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

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.

A Multicultivar Approach for Grape Bunch Weight Estimation Using Image Analysis

“…Syrah. The good agreement between these bunch features and bunch weight was similar to the results presented in previous research [1,34], but, to the best of our knowledge, have never been used together in the same estimation model.…”

“…As mentioned before, all of the analyzed features could be collected from a vine image of a realistic, vineyard scenario [12,15,34,37]. However, we predict that the application of the studied approach in such conditions would be subject to several challenges/adaptations, such as (i) image resolution, which would be particularly important to extract features such as bunch perimeter and visible berries, as these require more detail and, thus, higher resolution if images are to be taken from a larger distance; (ii) bunch occlusion by leaves, where recent works have explored ways to estimate the occluded bunches [39,40], but this challenge still remains unsolved; (iii) extracting features from occluded bunches, as even if occluded bunches are estimated, it will be impossible to have their corresponding exact area, visible berries or perimeter, and ratios between these features on the visible portion of the bunches can be a better option; and (iv) robust segmentation methods, as this challenge lies in the step before the weight estimation (segmentation step), being crucial for a vineyard scenario.…”

Overcoming the challenge of bunch occlusion by leaves for vineyard yield estimation using image analysis

“…The first reason is, again, related to differences in bunch morphological traits (OIV, 2009). An example of such a trait is bunch compactness, which has been mentioned in several works as a possible cause of error in bunch weight estimation using image analysis (Aquino et al, 2018b;Hacking et al, 2020). Although this trait changes with different varieties, it can also change with different management practices, flowering and fruit-set conditions and even water availability (Tello and Ibáñez, 2018).…”

“…However, in natural conditions, in general, yield components are not all visible to the scanning sensors, requiring manual intervention (e.g., defoliation) or, alternatively, modelling approaches to estimate non-visible fruits. Regarding the most used yield components for yield estimations with image analysis, there are two main trends that have been explored in previous research: one uses the estimated number of visible berries in an image (Nuske et al, 2014b;Aquino et al, 2018a;Milella et al, 2019;Liu et al, 2020), the other uses bunch projected area or bunch pixels (Liu et al, 2013;Lopes et al, 2017;Hacking et al, 2020;Victorino et al, 2020).…”

Early yield prediction in different grapevine varieties using computer vision and machine learning

“…Grapevine yield estimation has been widely addressed using computer vision at different phenological stages, such as budbreak (Liu et al, 2017), flowering (Liu et al, 2018;Palacios et al, 2020), pea-size (Aquino et al, 2018;Liu et al, 2020;Palacios et al, 2021Palacios et al, , 2022, and harvest (Dunn & Martin, 2004;Liu et al, 2013Nuske et al, 2014b;Xin et al, 2020). A recent work (Hacking et al, 2020), concluded that the final stage of berry ripening was the ideal phenological stage for grapevine yield estimation. Information about the number of berries can lead to an adequate and early estimation of the yield in grapevine.…”