ArticlePlant phenomics (Washington, D.C.)2020
Canopy Roughness: A New Phenotypic Trait to Estimate Aboveground Biomass from Unmanned Aerial System.
Article in Plant phenomics (Washington, D.C.), 2020. 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.
- 3DPotatoTwin: a paired potato tuber dataset for 3D multi-sensory fusion.Plant phenomics (Washington, D.C.) · 2025Article
- Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms.Frontiers in plant science · 2024Article
- Mapping rapeseed (Frontiers in plant science · 2024Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.Frontiers in plant science · 2023Article
- Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering.Frontiers in plant science · 2023Article
- Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income.Plant phenomics (Washington, D.C.) · 2023Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- The field phenotyping platform's next darling: Dicotyledons.Frontiers in plant science · 2022Review
- 4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography.Plant methods · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cost-effective phenotyping methods are urgently needed to advance crop genetics in order to meet the food, fuel, and fiber demands of the coming decades. Concretely, characterizing plot level traits in fields is of particular interest. Recent developments in high-resolution imaging sensors for UAS (unmanned aerial systems) focused on collecting detailed phenotypic measurements are a potential solution. We introduce canopy roughness as a new plant plot-level trait. We tested its usability with soybean by optical data collected from UAS to estimate biomass. We validate canopy roughness on a panel of 108 soybean [Glycine max (L.) Merr.] recombinant inbred lines in a multienvironment trial during the R2 growth stage. A senseFly eBee UAS platform obtained aerial images with a senseFly S.O.D.A. compact digital camera. Using a structure from motion (SfM) technique, we reconstructed 3D point clouds of the soybean experiment. A novel pipeline for feature extraction was developed to compute canopy roughness from point clouds. We used regression analysis to correlate canopy roughness with field-measured aboveground biomass (AGB) with a leave-one-out cross-validation. Overall, our models achieved a coefficient of determination (
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Registered trials
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