A Bayesian network approach for grain protein content prediction of winter wheat

Karampoiki, M., Todman, L. ORCID: https://orcid.org/0000-0003-1232-294X, Mahmood, S., Murdoch, A. J., Hammond, J. ORCID: https://orcid.org/0000-0002-6241-3551, Ranieri, E., Griepentrog, H.W. and Paraforos, D.S. (2023) A Bayesian network approach for grain protein content prediction of winter wheat. In: Stafford, J. V. (ed.) Precision agriculture '23. Wageningen Academic Publishers, pp. 429-434. ISBN 9789086863938 doi: 10.3920/978-90-8686-947-3_53

Abstract/Summary

Grain protein content is the most important indicator of wheat quality; it is affected by environmental conditions and agronomic practices. Thus, predictions at an early stage before harvest are crucial for farmers to decide their agronomic practices. This paper describes the development of a machine learning approach (MLA) based on the Bayesian networks (BNs) model to predict grain protein content using soil, topographic and yield data. The model has been developed using a Bayesian belief network software, categorising each node within each field based on the data available for a given field. The conditional interdependencies of these variables were learned using 75% of the data and then applied to 25% of the data to test the model. Grain protein content predictions were based on the probability of 50% chance of observing. The correlation between the predicted protein content and actual protein content was 0.40 and 0.48 for the German and UK test fields respectively.

Additional Information	14th European Conference on Precision Agriculture, ECPA Bologna 2023
Item Type	Book or Report Section
URI	https://reading-clone.eprints-hosting.org/id/eprint/112540
Item Type	Book or Report Section
Refereed	Yes
Divisions	Life Sciences > School of Agriculture, Policy and Development > Department of Crop Science
Additional Information	14th European Conference on Precision Agriculture, ECPA Bologna 2023
Publisher	Wageningen Academic Publishers
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