ArticleSensors (Basel, Switzerland)2019
Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 43 citations in OpenAlex.
- Detection of Remaining Feed in the Feed Troughs of Flat-Fed Meat Ducks Based on the RGB-D Sensor and YOLO V8.Animals : an open access journal from MDPI · 2025Article
- Advances in viticultureFrontiers in plant science · 2024Article
- Analysis of Depth Cameras for Proximal Sensing of Grapes.Sensors (Basel, Switzerland) · 2022Article
- Behind the Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks.Frontiers in artificial intelligence · 2022Article
- Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards.Sensors (Basel, Switzerland) · 2021Article
- Editorial: Special Issue "Emerging Sensor Technology in Agriculture".Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Registered trials
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