Modeling and forecasting tasks of agriculture based on machine learning - yield forecasting using machine learning algorithms

Aug 2, 2026

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Continuous advances in computer technology have accelerated agricultural research with machine learning methods. This study addresses crop yield forecasting to support decision-making in the agricultural sector using multivariate data from five districts of the Issyk-Kul region, including weather conditions, soil characteristics, and sowing-area preprocessing. Models are built with support vector methods, k-nearest neighbors, gradient boosting variants, and random forests, then evaluated via comparative analysis with multiple regression using MAPE as a key metric.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Abstract

Introduction

Food security and yield modeling

Key factors: weather vs. soil genotype

Related work and prior studies

Methods

Data preparation and feature inputs

Machine learning models compared

Results and discussion

Accuracy comparison with multiple regression

Impact analysis of environmental factors

Modeling and forecasting tasks of agriculture based on machine learning Baratbek Sabitov1, Asel Kartanova2,*, Talant Kurmanbek uulu3, Nazgul Seitkazieva3, Ainura Dyikanova4, and Aida Orozobekova2 1Kyrgyz National University named after Zh.Balasagyn, Bishkek, Kyrgyz Republic 2Kyrgyz State Technical University named after I.Razzakov, Bishkek, Kyrgyz Republic 3Kyrgyz State University named after I.Arabaev, Bishkek, Kyrgyz Republic 4Kyrgyz National Agrarian University named after K.I. Scriabin, Bishkek, Kyrgyz Republic Abstract. 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. Keywords: yield, algorithms, model, machine learning, agricultural problems. 1 Introduction Food security plays a key role worldwide and is an important lever in the success of a country’s economy as a whole. In this case, its main component is the yield of crops. Yields found in many agricultural projects belong to complex categories in modeling and forecasting tasks. It is a trait that, in general, can be defined as an amalgamation of many factors of nature and the natural conditions of the environment where a particular crop is grown and requires comprehensive research. Accurate crop yield prediction requires a fundamental understanding of the functional relationship between crop yield and factors * Corresponding author: [a.kartanova@gmail.com](a.kartanova@gmail.com) © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). related directly to specific natural phenomena like temperature, humidity, and precipitation, as well as indirectly affecting yields. As indirect factors, the characteristics of the sowing areas, the quality and composition of the soil treated with fertilizers, and the results of seed studies of plant genotype for the region under study can be taken into account. At the sametime, it is very important to have reliable data sets, as well as powerful algorithms for modeling and predicting such processes in order to identify the relationship. To build a yield model, it is also necessary to establish other relationships, for example, with the phenomena of climate change, which is currently typical for many regions of Kyrgyzstan. For example, despite the dry year of 2021, many regions of Kyrgyzstan were characterized by a shortage of irrigation water, abnormally hot days. But in Issyk-Kul region in the period from August 15 to September 15, 2021 there were incessant heavy rains, ambient humidity, favorable temperature conditions, coincided with the period of fruit bearing and ripening of fruits, rapid

FAQs

Which data sources are used to build the crop yield models in this study?

The study uses datasets collected from five districts of the Issyk-Kul region, including weather conditions, soil characteristics, and preprocessing related to the sowing area.

Which machine learning algorithms are compared for yield forecasting?

The work demonstrates prediction using support vector methods, k-nearest neighbors, gradient boosting variants, and random forest, and evaluates them against other machine learning approaches.

How do the findings explain the roles of environmental factors and soil genotype?

The results indicate that ensemble-based regression machine learning approaches are effective for yield forecasting, and that weather conditions have a greater impact on yield than soil genotype.

Aug 2, 2026

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