Kartanova Asel
@kstu.kg
Higher School of Economics and Business
KSTU Kyrgyzstan
RESEARCH INTERESTS
computer simulation fluid dynamics, fluid and gas mechanics, information technologies, computer added engeeniring
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Baratbek Sabitov, Asel Kartanova, Talant Kurmanbek uulu, Nazgul Seitkazieva, Ainura Dyikanova, and Aida Orozobekova
EDP Sciences
Continuous advances in computer technology have provided good support for the expansion of agricultural research using machine learning. This article considered the current problem of yield forecasting using methods and algorithms of machine learning to support management decision-making in the agricultural sector. For a set of data collected from five districts of the Issyk-Kul region, such as weather conditions, soil characteristics and pre-processing of the sowing area, a study of the yield of various crops using advanced machine learning algorithms, such as the support vector method, k-nearest neighbors, variants of gradient boosting and random forest, etc., is demonstrated. To assess the accuracy of the models, a comparative analysis with the results of multiple regression was carried out. It is shown that powerful regression machine learning algorithms like k-nearest neighbors (KNN), random forest (RF), support vector method (SVR) and gradient boosting (GBR) give tangible results in prediction compared to other machine learning methods (MAPE=10%). The calculation results showed the effectiveness of using algorithms with ensemble methods to solve the problems of yield forecasting, and that environmental factors (weather conditions) have a greater impact on yield than soil genotype.
ZhV Chashina, , AD Kartanova, and
National Research Mordovia State University MRSU