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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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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. |
format | Online Article Text |
id | pubmed-10075323 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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