Non-Destructive Infield Quality Estimation of Strawberries using Deep Architectures

Cees Jol; Junhan Wen; Jan van Gemert

doi:10.1109/ICCVW60793.2023.00058

Non-Destructive Infield Quality Estimation of Strawberries using Deep Architectures

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

Strawberries are profitable fruits, yet they have a short shelf life. Therefore, it is crucial to anticipate their quality and harvest them at the best time, which is vital not only for finding the appropriate market but also for minimizing food and economic waste. To this end, non-destructive strawberry quality measurements are useful. Much research is conducted on post-harvest strawberries: the fruits were only analyzed after harvesting and thus, these methods cannot be used to find a good time to harvest. Our research targets pre-harvest analysis for supporting the timing decisions of harvests. As such, we used an infield image dataset that was collected during the cultivation of strawberries. The images are labeled by quality assessments and measurements from post-harvest destructive tests. We evaluated deep learning for quality estimation and trained our algorithms to predict the ripeness, firmness, and sweetness of strawberries. Additionally, we applied depth estimation algorithms and shape inpainting models to estimate the size of strawberries using images. Our results demonstrate the feasibility of infield quality attribute prediction.

Original language	English
Title of host publication	Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Editors	Cristina Ceballos
Place of Publication	Piscataway
Publisher	IEEE
Pages	515-524
Number of pages	10
ISBN (Electronic)	979-8-3503-0744-3
ISBN (Print)	979-8-3503-0745-0
DOIs	https://doi.org/10.1109/ICCVW60793.2023.00058
Publication status	Published - 2023
Event	2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) - Paris, France Duration: 2 Oct 2023 → 6 Oct 2023

Conference

Conference	2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Country/Territory	France
City	Paris
Period	2/10/23 → 6/10/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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@inproceedings{620851e4f4ab40a3af96dd1aff0beea6,

title = "Non-Destructive Infield Quality Estimation of Strawberries using Deep Architectures",

abstract = "Strawberries are profitable fruits, yet they have a short shelf life. Therefore, it is crucial to anticipate their quality and harvest them at the best time, which is vital not only for finding the appropriate market but also for minimizing food and economic waste. To this end, non-destructive strawberry quality measurements are useful. Much research is conducted on post-harvest strawberries: the fruits were only analyzed after harvesting and thus, these methods cannot be used to find a good time to harvest. Our research targets pre-harvest analysis for supporting the timing decisions of harvests. As such, we used an infield image dataset that was collected during the cultivation of strawberries. The images are labeled by quality assessments and measurements from post-harvest destructive tests. We evaluated deep learning for quality estimation and trained our algorithms to predict the ripeness, firmness, and sweetness of strawberries. Additionally, we applied depth estimation algorithms and shape inpainting models to estimate the size of strawberries using images. Our results demonstrate the feasibility of infield quality attribute prediction.",

author = "Cees Jol and Junhan Wen and {van Gemert}, Jan",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) ; Conference date: 02-10-2023 Through 06-10-2023",

year = "2023",

doi = "10.1109/ICCVW60793.2023.00058",

language = "English",

isbn = "979-8-3503-0745-0",

pages = "515--524",

editor = "Ceballos, {Cristina }",

booktitle = "Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)",

publisher = "IEEE",

address = "United States",

}

Jol, C, Wen, J & van Gemert, J 2023, Non-Destructive Infield Quality Estimation of Strawberries using Deep Architectures. in C Ceballos (ed.), Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, Piscataway, pp. 515-524, 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France, 2/10/23. https://doi.org/10.1109/ICCVW60793.2023.00058

Non-Destructive Infield Quality Estimation of Strawberries using Deep Architectures. / Jol, Cees; Wen, Junhan ; van Gemert, Jan.
Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). ed. / Cristina Ceballos. Piscataway: IEEE, 2023. p. 515-524.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Non-Destructive Infield Quality Estimation of Strawberries using Deep Architectures

AU - Jol, Cees

AU - Wen, Junhan

AU - van Gemert, Jan

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

Y1 - 2023

N2 - Strawberries are profitable fruits, yet they have a short shelf life. Therefore, it is crucial to anticipate their quality and harvest them at the best time, which is vital not only for finding the appropriate market but also for minimizing food and economic waste. To this end, non-destructive strawberry quality measurements are useful. Much research is conducted on post-harvest strawberries: the fruits were only analyzed after harvesting and thus, these methods cannot be used to find a good time to harvest. Our research targets pre-harvest analysis for supporting the timing decisions of harvests. As such, we used an infield image dataset that was collected during the cultivation of strawberries. The images are labeled by quality assessments and measurements from post-harvest destructive tests. We evaluated deep learning for quality estimation and trained our algorithms to predict the ripeness, firmness, and sweetness of strawberries. Additionally, we applied depth estimation algorithms and shape inpainting models to estimate the size of strawberries using images. Our results demonstrate the feasibility of infield quality attribute prediction.

AB - Strawberries are profitable fruits, yet they have a short shelf life. Therefore, it is crucial to anticipate their quality and harvest them at the best time, which is vital not only for finding the appropriate market but also for minimizing food and economic waste. To this end, non-destructive strawberry quality measurements are useful. Much research is conducted on post-harvest strawberries: the fruits were only analyzed after harvesting and thus, these methods cannot be used to find a good time to harvest. Our research targets pre-harvest analysis for supporting the timing decisions of harvests. As such, we used an infield image dataset that was collected during the cultivation of strawberries. The images are labeled by quality assessments and measurements from post-harvest destructive tests. We evaluated deep learning for quality estimation and trained our algorithms to predict the ripeness, firmness, and sweetness of strawberries. Additionally, we applied depth estimation algorithms and shape inpainting models to estimate the size of strawberries using images. Our results demonstrate the feasibility of infield quality attribute prediction.

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BT - Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

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PB - IEEE

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T2 - 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Y2 - 2 October 2023 through 6 October 2023

ER -

Non-Destructive Infield Quality Estimation of Strawberries using Deep Architectures

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