E3S Web Conf.
Volume 380, 2023International Conference “Scientific and Technological Development of the Agro-Industrial Complex for the Purposes of Sustainable Development” (STDAIC-2022)
|Number of page(s)||15|
|Published online||13 April 2023|
Modeling and forecasting tasks of agriculture based on machine learning
1 Kyrgyz National University named after Zh.Balasagyn, Bishkek, Kyrgyz Republic
2 Kyrgyz State Technical University named after I.Razzakov, Bishkek, Kyrgyz Republic
3 Kyrgyz State University named after I.Arabaev, Bishkek, Kyrgyz Republic
4 Kyrgyz National Agrarian University named after K.I.Scriabin, Bishkek, Kyrgyz Republic
* Corresponding author: email@example.com
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.
Key words: yield / algorithms / model / machine learning / agricultural problems
© The Authors, published by EDP Sciences, 2023
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/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.