Crop modelling is an essential part of biosystems engineering; selecting or developing a crop model for a specific application, having its requirements and desires, is difficult if not impossible without the required domain knowledge. This paper presents a fundamentally different model selection approach based on biological functionalities. This is enabled by a common structure, which allows for a combining of components, yielding new models. This increased design space allows the development of models which are better suited to the application than the original models. The use of a common structure, and its potential, are demonstrated by a use-case involving the selection of a tomato crop model for a model-based control application, but the rationales and methodologies can apply to other crops and applications as well. In this paper, 27 valid model combinations have been created from 4 models. In the use-case presented, the models are compared to data originating from a real system. The predictive performance of a model is quantified by the Root-Mean-Squared-Error (RSME) between the predictions and data. One trade-off is model accuracy versus computational speed. With the model set used, a 13% decrease in RSME was obtained by allowing a 7.5% increase in model computation time compared to one of the original models.
|Number of pages||11|
|Publication status||Published - 1 Nov 2019|
- Common structure
- Model selection
- Tomato crop growth models