Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images
Abstract Background Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heter...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | Plant Methods |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13007-023-01125-8 |
| _version_ | 1797388249137676288 |
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| author | Benoit Daviet Christian Fournier Llorenç Cabrera-Bosquet Thierry Simonneau Maxence Cafier Charles Romieu |
| author_facet | Benoit Daviet Christian Fournier Llorenç Cabrera-Bosquet Thierry Simonneau Maxence Cafier Charles Romieu |
| author_sort | Benoit Daviet |
| collection | DOAJ |
| description | Abstract Background Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, remain nearly unknown. To address these limitations, we propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. Results First, a deep-learning approach is used to detect berries with at least 50 ± 10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day. Conclusions We successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images acquired in laboratory conditions. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one cultivar, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift. |
| first_indexed | 2024-03-08T22:38:04Z |
| format | Article |
| id | doaj.art-22639a9aa94a49ca9f8fc33647829a10 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-03-08T22:38:04Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-22639a9aa94a49ca9f8fc33647829a102023-12-17T12:18:56ZengBMCPlant Methods1746-48112023-12-0119111610.1186/s13007-023-01125-8Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse imagesBenoit Daviet0Christian Fournier1Llorenç Cabrera-Bosquet2Thierry Simonneau3Maxence Cafier4Charles Romieu5LEPSE, Univ Montpellier, INRAE, Institut AgroLEPSE, Univ Montpellier, INRAE, Institut AgroLEPSE, Univ Montpellier, INRAE, Institut AgroLEPSE, Univ Montpellier, INRAE, Institut AgroAGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut AgroAGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut AgroAbstract Background Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, remain nearly unknown. To address these limitations, we propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. Results First, a deep-learning approach is used to detect berries with at least 50 ± 10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day. Conclusions We successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images acquired in laboratory conditions. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one cultivar, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift.https://doi.org/10.1186/s13007-023-01125-8High-throughput phenotypingComputer visionGrapevine berryFruit detectionFruit segmentationTracking |
| spellingShingle | Benoit Daviet Christian Fournier Llorenç Cabrera-Bosquet Thierry Simonneau Maxence Cafier Charles Romieu Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images Plant Methods High-throughput phenotyping Computer vision Grapevine berry Fruit detection Fruit segmentation Tracking |
| title | Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images |
| title_full | Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images |
| title_fullStr | Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images |
| title_full_unstemmed | Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images |
| title_short | Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images |
| title_sort | ripening dynamics revisited an automated method to track the development of asynchronous berries on time lapse images |
| topic | High-throughput phenotyping Computer vision Grapevine berry Fruit detection Fruit segmentation Tracking |
| url | https://doi.org/10.1186/s13007-023-01125-8 |
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