ArticleEcology and evolution2020
Field-based individual plant phenotyping of herbaceous species by unmanned aerial vehicle.
Article in Ecology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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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Who cites it
7 citing papers in PubMed.
- High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models.Plant phenomics (Washington, D.C.) · 2024Article
- UAV-based individual Chinese cabbage weight prediction using multi-temporal data.Scientific reports · 2023Article
- PhytoOracle: Scalable, modular phenomics data processing pipelines.Frontiers in plant science · 2023Article
- 3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping.Breeding science · 2022Article
- Recent developments and potential of robotics in plant eco-phenotyping.Emerging topics in life sciences · 2021Article
- GIS-Based Analysis for UAV-Supported Field Experiments Reveals Soybean Traits Associated With Rotational Benefit.Frontiers in plant science · 2021Article
- Field-based individual plant phenotyping of herbaceous species by unmanned aerial vehicle.Ecology and evolution · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in Unmanned Aerial Vehicle (UAVs) and image processing have made high-throughput field phenotyping possible at plot/canopy level in the mass grown experiment. Such techniques are now expected to be used for individual level phenotyping in the single grown experiment.We found two main challenges of phenotyping individual plants in the single grown experiment: plant segmentation from weedy backgrounds and the estimation of complex traits that are difficult to measure manually.In this study, we proposed a methodological framework for field-based individual plant phenotyping by UAV. Two contributions, which are weed elimination for individual plant segmentation, and complex traits (volume and outline) extraction, have been developed. The framework demonstrated its utility in the phenotyping of
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Registered trials
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