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Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data

To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In this study, eight machine learning models were trained on a dataset containing data from 180 ‘Suncrest’...

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Autores principales: Ljubobratović, Dejan, Vuković, Marko, Brkić Bakarić, Marija, Jemrić, Tomislav, Matetić, Maja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371007/
https://www.ncbi.nlm.nih.gov/pubmed/35957349
http://dx.doi.org/10.3390/s22155791
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author Ljubobratović, Dejan
Vuković, Marko
Brkić Bakarić, Marija
Jemrić, Tomislav
Matetić, Maja
author_facet Ljubobratović, Dejan
Vuković, Marko
Brkić Bakarić, Marija
Jemrić, Tomislav
Matetić, Maja
author_sort Ljubobratović, Dejan
collection PubMed
description To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In this study, eight machine learning models were trained on a dataset containing data from 180 ‘Suncrest’ peaches. Before the models were trained, the dataset was subjected to dimensionality reduction using the least absolute shrinkage and selection operator (LASSO) regularization, and 8 input variables (out of 29) were chosen. At the same time, a subgroup consisting of the peach ground color measurements was singled out by dividing the set of variables into three subgroups and by using group LASSO regularization. This type of variable subgroup selection provided valuable information on the contribution of specific groups of peach traits to the maturity prediction. The area under the receiver operating characteristic curve (AUC) values of the selected models were compared, and the artificial neural network (ANN) model achieved the best performance, with an average AUC of 0.782. The second-best machine learning model was linear discriminant analysis with an AUC of 0.766, followed by logistic regression, gradient boosting machine, random forest, support vector machines, a classification and regression trees model, and k-nearest neighbors. Although the primary parameter used to determine the performance of the model was AUC, accuracy, F1 score, and kappa served as control parameters and ultimately confirmed the obtained results. By outperforming other models, ANN proved to be the most accurate model for peach maturity prediction on the given dataset.
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spelling pubmed-93710072022-08-12 Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data Ljubobratović, Dejan Vuković, Marko Brkić Bakarić, Marija Jemrić, Tomislav Matetić, Maja Sensors (Basel) Article To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In this study, eight machine learning models were trained on a dataset containing data from 180 ‘Suncrest’ peaches. Before the models were trained, the dataset was subjected to dimensionality reduction using the least absolute shrinkage and selection operator (LASSO) regularization, and 8 input variables (out of 29) were chosen. At the same time, a subgroup consisting of the peach ground color measurements was singled out by dividing the set of variables into three subgroups and by using group LASSO regularization. This type of variable subgroup selection provided valuable information on the contribution of specific groups of peach traits to the maturity prediction. The area under the receiver operating characteristic curve (AUC) values of the selected models were compared, and the artificial neural network (ANN) model achieved the best performance, with an average AUC of 0.782. The second-best machine learning model was linear discriminant analysis with an AUC of 0.766, followed by logistic regression, gradient boosting machine, random forest, support vector machines, a classification and regression trees model, and k-nearest neighbors. Although the primary parameter used to determine the performance of the model was AUC, accuracy, F1 score, and kappa served as control parameters and ultimately confirmed the obtained results. By outperforming other models, ANN proved to be the most accurate model for peach maturity prediction on the given dataset. MDPI 2022-08-03 /pmc/articles/PMC9371007/ /pubmed/35957349 http://dx.doi.org/10.3390/s22155791 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ljubobratović, Dejan
Vuković, Marko
Brkić Bakarić, Marija
Jemrić, Tomislav
Matetić, Maja
Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data
title Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data
title_full Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data
title_fullStr Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data
title_full_unstemmed Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data
title_short Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data
title_sort assessment of various machine learning models for peach maturity prediction using non-destructive sensor data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371007/
https://www.ncbi.nlm.nih.gov/pubmed/35957349
http://dx.doi.org/10.3390/s22155791
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