Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
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 estima...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/17/3652 |
| _version_ | 1811297780104167424 |
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| author | Chris Hacking Nitesh Poona Nicola Manzan Carlos Poblete-Echeverría |
| author_facet | Chris Hacking Nitesh Poona Nicola Manzan Carlos Poblete-Echeverría |
| author_sort | Chris Hacking |
| collection | DOAJ |
| description | 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 (r<sup>2</sup> = 0.950), which outperformed RGB imagery (r<sup>2</sup> = 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. |
| first_indexed | 2024-04-13T06:09:18Z |
| format | Article |
| id | doaj.art-d87ac0d792e846c1bcafd58b023b6bd8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:09:18Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d87ac0d792e846c1bcafd58b023b6bd82022-12-22T02:59:07ZengMDPI AGSensors1424-82202019-08-011917365210.3390/s19173652s19173652Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield EstimationChris Hacking0Nitesh Poona1Nicola Manzan2Carlos Poblete-Echeverría3Department of Geography and Environmental Studies, Stellenbosch University, Private Bag X1, Matieland 7602, South AfricaDepartment of Geography and Environmental Studies, Stellenbosch University, Private Bag X1, Matieland 7602, South AfricaDipartimento di Scienze AgroAlimentari, Ambientali e Animali, University of Udine, Via delle Scienze 208, 33100 Udine, ItalyDepartment of Viticulture and Oenology, Faculty of AgriSciences, Stellenbosch University, Private Bag X1, Matieland 7602, South AfricaVineyard 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 (r<sup>2</sup> = 0.950), which outperformed RGB imagery (r<sup>2</sup> = 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.https://www.mdpi.com/1424-8220/19/17/3652Kinect sensorRGBRGB-Dimage segmentationcolour thresholdingbunch areabunch volumepoint cloudmeshsurface reconstruction |
| spellingShingle | Chris Hacking Nitesh Poona Nicola Manzan Carlos Poblete-Echeverría Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation Sensors Kinect sensor RGB RGB-D image segmentation colour thresholding bunch area bunch volume point cloud mesh surface reconstruction |
| title | Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation |
| title_full | Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation |
| title_fullStr | Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation |
| title_full_unstemmed | Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation |
| title_short | Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation |
| title_sort | investigating 2 d and 3 d proximal remote sensing techniques for vineyard yield estimation |
| topic | Kinect sensor RGB RGB-D image segmentation colour thresholding bunch area bunch volume point cloud mesh surface reconstruction |
| url | https://www.mdpi.com/1424-8220/19/17/3652 |
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