. T. van Klompenburg, A. Kassahun, C. Catal, Crop yield prediction using machine learning: A systematic literature review, Comput. Electron. Agric. 177 (2020) 105709.
  2. D.T.V.N. Rao, S. Manasa, Artificial Neural Networks for Soil Quality and Crop Yield Prediction using Machine Learning, Int. J. Futur. Revolut. Comput. Sci. Commun. Eng. 5 (2019) 57 – 60–57 – 60.
  3. A.X. Wang, C. Tran, N. Desai, D. Lobell, S. Ermon, Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data, in: Proc. 1st ACM SIGCAS Conf. Comput. Sustain. Soc., Association for Computing Machinery, New York, NY, USA, 2018.
  4. P. Filippi, E.J. Jones, N.S. Wimalathunge, P.D.S.N. Somarathna, L.E. Pozza, S.U. Ugbaje, T.G. Jephcott, S.E. Paterson, B.M. Whelan, T.F.A. Bishop, An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning, Precis. Agric. 20 (2019) 1015–1029.
  5. B.M. Sagar, N.K. Cauvery, P. Abbi, N. Vismita, B. Pranava, P.A. Bhat, Analysis and Prediction of Cotton Yield with Fertilizer Recommendation Using Gradient Boost Algorithm BT - Information and Communication Technology for Competitive Strategies (ICTCS 2020), in: A. Joshi, M. Mahmud, R.G. Ragel, N. V Thakur (Eds.), Springer Singapore, Singapore, 2022: pp. 1143–1152.
  6. N. Gandhi, O. Petkar, L.J. Armstrong, A.K. Tripathy, Rice crop yield prediction in India using support vector machines, 2016 13th Int. Jt. Conf. Comput. Sci. Softw. Eng. JCSSE 2016. (2016) 1–5.
  7. M. Keerthana, K.J.M. Meghana, S. Pravallika, M. Kavitha, An ensemble algorithm for crop yield prediction, Proc. 3rd Int. Conf. Intell. Commun. Technol. Virtual Mob. Networks, ICICV 2021. (2021) 963–970.
  8. H.R. Seireg, Y.M.K. Omar, F.E.A.B.D. El-samie, A.S. Elfishawy, A. Elmahalawy, Ensemble Machine Learning Techniques Using Computer Simulation Data for Wild Blueberry Yield Prediction, IEEE Access. 10 (2022) 64671–64687.
  9. K.S.M. Anbananthen, S. Subbiah, D. Chelliah, P. Sivakumar, V. Somasundaram, K.H. Velshankar, M.K.A.A. Khan, An intelligent decision support system for crop yield prediction using hybrid achine learning algorithms., F1000Research. 10 (2021) 1143.
  10. S.G. Kundu, A. Ghosh, A. Kundu, G. G P, A ML-AI ENABLED ENSEMBLE MODEL FOR PREDICTING AGRICULTURAL YIELD, Cogent Food Agric. 8 (2022) 2085717.
  11. C.A. van Diepen, J. Wolf, H. van Keulen, C. Rappoldt, WOFOST: a simulation model of crop production, Soil Use Manag. 5 (1989) 16–24.
  12. K. Osama, B.N. Mishra, P. Somvanshi, Machine Learning Techniques in Plant Biology BT - PlantOmics: The Omics of Plant Science, in: D. Barh, M.S. Khan, E. Davies (Eds.), Springer India, New Delhi, 2015: pp. 731–754.
  13. W. Yang, K. Wang, W. Zuo, Neighborhood component feature selection for high-dimensional data, J. Comput. 7 (2012) 162–168.
  14. J.H. Friedman, Greedy function approximation: A gradient boosting machine, Ann. Stat. 29 (2001) 1189–1232.

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