Model for determining factors affecting wheat productivity by machine learning - Tazhibay Ләззат; Murzabekova Гульден; Stybayev Ғани; Muratova Гульжан (Bulletin of L.N. Gumilyov Eurasian National University. Mathematics, computer science, mechanics series, 2024)
To stimulate economic growth, crop production is the basis of the economy of Kazakhstan. Since productivity forecasting is an important aspect of agricultural planning and management, modern forecasting methods and models play an important role. Agricultural yields depend on weather conditions. Modeling using modern intelligent methods, including machine learning methods, to predict the impact of weather on wheat yields is highly effective. Models based on weather data and machine learning (ML) techniques can significantly reduce the time it takes to predict performance and identify the impact of weather on performance. This paper used advanced machine learning algorithms to predict crop yields based on available data. A comparative analysis of the considered algorithms has been carried out. ML models based on linear algorithm, decision trees and boosting algorithms were used. Productivity is predicted based on weather data from the Akkol district of the Akmola region.