Digital Twins analysis as a tool to find new descriptors for grapevine bunch morphology categorisation and grey mould infection risk evaluation

Abstract

Botrytis cinerea is one of the most destructive diseases for Vitis vinifera, and grape bunch morphology plays a crucial role in grey mould infection. However, the common visual evaluation technique for assessing bunch compactness suffers from a lack of sensitivity and objectivity. This study proposes a standardised digital twin shape analysis to evaluate the morphology of grape bunches. Seventeen Pinot Gris and six Pinot Noir clones were considered. The grey mould severity was evaluated in the field. Fully ripened bunches (138) were gathered and then photographed at different angles. Digital twin reconstruction was carried out using the photogrammetry technique. Several measures and indices were extracted from each digital twin. Principal component analysis and multiple linear regression models were applied to identify the descriptors most related to grey mould symptoms. The results revealed that the most significant factors include the berries density, the estimated empty volume, and the bunch width. These results show that digital twins are a suitable tool for estimating grey mould infection risks. Two linear models, divided into 2D and 3D descriptor models, were proposed. The R-squared value and the root mean square error were compared between the models. For Pinot Gris, from the 2D to the 3D models, the R-squared value rose from 0.656 to 0.838, while the error decreased from 1.713 to 1.175. In Pinot Noir, the 2D model did not provide sufficient robustness, while the 3D model had an R-squared value of 0.936 and an error of 0.290.