ArticleSensors (Basel, Switzerland)2016
Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
The trial behind it
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Who cites it
10 citing papers in PubMed.
- LiDAR Is Effective in Characterizing Vine Growth and Detecting Associated Genetic Loci.Plant phenomics (Washington, D.C.) · 2023Article
- Analysis of Depth Cameras for Proximal Sensing of Grapes.Sensors (Basel, Switzerland) · 2022Article
- A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots.Plant phenomics (Washington, D.C.) · 2022Review
- Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner.Sensors (Basel, Switzerland) · 2020Article
- Fluorescence-Sensor Mapping for the in Vineyard Non-Destructive Assessment of Crimson Seedless Table Grape Quality.Sensors (Basel, Switzerland) · 2020Article
- Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation.Sensors (Basel, Switzerland) · 2019Article
- A Method for Broccoli Seedling Recognition in Natural Environment Based on Binocular Stereo Vision and Gaussian Mixture Model.Sensors (Basel, Switzerland) · 2019Article
- High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation.Sensors (Basel, Switzerland) · 2018Article
- Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.GigaScience · 2017Review
- Phenoliner: A New Field Phenotyping Platform for Grapevine Research.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.