Location-specific vs location-agnostic machine learning metamodels for predicting pasture nitrogen response rate
Abstract
In this work we compare the performance of a location- specific and a location-agnostic machine learning metamodel for crop nitrogen response rate prediction. We conduct a case study for grass- only pasture in several locations in New Zealand. We generate a large dataset of APSIM simulation outputs and train machine learning models based on that data. Initially, we examine how the models perform at the location where the location-specific model was trained. We then perform the Mann–Whitney U test to see if the difference in the predictions of the two models (i.e. location-specific and location-agnostic) is significant. We expand this procedure to other locations to investigate the generalization capability of the models. We find that there is no statistically significant difference in the predictions of the two models. This is both interesting and useful because the location-agnostic model generalizes better than the location-specific model which means that it can be applied to virgin sites with similar confidence to experienced sites.
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Published as:
C. Pylianidis,
V.Snow,
D. Holzworth,
J. Bryant,
I. N. Athanasiadis,
Location-specific vs location-agnostic machine learning metamodels for predicting pasture nitrogen response rate,
Lecture Notes in Computer Science (Pattern Recognition. ICPR International Workshops and Challenges), vol. 12666, pg. 96-109,
2021, Springer, doi:10.1007/978-3-030-68780-9_5.
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