How good are the models available for estimating sugar content in sugarcane?
2020
Oliveira, Monique Pires Gravina de | Antunes Rodrigues, Luiz Henrique | Rodrigues, Luiz Henrique Antunes
While sugarcane growth models could assist farmers in harvesting fields as close as possible to peak maturity, few of them are available to the industry. Even in those that are available, either there is room for improvement in sugar content estimation, or they were not properly assessed as to how they would perform given weather variability. In this work, we show that when developed with a dataset comprised of typical weather patterns, the outputs of empirical models that have been recently developed are qualitatively analogous to those of a process-based model and have smaller errors. However, when the training data is not representative, the same doesn’t happen and they are not consistent with known responses from sugarcane and with the output of mechanistic models. We used data from three years of harvests of a sugarcane mill to develop and evaluate the performance of machine learning models, as well as to evaluate an empirical model recently developed and DSSAT/Canegro. All models’ performances were evaluated in each of the three years separately, as well as through sensitivity analysis, to observe the effects of unknown weather in the estimates obtained by the model. This evaluation suggests that while machine learning techniques applied to industry data may be a promising tool for decision-makers, by themselves they are not capable of capturing all the effects that influence sucrose accumulation in sugarcane stalks.
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