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 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-serie

SUBMITTER: Daviet B 

PROVIDER: S-EPMC10720176 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature