Using Heterogeneous Model Ensembles to Improve the Prediction of Yeast Contamination in Peppermint

Research output: Contribution to conferencePaperpeer-review

Abstract

In this paper, we present an heterogeneous ensemble modeling approach to learn predictors for yeast contamination in freshly
harvested peppermint batches. Our research is based on data about numerous parameters of the harvesting process, such as planting,
tillage, fertilization, harvesting, drying, as well as information about microbial contamination. We use several different machine
learning methods, namely random forests, gradient boosting trees, symbolic regression by genetic programming, and support
vector machines to learn models that predict contamination on the basis of available harvesting parameters. Using those models
we form model ensembles in order to improve the accuracy as well as to reduce the false negative rate, i.e., to oversee as few
contaminations as possible. As we summarize in this paper, ensemble modeling indeed helps to increase the prediction accuracy
for our application, especially when using only the best models. The final prediction accuracy as well as other statistical indicators
such as false negative rate and false positive rate depend on the choice of the discrimination threshold; in the optimal case, model
ensembles are able to predict yeast contamination with 65.91% accuracy and only 19.15% of the samples are false negative, i.e.,
overseen contaminations.
Translated title of the contributionVerwendung heterogener Modell-Ensembles zur Verbesserung der Vorhersage von Hefekontaminationen in Pfefferminz
Original languageEnglish
Pages1194-1200
Number of pages7
DOIs
Publication statusPublished - 2022
EventInternational Conference on Industry 4.0 and Smart Manufacturing - Hagenberg, Austria
Duration: 17 Nov 202119 Nov 2021

Conference

ConferenceInternational Conference on Industry 4.0 and Smart Manufacturing
Abbreviated titleISM 2021
Country/TerritoryAustria
CityHagenberg
Period17.11.202119.11.2021

Keywords

  • herbs
  • heterogeneous model ensembles
  • machine learning
  • yeast contamination

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