Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data

Abstract 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 measur...

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Autores principales:	Pietro Ibba, Christian Tronstad, Roberto Moscetti, Tanja Mimmo, Giuseppe Cantarella, Luisa Petti, Ørjan G. Martinsen, Stefano Cesco, Paolo Lugli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7c67d5a035204cbbac622f15df99398c
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spelling	oai:doaj.org-article:7c67d5a035204cbbac622f15df99398c2021-12-02T14:49:25ZSupervised binary classification methods for strawberry ripeness discrimination from bioimpedance data10.1038/s41598-021-90471-52045-2322https://doaj.org/article/7c67d5a035204cbbac622f15df99398c2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90471-5https://doaj.org/toc/2045-2322Abstract 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 $$_1$$ 1 , F $$_{0.5}$$ 0.5 and F $$_2$$ 2 -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.Pietro IbbaChristian TronstadRoberto MoscettiTanja MimmoGiuseppe CantarellaLuisa PettiØrjan G. MartinsenStefano CescoPaolo LugliNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pietro Ibba Christian Tronstad Roberto Moscetti Tanja Mimmo Giuseppe Cantarella Luisa Petti Ørjan G. Martinsen Stefano Cesco Paolo Lugli Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
description	Abstract 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 $$_1$$ 1 , F $$_{0.5}$$ 0.5 and F $$_2$$ 2 -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.
format	article
author	Pietro Ibba Christian Tronstad Roberto Moscetti Tanja Mimmo Giuseppe Cantarella Luisa Petti Ørjan G. Martinsen Stefano Cesco Paolo Lugli
author_facet	Pietro Ibba Christian Tronstad Roberto Moscetti Tanja Mimmo Giuseppe Cantarella Luisa Petti Ørjan G. Martinsen Stefano Cesco Paolo Lugli
author_sort	Pietro Ibba
title	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_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_sort	supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7c67d5a035204cbbac622f15df99398c
work_keys_str_mv	AT pietroibba supervisedbinaryclassificationmethodsforstrawberryripenessdiscriminationfrombioimpedancedata AT christiantronstad supervisedbinaryclassificationmethodsforstrawberryripenessdiscriminationfrombioimpedancedata AT robertomoscetti supervisedbinaryclassificationmethodsforstrawberryripenessdiscriminationfrombioimpedancedata AT tanjamimmo supervisedbinaryclassificationmethodsforstrawberryripenessdiscriminationfrombioimpedancedata AT giuseppecantarella supervisedbinaryclassificationmethodsforstrawberryripenessdiscriminationfrombioimpedancedata AT luisapetti supervisedbinaryclassificationmethodsforstrawberryripenessdiscriminationfrombioimpedancedata AT ørjangmartinsen supervisedbinaryclassificationmethodsforstrawberryripenessdiscriminationfrombioimpedancedata AT stefanocesco supervisedbinaryclassificationmethodsforstrawberryripenessdiscriminationfrombioimpedancedata AT paololugli supervisedbinaryclassificationmethodsforstrawberryripenessdiscriminationfrombioimpedancedata
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Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data

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