{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T05:04:42Z","timestamp":1781327082529,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Rijeka","award":["uniri-drustv-18-122"],"award-info":[{"award-number":["uniri-drustv-18-122"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u2018Suncrest\u2019 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.<\/jats:p>","DOI":"10.3390\/s22155791","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T23:33:01Z","timestamp":1659569581000},"page":"5791","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9683-3204","authenticated-orcid":false,"given":"Dejan","family":"Ljubobratovi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Informatics and Digital Technologies, University of Rijeka, Radmile Matej\u010di\u0107 2, 51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7889-6888","authenticated-orcid":false,"given":"Marko","family":"Vukovi\u0107","sequence":"additional","affiliation":[{"name":"Division of Horticulture and Landscape Architecture, Department of Pomology, Sveto\u0161imunska cesta 25, University of Zagreb Faculty of Agriculture, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4079-4012","authenticated-orcid":false,"given":"Marija","family":"Brki\u0107 Bakari\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Digital Technologies, University of Rijeka, Radmile Matej\u010di\u0107 2, 51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6672-1834","authenticated-orcid":false,"given":"Tomislav","family":"Jemri\u0107","sequence":"additional","affiliation":[{"name":"Division of Horticulture and Landscape Architecture, Department of Pomology, Sveto\u0161imunska cesta 25, University of Zagreb Faculty of Agriculture, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4571-1546","authenticated-orcid":false,"given":"Maja","family":"Mateti\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Digital Technologies, University of Rijeka, Radmile Matej\u010di\u0107 2, 51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Layne, D.R., and Bassi, D. 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