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- W4382867030 abstract "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." @default.
- W4382867030 created "2023-07-02" @default.
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- W4382867030 date "2023-07-02" @default.
- W4382867030 modified "2023-09-27" @default.
- W4382867030 title "53. A Bayesian Network approach for grain protein content prediction of winter wheat" @default.
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- W4382867030 doi "https://doi.org/10.3920/978-90-8686-947-3_53" @default.
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