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

Karampoiki, M., Todman, L. ORCID:, Mahmood, S., Murdoch, A. J., Hammond, J. ORCID:, 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

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To link to this item DOI: 10.3920/978-90-8686-947-3_53


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.

Item Type:Book or Report Section
Divisions:Life Sciences > School of Agriculture, Policy and Development > Department of Crop Science
ID Code:112540
Additional Information:14th European Conference on Precision Agriculture, ECPA Bologna 2023
Publisher:Wageningen Academic Publishers

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