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“How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware

Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to...

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Detalles Bibliográficos
Autores principales: Wen, Junhan, Abeel, Thomas, de Weerdt, Mathijs
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10075323/
https://www.ncbi.nlm.nih.gov/pubmed/37035076
http://dx.doi.org/10.3389/fpls.2023.1160645
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author Wen, Junhan
Abeel, Thomas
de Weerdt, Mathijs
author_facet Wen, Junhan
Abeel, Thomas
de Weerdt, Mathijs
author_sort Wen, Junhan
collection PubMed
description Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to obtain quality estimations in a consistent and non-destructive manner. The majority of research on fruit quality measurements analyzed fruits in the lab with uniform data collection. However, it is laborious and expensive to scale up to the level of the whole yield. The “harvest-first, analysis-second” method also comes too late to decide to adjust harvesting schedules. In this research, we validated our hypothesis of using in-field data acquirable via commodity hardware to obtain acceptable accuracies. The primary instance that the research concerns is the sugariness of strawberries, described by the juice’s total soluble solid (TSS) content (unit: °Brix or Brix). We benchmarked the accuracy of strawberry Brix prediction using convolutional neural networks (CNN), variational autoencoders (VAE), principal component analysis (PCA), kernelized ridge regression (KRR), support vector regression (SVR), and multilayer perceptron (MLP), based on fusions of image data, environmental records, and plant load information, etc. Our results suggest that: (i) models trained by environment and plant load data can perform reliable prediction of aggregated Brix values, with the lowest RMSE at 0.59; (ii) using image data can further supplement the Brix predictions of individual fruits from (i), from 1.27 to as low up to 1.10, but they by themselves are not sufficiently reliable.
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spelling pubmed-100753232023-04-06 “How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware Wen, Junhan Abeel, Thomas de Weerdt, Mathijs Front Plant Sci Plant Science Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to obtain quality estimations in a consistent and non-destructive manner. The majority of research on fruit quality measurements analyzed fruits in the lab with uniform data collection. However, it is laborious and expensive to scale up to the level of the whole yield. The “harvest-first, analysis-second” method also comes too late to decide to adjust harvesting schedules. In this research, we validated our hypothesis of using in-field data acquirable via commodity hardware to obtain acceptable accuracies. The primary instance that the research concerns is the sugariness of strawberries, described by the juice’s total soluble solid (TSS) content (unit: °Brix or Brix). We benchmarked the accuracy of strawberry Brix prediction using convolutional neural networks (CNN), variational autoencoders (VAE), principal component analysis (PCA), kernelized ridge regression (KRR), support vector regression (SVR), and multilayer perceptron (MLP), based on fusions of image data, environmental records, and plant load information, etc. Our results suggest that: (i) models trained by environment and plant load data can perform reliable prediction of aggregated Brix values, with the lowest RMSE at 0.59; (ii) using image data can further supplement the Brix predictions of individual fruits from (i), from 1.27 to as low up to 1.10, but they by themselves are not sufficiently reliable. Frontiers Media S.A. 2023-03-22 /pmc/articles/PMC10075323/ /pubmed/37035076 http://dx.doi.org/10.3389/fpls.2023.1160645 Text en Copyright © 2023 Wen, Abeel and de Weerdt https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Wen, Junhan
Abeel, Thomas
de Weerdt, Mathijs
“How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware
title “How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware
title_full “How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware
title_fullStr “How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware
title_full_unstemmed “How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware
title_short “How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware
title_sort “how sweet are your strawberries?”: predicting sugariness using non-destructive and affordable hardware
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10075323/
https://www.ncbi.nlm.nih.gov/pubmed/37035076
http://dx.doi.org/10.3389/fpls.2023.1160645
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