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

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dc.contributor.authorHacking, Chrisen_ZA
dc.contributor.authorPoona, Niteshen_ZA
dc.contributor.authorPoblete-Echeverria, Carlosen_ZA
dc.date.accessioned2022-01-24T08:59:29Z
dc.date.available2022-01-24T08:59:29Z
dc.date.issued2020-10-23
dc.descriptionCITATION: Hacking, C., Poona, N. & Poblete-Echeverria, C. 2020. Vineyard yield estimation using 2-D proximal sensing : a multitemporal approach. Oeno One, 4:793-812, doi:10.20870/oeno-one.2020.54.4.3361.
dc.descriptionThe original publication is available at https://oeno-one.eu
dc.description.abstractVineyard 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 across-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.en_ZA
dc.description.versionPublisher's version
dc.format.extent20 pages
dc.identifier.citationHacking, C., Poona, N. & Poblete-Echeverria, C. 2020. Vineyard yield estimation using 2-D proximal sensing : a multitemporal approach. Oeno One, 4:793-812, doi:10.20870/oeno-one.2020.54.4.3361.
dc.identifier.issn2494-1271 (online)
dc.identifier.otherdoi:10.20870/oeno-one.2020.54.4.3361
dc.identifier.urihttp://hdl.handle.net/10019.1/124134
dc.language.isoen_ZAen_ZA
dc.publisherInternational Viticulture and Enology Society
dc.rights.holderAuthors retain copyright
dc.subjectVineyardsen_ZA
dc.subjectComputer visionen_ZA
dc.subjectCrop estimatingen_ZA
dc.subjectPrecision viticultureen_ZA
dc.subjectImage segmentationen_ZA
dc.subjectRemote sensingen_ZA
dc.titleVineyard yield estimation using 2-D proximal sensing : a multitemporal approachen_ZA
dc.typeArticleen_ZA
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