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Vineyard yield estimation is a fundamental aspect in precision viticulture that enables a better understanding of the inherent variability within a vineyard. Yield estimation conducted early in the growing season provides insightful information to ensure the best fruit quality for the maximum desired yield. Proximal sensing techniques provide non-destructive in situ data acquisition for yield estimation during the growing season. This study aimed to determine the ideal phenological stage for yield estimation using 2-dimensional (2-D) proximal sensing and computer vision techniques in a vertical shoot positioned (VSP) vineyard. To achieve this aim, multitemporal digital imagery was acquired weekly over a 12-week period, with a final acquisition two days prior to harvest. Preceding the multitemporal analysis for yield estimation, an unsupervised k-means clustering (KMC) algorithm was evaluated for image segmentation on the final dataset captured before harvest, yielding bunch-level segmentation accuracies as high as 0.942, with a corresponding F1-score of 0.948. The segmentation yielded a pixel area (cm2), which served as input to a cross-validation model for calculating bunch mass (g). The ‘calculated mass’ was linearly regressed against the ‘actual mass’, indicating the capability for estimating vineyard yield. Results of the multitemporal analysis showed that the final stage of berry ripening was the ideal phenological stage for yield estimation, achieving a global r2 of 0.790.

Vineyard yield estimation using 2-D proximal sensing: a multitemporal approach