Investigating 2-D and 3-D proximal remote sensing techniques for vineyard yield estimation

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dc.contributor.authorHacking, Chrisen_ZA
dc.contributor.authorPoona, Niteshen_ZA
dc.contributor.authorManzan, Nicolaen_ZA
dc.contributor.authorPoblete-Echeverria, Carlosen_ZA
dc.date.accessioned2021-11-09T13:54:20Z
dc.date.available2021-11-09T13:54:20Z
dc.date.issued2019-08-22
dc.descriptionCITATION: Hacking, C. et al. 2019. Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation. Sensors, 19(17). doi:10.3390/s19173652.
dc.descriptionThe original publication is available at https://www.mdpi.com/journal/sensors
dc.description.abstractVineyard yield estimation provides the winegrower with insightful information regarding the expected yield, facilitating managerial decisions to achieve maximum quantity and quality and assisting the winery with logistics. The use of proximal remote sensing technology and techniques for yield estimation has produced limited success within viticulture. In this study, 2-D RGB and 3-D RGB-D (Kinect sensor) imagery were investigated for yield estimation in a vertical shoot positioned (VSP) vineyard. Three experiments were implemented, including two measurement levels and two canopy treatments. The RGB imagery (bunch- and plant-level) underwent image segmentation before the fruit area was estimated using a calibrated pixel area. RGB-D imagery captured at bunch-level (mesh) and plant-level (point cloud) was reconstructed for fruit volume estimation. The RGB and RGB-D measurements utilised cross-validation to determine fruit mass, which was subsequently used for yield estimation. Experiment one’s (laboratory conditions) bunch-level results achieved a high yield estimation agreement with RGB-D imagery (r2 = 0.950), which outperformed RGB imagery (r2 = 0.889). Both RGB and RGB-D performed similarly in experiment two (bunch-level), while RGB outperformed RGB-D in experiment three (plant-level). The RGB-D sensor (Kinect) is suited to ideal laboratory conditions, while the robust RGB methodology is suitable for both laboratory and in-situ yield estimation.en_ZA
dc.description.urihttps://www.mdpi.com/1424-8220/19/17/3652
dc.description.versionPublisher’s version
dc.format.extent20 pages : illustrations (some color)en_ZA
dc.identifier.citationHacking, C. et al. 2019. Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation. Sensors, 19(17). doi:10.3390/s19173652.
dc.identifier.issn1424-8220 (online)
dc.identifier.otherdoi:10.3390/s19173652
dc.identifier.urihttp://hdl.handle.net/10019.1/123411
dc.language.isoen_ZAen_ZA
dc.publisherMDPI
dc.rights.holderAuthors retain copyright
dc.subjectKinect sensoren_ZA
dc.subjectRGBen_ZA
dc.subjectRGB-D (Kinect sensor)en_ZA
dc.subjectImage segmentationen_ZA
dc.subjectColour thresholdingen_ZA
dc.subjectBunch areaen_ZA
dc.subjectBunch volumeen_ZA
dc.subjectPoint clouden_ZA
dc.subjectMeshen_ZA
dc.subjectSurface reconstructionen_ZA
dc.subjectVineyards -- Yieldsen_ZA
dc.subjectRemote sensing -- Data processingen_ZA
dc.titleInvestigating 2-D and 3-D proximal remote sensing techniques for vineyard yield estimationen_ZA
dc.typeArticleen_ZA
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