APPLICATION OF CHAID DECISION TREES AND NEURAL NETWORKS METHODS IN FORECASTING THE YIELD OF CEREAL INDUSTRY COMPANIES
DOI:
https://doi.org/10.17770/het2024.28.8264Keywords:
CHAID decision tree, neural networks, grain-growing industry, yield, forecastingAbstract
Within the research the most important factors affecting the grain yield and their impact indicators were considered. The application of CHAID decision trees and neural networks in forecasting aspects of the grain-growing industry was investigated. In the study, the organic agricultural companies of the Latgale region, which are engaged in grain cultivation, were selected and the harvest volumes of the companies and their influencing factors were collected. Based on the collected data, grain yield forecasting was performed using CHAID decision tree and neural network methods.
Downloads
References
Lauku tīkls (2022). Graudkopībā viss tiek plānots par labu ražai. https://www.laukutikls.lv/nozares/lauksaimnieciba/raksti/graudkopiba-viss-tiek-planots-par-labu-razai, sk. 28.10.2023.
Blake, J.J., Spink, J.H., Dyer, C. (2003). Factors affecting cereal establishment and its prediction Retrieved October, 29, 2023, from https://projectblue.blob.core.windows.net/media/Default/Research%20Papers/Cereals%20and%20Oilseed/rr51_complete_final_report.pdf
Kazlauska, D. (2022). Kā izvēlēties piemērotāko sēklas materiālu. https://www.saimnieks.lv/raksts/ka-izveleties-piemerotako-seklas-materialu, sk. 30.10.2023.
NicheAgriculture [n.d.]. How Rainfall Affects Crop Health. Retrieved November, 11, 2023, from https://www.nicheagriculture.com/how-rainfall-affects-crop-health/
Liliane, T.N., Charles, M.S. (2019). Factors Affecting Yield of Crops. Retrieved October, 29, 2023, from https://www.intechopen.com/chapters/70658
Gaile, Z. [n.d.]. Augsnes apstrādes veidi un augsnes apstrādes sistēmas. https://www.lbtu.lv/sites/default/files/files/lapas/Augsnes_apstrades_veidi.pdf, sk. 30.10.2023.
Kreišmane, S., Popluga, D., Naglis-Liepa, K., Lēnerts, A. (2019). Augsnes auglības uzlabošana saimniecību ienesīgumam un labvēlīgākam klimatam. https://www.saimnieks.lv/raksts/augsnes-auglibas-uzlabosana-saimniecibu-ienesigumam-un-labveligakam-klimatam, sk. 30.10.2023.
Vaderstad [b.g.]. Tradicionālā augsnes apstrāde. https://www.vaderstad.com/lv/zini-ka/augsnes-sagatavosana/tradicionala-augsnes-apstrade/, sk. 30.10.2023.
Vaderstad [b.g.]. Minimālā augsnes apstrāde. https://www.vaderstad.com/lv/zini-ka/augsnes-sagatavosana/minimala-augsnes-apstrade/, sk. 30.10.2023.
Vaderstad [b.g.]. Tiešā sēja. https://www.vaderstad.com/lv/zini-ka/augsnes-sagatavosana/tiesa-seja/, sk. 30.10.2023.
Melece, L. (2020). Augsnes apstrādes tehnoloģijas. https://www.arei.lv/sites/arei/files/files/projects/EIP_sanaksme_4.03.2020._AREI_LMelece_Augsnes%20apstrade.pdf, sk. 30.10.2023.
Kazlauska, D. (2022). Kā izvēlēties piemērotāko sēklas materiālu. https://www.saimnieks.lv/raksts/ka-izveleties-piemerotako-seklas-materialu, sk. 30.10.2023.
Narayanan, S. (2018). Effects of high temperature stress and traits associated with tolerance in wheat. Open Access Journal of Science, Vol.2, Issue 3, p.177-186. Retrieved October, 29, 2023, from https://doi.org/10.15406/oajs.2018.02.00067
Gammans, M., Merel, P., Ortiz-Bobea, A. (2017). Negative impacts of climate change on cereal yields: statistical evidence from France. Enviromental Research Letters, Vol. 12, Issue 5. Retrieved October, 29, 2023, from https://iopscience.iop.org/article/10.1088/1748-9326/aa6b0c
Centrālā statistikas pārvalde (2023). Lauksaimniecības kultūraugu sējumu kopraža un vidējā ražība – Kultūraugi, Rādītājs un Laika periods.
https://data.stat.gov.lv/pxweb/lv/OSP_PUB/START__NOZ__LA__LAG/LAG020/table/tableViewLayout1/, sk. 28.10.2023
Java [n.d.]. Crop Yield Prediction Using Machine Learning. Retrieved November, 02, 2023, from https://www.javatpoint.com/crop-yield-prediction-using-machine-learning
Sreerama, A., Sagar, B.M. (2020). A Machine Learning Approach to Crop Yield Prediction. International Research Journal of Engineering and Technology (IRJET), Vol. 07, Issue 05. Retrieved October, 31, 2023, from https://www.irjet.net/archives/V7/i5/IRJET-V7I51246.pdf
Burhan, H.A. (2022). Crop yield prediction by integrating meteorological and pesticides use data with machine learning methods: an application for major crops in turkey. Retrieved October, 31, 2023, from https://dergipark.org.tr/en/download/article-file/2558217
Ramzai, J. (2020). Simple guide for Top 2 types of Decision Trees: CHAID and CART. Retrieved November, 23, 2023, from https://towardsdatascience.com/clearly-explained-top-2-types-of-decision-trees-chaid-cart-8695e441e73e
Wells, C. (2023). A guide to CHAID: a decision tree algorithm for data analysis. Retrieved November, 23, 2023, from https://www.adience.com/blog/how-to/a-guide-to-chaid-a-decision-tree-algorithm-for-data-analysis/
Khaki, S., Wang, L. (2019). Crop Yield Prediction Using Deep Neural Networks. Retrieved October, 31, 2023, from https://www.frontiersin.org/articles/10.3389/fpls.2019.00621/full
Sadenova, M., Beisekenov, N., Varbanov, P.S., Pan, T. (2023). Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan. Agriculture, Vol. 13, Issue, 6. Retrieved October, 31, 2023, from https://doi.org/10.3390/agriculture13061195
