Mathematical Modeling
Vol. 6 (2022), Issue 3, pg(s) 86-89

MATHEMATICAL MODELLING OF TECHNOLOGICAL PROCESSES AND SYSTEMS

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

Abstract

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.

Keywords

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