Abstract
Machine learning is an important decision support tool for predicting crop yields, including supporting decisions about which crops to grow and what to do during the crop growing season. Various machine learning algorithms have been used to support crop yield prediction research, including K-Nearest Neighbor, Random Forest Classifier, logistic regression and many more. The objective of this paper is to develop a linear machine learning algorithm based on a Generalized Linear Model (GLM) that is more accurate and also provides early prediction of corn yield with a relative error of less than 20%, which is crucial for decisions on allocating harvesting and storage resources in Croatia. Input parameters for our model include various climate and greenhouse gas parameters. We examined how accurate the corn yield prediction would be if farmers wanted to know what the corn yield would be at a given harvest date during the R1 (Silking) phase of the corn and thereafter. For this test case, the relative error for our model was 11.63%, while for GLM it was 12.55%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Moussaid, A., El Fkihi, S., Zennayi, Y., Lahlou, O., Kassou, I., Bourzeix, F., El Mansouri, L., Imani, Y.: Machine learning applied to tree crop yield prediction using field data and satellite imagery: a case study in a citrus orchard. Informatics 9(4) (2022), https://www.mdpi.com/2227-9709/9/4/80
van Klompenburg, T., Kassahun, A., Catal, C.: Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177, 105709 (2020), https://www.sciencedirect.com/science/article/pii/S0168169920302301
Factors affecting crop production (2021), https://www.agriculturewale.com/factors-affecting-crop-production/
Cedric, L.S., Adoni, W.Y.H., Aworka, R., Zoueu, J.T., Mutombo, F.K., Krichen, M., Kimpolo, C.L.M.: Crops yield prediction based on machine learning models: case of west African countries. Smart Agric. Technol. 2, 100049 (2022), https://www.sciencedirect.com/science/article/pii/S2772375522000168
Reddy, D., Kumar, M.R.: Crop yield prediction using machine learning algorithm. In: 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1466–1470 (2021)
Manivasagam, M.A., Sumalatha, P., Likitha, A., Pravallika, V., Satish, K.V., Sreeram, S.: An efficient crop yield prediction using machine learning. Int. J. Res. Eng. Sci. Manag. 5(3), 106–111 (2022), https://journals.resaim.com/ijresm/article/view/1862
Cubillas, J.J., Ramos, M.I., Jurado, J.M., Feito, F.R.: A machine learning model for early prediction of crop yield, nested in a web application in the cloud: a case study in an olive grove in southern Spain. Agriculture 12(9) (2022), https://www.mdpi.com/2077-0472/12/9/1345
Generalized linear model | what does it mean? (2022), https://www.mygreatlearning.com/blog/generalized-linear-models/
Garden, https://garden.org/
Determining corn growth stages (2020), https://www.krugerseed.com/en-us/agronomy-library/corn-growth-stages-and-gdu-requirements.html
Acknowledgements
This work has been supported by the project IoT-field: An Ecosystem of Networked Devices and Services for IoT Solutions Applied in Agriculture funded by European Union from the European Regional Development Fund.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kralj, I., Kusek, M., Jezic, G. (2023). Linear Machine Learning Algorithm for Early Annual Corn Yield Prediction. In: Jezic, G., Chen-Burger, J., Kusek, M., Sperka, R., Howlett, R.J., Jain, L.C. (eds) Agents and Multi-agent Systems: Technologies and Applications 2023. KES-AMSTA 2023. Smart Innovation, Systems and Technologies, vol 354. Springer, Singapore. https://doi.org/10.1007/978-981-99-3068-5_6
Download citation
DOI: https://doi.org/10.1007/978-981-99-3068-5_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3067-8
Online ISBN: 978-981-99-3068-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)