A Preliminary Method for Tracking In-Season Grapevine Cluster Closure Using Image Segmentation and Image Thresholding

Mapping and monitoring cluster morphology provides insights for disease risk assessment, quality control in wine production, and understanding environmental influences on cluster shape. During the progression of grapevine morphology, cluster closure (CC) (also called bunch closure) is the stage when berries touch one another. This study used mobile phone images to develop a direct quantification method for tracking CC in three grapevine cultivars (Riesling, Pinot gris, and Cabernet Franc). A total of 809 cluster images from fruit set to veraison were analyzed using two image segmentation methods: (i) a Pyramid Scene Parsing Network (PSPNet) to extract cluster boundaries and (ii) Otsu’s image thresholding method to calculate % CC based on gaps between the berries. PSPNet produced high accuracy (mean accuracy = 0.98, mean intersection over union (mIoU) = 0.95) with mIoU > 0.90 for both cluster and noncluster classes. Otsu’s thresholding method resulted in <2% falsely classified gap and berry pixels affecting quantified % CC. The progression of CC was described using basic statistics (mean and standard deviation) and using a curve fit. The CC curve showed an asymptotic trend, with a higher rate of progression observed in the first three weeks, followed by a gradual approach towards an asymptote. We propose that the X value (in this example, number of weeks past berry set) at which the CC progression curve reaches the asymptote be considered as the official phenological stage of CC. The developed method provides a continuous scale of CC throughout the season, potentially serving as a valuable open-source research tool for studying grapevine cluster phenology and factors affecting CC.