Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images

Abstract

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, as well as its impacts on both vine yield and wine quality, remain nearly unknown. To address these limitations, we propose to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. Result 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. 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 genotype, 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.

Plant phenotyping relevance

ブドウ果粒の検出・セグメンテーション・時系列追跡を開発し、ロボット型高スループット表現型解析基盤で検証して、個別果粒の成長・着色形質を抽出する方法が中心である。

Code and data availability

The paper describes an open-source berry detection/segmentation/tracking pipeline and image datasets from PhenoArch, but no supplied block contains an authors' public URL, repository, or deposit for the code, trained models, annotations, or image data. The only URL present is the PhenoArch platform facility page, which

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