Ainakulov, Zh., Iskakova, A., & Kurmankulova, G.
Machine learning methods in planning experiments and data analysis in the agricultural industry. |
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Full Article: PDF
Scientific Object Identifier: http://s-o-i.org/1.1/TAS-04-144-8
DOI: https://dx.doi.org/10.15863/TAS.2025.04.144.8
Language: Russian
Citation: Ainakulov, Zh., Iskakova, A., & Kurmankulova, G. (2025). Machine learning methods in planning experiments and data analysis in the agricultural industry. ISJ Theoretical & Applied Science, 04 (144), 38-44. Soi: https://s-o-i.org/1.1/TAS-04-144-8 Doi: https://dx.doi.org/10.15863/TAS.2025.04.144.8 |
Pages: 38-44
Published: 30.04.2025
Abstract: The application of machine learning methods in the agro-industrial complex (AIC) is becoming increasingly relevant for solving complex problems related to the planning of experiments and data analysis. This paper reviews key machine learning approaches that improve the efficiency of agricultural research, including predictive modeling, optimization of experimental designs, and identification of significant factors influencing productivity and quality. Special attention is given to supervised and unsupervised learning methods, such as decision trees, random forests, neural networks, and clustering algorithms. The integration of these methods enables the processing of large volumes of agro-technological data, supports decision-making, and contributes to the development of precision agriculture. The paper also discusses practical examples of using machine learning in soil analysis, crop yield forecasting, and optimization of agrotechnical measures.
Key words: machine learning, agricultural experiment, planning, data analysis, yield, forecasting, precision agriculture.
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