COMPARATIVE ASSESSMENT OF MACHINE LEARNING ALGORITHMS FOR FORECASTING WHEAT YIELDS USING CLIMATE INDICATORS AND SATELLITE VEGETATION INDICES

Kurmanov N. Kenzhin Z. Baxultanov D. Zhagalbayev B. Mussabalina D. Zhagalbayeva M. Amrenova G.
2025 Technology Center

Eastern-European Journal of Enterprise Technologies
2025 #5 Issue 13 72 - 80 pp.

The object of the study is wheat yield forecasting based on the integration of climatic indicators, satellite vegetation indices, and machine learning algorithms. The problem to be solved is the limited accuracy of traditional crop yield forecasting methods, which fail to capture the complex nonlinear and multidimensional interactions among climatic, biophysical, and agronomic factors, thereby reducing their applicability for global food security tasks. The proposed approach is applied to a dataset comprising 345 observations from 2001–2023, combining vegetation indices (MODIS), climatic parameters (ERA5), and official statistics on yield and sown areas. The methodology included descriptive statistics, correlation analysis and forecasting models based on random forest, support vector machine and convolutional neural network. Model performance was assessed using coefficient of determination, root mean square error and mean absolute error. Random forest and support vector machine showed the highest accuracy (R² = 0.85 with low errors), while convolutional neural network was less effective due to the limited dataset. The analysis confirmed the decisive role of vegetation indices, especially the normalized difference vegetation index, together with precipitation, temperature and sown area. The results address the identified research gap by demonstrating that the integration of climatic indicators and satellite vegetation indices significantly enhances the performance of machine learning models for wheat yield forecasting. In particular, the findings highlight the advantages of ensemble and support vector methods, which proved to be more robust and accurate under conditions of high climatic variability. The practical value lies in the potential use of these models in early warning and decision-support systems for farmers and state institutions, improving agrotechnical planning, resource allocation, and reducing food security risks, thereby contributing to global food security. Copyright

convolutional neural network , enhanced vegetation index , ERA5 , MODIS , normalized difference vegetation index , random forest , support vector machine , wheat yield forecasting

Text of the article Перейти на текст статьи

Department of Management and Innovation in Sports, Kazakh National Sports University, Mangilik El ave., B 2.2, Astana, 010000, Kazakhstan
Higher School of Business and Digital Technologies, Turan-Astana University, Ykylas Dukenuly str., 29, Astana, 010013, Kazakhstan
Department of Economic Specialties, Abai Kazakh National Pedagogical University, Dostyk ave., 13, Almaty, 050010, Kazakhstan
College of Economics and Management, Northwest A&F University, Taicheng rd., 3, Shaanxi, Yangling, 712100, China
Department of Economics L. N. Gumilyov Eurasian National University, Satbaev str., 2, Astana, 010000, Kazakhstan

Department of Management and Innovation in Sports
Higher School of Business and Digital Technologies
Department of Economic Specialties
College of Economics and Management
Department of Economics L. N. Gumilyov Eurasian National University

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026