ArticleFrontiers in plant science2017
Quantitative Analysis of Cotton Canopy Size in Field Conditions Using a Consumer-Grade RGB-D Camera.
Article in Frontiers in plant science, 2017. 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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Who cites it
10 citing papers in PubMed.
- An intelligent method and platform for obtaining lettuce canopy coverage.Frontiers in plant science · 2026Article
- Drone Delivery of Insecticide Is Uneven Yet Sufficiently Controls Subterranean Weevils Infesting Sweet Potato Plants.Plants (Basel, Switzerland) · 2025Article
- Competition for Light Interception in Different Plant Canopy Characteristics of Diverse Cotton Cultivars.Genes · 2023Article
- Article
- Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images.Sensors (Basel, Switzerland) · 2022Article
- Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments.Sensors (Basel, Switzerland) · 2022Article
- Surface Defect Detection of Cabbage Based on Curvature Features of 3D Point Cloud.Frontiers in plant science · 2022Article
- A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots.Plant phenomics (Washington, D.C.) · 2022Review
- MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction.Plant phenomics (Washington, D.C.) · 2020Article
- Comparing RGB-D Sensors for Close Range Outdoor Agricultural Phenotyping.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant canopy structure can strongly affect crop functions such as yield and stress tolerance, and canopy size is an important aspect of canopy structure. Manual assessment of canopy size is laborious and imprecise, and cannot measure multi-dimensional traits such as projected leaf area and canopy volume. Field-based high throughput phenotyping systems with imaging capabilities can rapidly acquire data about plants in field conditions, making it possible to quantify and monitor plant canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze cotton canopy development in field conditions. A cotton field was planted with 128 plots, including four genotypes of 32 plots each. The field was scanned by GPhenoVision (a customized field-based high throughput phenotyping system) to acquire color and depth images with GPS information in 2016 covering two growth stages: canopy development, and flowering and boll development. A data processing pipeline was developed, consisting of three steps: plot point cloud reconstruction, plant canopy segmentation, and trait extraction. Plot point clouds were reconstructed using color and depth images with GPS information. In colorized point clouds, vegetation was segmented from the background using an excess-green (ExG) color filter, and cotton canopies were further separated from weeds based on height, size, and position information. Static morphological traits were extracted on each day, including univariate traits (maximum and mean canopy height and width, projected canopy area, and concave and convex volumes) and a multivariate trait (cumulative height profile). Growth rates were calculated for univariate static traits, quantifying canopy growth and development. Linear regressions were performed between the traits and fiber yield to identify the best traits and measurement time for yield prediction. The results showed that fiber yield was correlated with static traits after the canopy development stage (
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