Abstract
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.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by-nc-nd. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 2
Claim · model-written summary
The common structure produced 27 valid model combinations from four models.
- Taxon
- Tomato
Solanum lycopersicumt0 - Outcome
- valid model combinationse0
- Value
- 27 valid model combinationse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In this paper, 27 valid model combinations have been created from 4 models.”
t0“Tomato”
Claim · model-written summary
A selected model lowered RSME by 13% while raising computation time by 7.5% versus an original model.
- Taxon
- TomatoSolanum lycopersicumt0
- Outcome
- RSMEe0
- Direction
- decreasee0
- Value
- 13%e0
- Qualifier
- 7.5% increase in model computation timee0
- Comparator
- one of the original modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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.”
t0“Tomato”