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

Chris Hacking,Carlos Poblete-Echeverria,Nitesh Poona

doi:10.20870/oeno-one.2020.54.4.3361

Abstract

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.

Highlights

Yield estimation is fundamental for precision viticulture practices, providing important information to both the vineyard manager and the winemaker (Nuske et al, 2014)
Due to the limitations of traditional methods, the combination of proximal sensing and computer vision techniques has been investigated as an alternative for yield estimation
This study investigated the use of 2-D proximal sensing and related computer vision techniques for yield estimation in a vertical shoot position (VSP) Shiraz vineyard, using multitemporal RGB data acquired weekly over a 12-week period

Summary

Introduction

Yield estimation is fundamental for precision viticulture practices, providing important information to both the vineyard manager and the winemaker (Nuske et al, 2014). Computer vision techniques – including segmentation, feature extraction and classification – are ideal for extracting information from raw datasets in agriculture (Mochida et al, 2018; Tian et al, 2020). Bunch area (cm2) was linearly regressed against the respective volume measurement (cm3) in the first cross-validation model. 2. The ‘fitted’ volume (cm3) values were linearly regressed against the actual mass (g) in a subsequent cross-validation model, yielding estimated mass (g) from the ‘fitted values’. The ‘fitted’ volume (cm3) values were linearly regressed against the actual mass (g) in a subsequent cross-validation model, yielding estimated mass (g) from the ‘fitted values’ This step was justified by the established relationship between bunch mass and volume presented in Hacking et al (2019)

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Conclusion