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Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data

Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured direct...

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Autores principales: Ibba, Pietro, Tronstad, Christian, Moscetti, Roberto, Mimmo, Tanja, Cantarella, Giuseppe, Petti, Luisa, Martinsen, Ørjan G., Cesco, Stefano, Lugli, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160339/
https://www.ncbi.nlm.nih.gov/pubmed/34045542
http://dx.doi.org/10.1038/s41598-021-90471-5
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author Ibba, Pietro
Tronstad, Christian
Moscetti, Roberto
Mimmo, Tanja
Cantarella, Giuseppe
Petti, Luisa
Martinsen, Ørjan G.
Cesco, Stefano
Lugli, Paolo
author_facet Ibba, Pietro
Tronstad, Christian
Moscetti, Roberto
Mimmo, Tanja
Cantarella, Giuseppe
Petti, Luisa
Martinsen, Ørjan G.
Cesco, Stefano
Lugli, Paolo
author_sort Ibba, Pietro
collection PubMed
description Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F[Formula: see text] , F[Formula: see text] and F[Formula: see text] -score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.
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spelling pubmed-81603392021-06-01 Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data Ibba, Pietro Tronstad, Christian Moscetti, Roberto Mimmo, Tanja Cantarella, Giuseppe Petti, Luisa Martinsen, Ørjan G. Cesco, Stefano Lugli, Paolo Sci Rep Article Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F[Formula: see text] , F[Formula: see text] and F[Formula: see text] -score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers. Nature Publishing Group UK 2021-05-27 /pmc/articles/PMC8160339/ /pubmed/34045542 http://dx.doi.org/10.1038/s41598-021-90471-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ibba, Pietro
Tronstad, Christian
Moscetti, Roberto
Mimmo, Tanja
Cantarella, Giuseppe
Petti, Luisa
Martinsen, Ørjan G.
Cesco, Stefano
Lugli, Paolo
Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
title Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
title_full Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
title_fullStr Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
title_full_unstemmed Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
title_short Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
title_sort supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160339/
https://www.ncbi.nlm.nih.gov/pubmed/34045542
http://dx.doi.org/10.1038/s41598-021-90471-5
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