Assessment of grapevine bunch withering: advances in fruit 3D morphology and colour evaluation

Abstract

The agro-industrial sector is experiencing a new wave of innovation driven by goods-inspecting devices designed to optimise operations, improve product quality, and reduce yield losses. Grape withering is widely used to concentrate berry juice for raisin and sweet wine production. While this process alters wine characteristics, it also introduces costs and risks, as pathogen infections can compromise the quality of the final product. This study presents a reliable three-dimensional analysis for assessing grape colour and bunch morphology to evaluate infection risk and drying performance. Twenty Vitis vinifera bunches were dried under environmental conditions. Colour analysis focused on the distribution of colours in healthy versus rotten bunches. Three-dimensional digital replicas were generated with two methods: i) photogrammetry, and ii) a recently developed artificial intelligence model. The point clouds and meshes output from the two approaches were compared, and morphometric traits were directly measured, including volume, surface area, and both horizontal and vertical sections for each bunch. Key geometrical descriptors of the bunch's horizontal sections and individual berries were found to be relevant for classifying the risk of bunch rot. Additionally, morphometric traits related to bunch compactness were linked to drying speed. A linear model incorporating three-dimensional descriptors was developed to estimate weight loss during withering, achieving an R 2 value of 0.98 and a relative error of 0.07. The artificial intelligence-based technique produced lower-quality models for grape reconstruction, but the selected morphometric traits remained effective. • Grapevine bunches morphology evaluation through three-dimensional reconstruction. • Morphometric traits classified bunch dehydration speed and rot infection risk. • Colour analysis described withering progress and rotting spreading. • A fast artificial intelligence-based 3D acquisition technique was tested.

Plant phenotyping relevance

ブドウ房の3D画像再構成と色解析を用いて形態形質・感染リスク・乾燥性能を定量化し、 photogrammetryとAI手法を比較評価しているため、植物フェノタイピング手法が中心である。

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