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Article

Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation

1
Department of Geography and Environmental Studies, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
2
Dipartimento di Scienze AgroAlimentari, Ambientali e Animali, University of Udine, Via delle Scienze 208, 33100 Udine, Italy
3
Department of Viticulture and Oenology, Faculty of AgriSciences, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
*
Author to whom correspondence should be addressed.
Received: 27 June 2019 / Revised: 10 August 2019 / Accepted: 20 August 2019 / Published: 22 August 2019
(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)
Vineyard 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. View Full-Text
Keywords: Kinect sensor; RGB; RGB-D; image segmentation; colour thresholding; bunch area; bunch volume; point cloud; mesh; surface reconstruction Kinect sensor; RGB; RGB-D; image segmentation; colour thresholding; bunch area; bunch volume; point cloud; mesh; surface reconstruction
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MDPI and ACS Style

Hacking, C.; Poona, N.; Manzan, N.; Poblete-Echeverría, C. Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation. Sensors 2019, 19, 3652. https://0-doi-org.brum.beds.ac.uk/10.3390/s19173652

AMA Style

Hacking C, Poona N, Manzan N, Poblete-Echeverría C. Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation. Sensors. 2019; 19(17):3652. https://0-doi-org.brum.beds.ac.uk/10.3390/s19173652

Chicago/Turabian Style

Hacking, Chris, Nitesh Poona, Nicola Manzan, and Carlos Poblete-Echeverría. 2019. "Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation" Sensors 19, no. 17: 3652. https://0-doi-org.brum.beds.ac.uk/10.3390/s19173652

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