Model for determining factors affecting wheat productivity by machine learning
Views: 141 / PDF downloads: 74
DOI:
https://doi.org/10.32523/3007-0155/bulmathenu.2024/2.2Keywords:
machine learning, predictive model, feature importance, predictive analysis, decision makingAbstract
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.
References
Koshkarov A. V., Koshkarova T. A. Tekhnologii sbora i analiza dannyh v cifrovom sel'skom hozyajstve: bar'ery i usloviya dlya vnedreniya i ispol'zovaniya. [Koshkarov A.V., Koshkarova T.A. Technologies for collecting and analyzing data in digital agriculture: barriers and conditions for implementation and use], Sovremennaya nauka: aktual'nye problemy teorii i praktiki. Seriya: Estestvennye i Tekhnicheskie Nauki [Modern science: current problems of theory and practice. Series: Natural and Technical Sciences]. 2018. №05. P. 100-104.
Zayac O.A., Nazarova YU.N., Strizhakova E.A., Pen'kova R.I. Tekhnologii Big Data v sel'skom hozyajstve. [Zayats O.A., Nazarova Yu.N., Strizhakova E.A., Penkova R.I. Big Data technologies in agriculture], Fundamental research. 2022. №7. P. 35-40.
Xu X., Gao P., Zhu X., Guo W., Ding J., Li C., Wu X. Design of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu Province, China, Ecological Indicators. 2019. Vol. 101. P. 943-953. DOI: https://doi.org/10.1016/j.ecolind.2019.01.059
Alpaydin E. Introduction to Machine Learning, The MIT Press. Cambridge. 2014. 3rd edition. p. 3-4. [Electronic resource]. URL: url{https://dl.matlabyar.com/siavash/ML/Book/Ethem%20Alpaydin-Introduction%20to%20Machine%20Learning-The%20MIT%20Press%20(2014).pdf} (Accessed: 20.02.2024).
Van K. T., Kassahun A., Catal C. Crop yield prediction using machine learning: A systematic literature review, Computers and Electronics in Agriculture. 2020. Vol. 177. P. 105709. DOI: url{https://doi.org/10.1016/j.compag.2020.105709}. DOI: https://doi.org/10.1016/j.compag.2020.105709
Kitov V. Prakticheskie aspekty mashinnogo obucheniya [Kitov V. Practical aspects of machine learning], Otkrytye sistemy. SUBD [Open systems. DBMS]. №01. 2016. [Electronic resource]. URL: (url{https://www.osp.ru/os/2016/01/13048648}).
Tazhіbaj L.K., Myrzabekova G.E. Auyl sharuashylygyndagy Data Mining tekhnologiyalary [Tazhibay L.K., Myrzabekova G.E. Data Mining technologies in agriculture], M.A. Gendel’mannyn 110 zhyldygyna arnalgan «Sejfullin okulary-19» halykaralyk gylymi-praktikalyk konferenciya materialdary [Proceedings of the international scientific and practical conference "Seifullin readings-19" dedicated to the 110th anniversary of M.A. Handelman]. 2023. T.I, B.III. 137-141 b.
Crane-Droesch A. Machine learning methods for crop yield prediction and climate change impact assessment in agriculture, Environmental Research Letters 2018. Vol.13, №11. P. 1748-9326. DOI: https://dx.doi.org/10.1088/1748-9326/aae159. DOI: https://doi.org/10.1088/1748-9326/aae159
Elbasi E., Zaki C., Topcu AE., Abdelbaki W., Zreikat AI., Cina E., Shdefat A., Saker L. Crop Prediction Model Using Machine Learning Algorithms, Applied Sciences. 2023. Vol. 13, №16. P. 9288. DOI: https://doi.org/10.3390/app13169288. DOI: https://doi.org/10.3390/app13169288
Gajula A., Singamsetty J., Dodda V., Kuruguntla L. Prediction of crop and yield in agriculture using machine learning technique, 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). 2021. P.1-5. DOI: 10.1109/ICCCNT51525.2021.9579843. DOI: https://doi.org/10.1109/ICCCNT51525.2021.9579843
Tussupov J., Yessenova M., Abdikerimova G., Aimbetov A., Baktybekov K., Murzabekova G., Aitimova U. Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods, IEEE Access. 2024. Vol.12. P. 19902–19910. DOI: https://doi.org/10.1109/ACCESS.2024.3361046
Yessenova M., Abdikerimova G., Ayazbaev T., Ismailova A., Beldeubayeva Z., Ainagulova A., Mukhanova A. The effectiveness of methods and algorithms for detecting and isolating factors that negatively affect the growth of crops, International Journal of Electrical and Computer Engineering. 2003. Vol. 13, №2. P. 1669-1679. DOI: 10.11591/ijece.v13i2. DOI: https://doi.org/10.11591/ijece.v13i2.pp1669-1679
Kucenogij P.K., Kalichkin V.K., Pakul' A.L., Kucenogij S.P. Mashinnoe obuchenie v kachestve instrumenta prognozirovaniya urozhajnosti sel'skohozyajstvennyh kul'tur. [Kutsenogiy P.K., Kalichkin V.K., Pakul A.L., Kutsenogiy S.P. Machine learning as a tool for crop yield forecasting], Rossijskaya sel'skohozyajstvennaya nauka. [Russian agricultural science]. 2021.№1. P. 72-75. DOI: 10.31857/S2500262721010178 DOI: https://doi.org/10.31857/S2500262721010178
Arhipova M. YU. Modelirovanie urozhajnosti zernovyh kul'tur sel'skohozyajstvennyh regionov c ispol'zovaniem tekhnologij komp'yuternogo zreniya. [Arkhipova M. Yu. Modeling the yield of grain crops in agricultural regions using computer vision technologies], Ekonomika regiona.[Economy of the region]. 2022. Vol. Is. 18, vyp. 2. P. 581-594. DOI: https://doi.org/10.17059/ekon.reg.2022-2-20. DOI: https://doi.org/10.17059/ekon.reg.2022-2-20
Akshalov K.A. Vysokoeffektivnye tekhnologii vozdelyvaniya zernovyh kul'tur v poluzasushlivyh usloviyah Severnogo Kazahstana. [Akshalov K.A. Highly efficient technologies for cultivating grain crops in semi-arid conditions of Northern Kazakhstan], Metodicheskie rekomendacii. [Guidelines]. 2009. 57p.
Erenstein O., Jaleta M., Abdul Mottaleb K., Sonder K., Donovan J., Braun H.-J. Global Trends in Wheat Production, Consumption and Trade, Wheat Improvement: Food Security in a Changing Climate. 2022. P. 47 – 66. DOI: https://doi.org/10.1007/978-3-030-90673-3_4
Zachow M., Nóia Júnior, Rogério de S., Asseng S. Seasonal climate models for national wheat yield forecasts in Brazil, Agricultural and Forest Meteorology. 2023. Vol. 342. P.109753-109767. DOI: https://doi.org/10.1016/j.agrformet.2023.109753
