ArticleFrontiers in plant science2019
An Automatic Field Plot Extraction Method From Aerial Orthomosaic Images.
Article in Frontiers in plant science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- From plots to commercial fields: scalable, transferable cotton morphology and productivity estimation using functional growth proxies from UAV and PlanetScope time series.Plant phenomics (Washington, D.C.) · 2026Article
- Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation.Scientific reports · 2021Article
- Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method.Plant phenomics (Washington, D.C.) · 2021Article
- Semi-Automated Field Plot Segmentation From UAS Imagery for Experimental Agriculture.Frontiers in plant science · 2020Article
- Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping.Plant phenomics (Washington, D.C.) · 2019Article
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Authors and funding
2 authors.
Funding
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Abstract
Unmanned aerial vehicles have an immense capacity for remote imaging of plants in agronomic field research trials. Traits extracted from the plots can explain development of the plants coverage, growth, flowering status, and related phenomenon. An important prerequisite step to obtain such information is to find the exact position of plots to extract them from an orthomosaic image. Extraction of plots using tools which assume a uniform spacing is often erroneous because the plots may neither be perfectly aligned nor equally distributed in a field. A novel approach is proposed which uses image-based optimization algorithm to find the alignment of plots. The method begins with a uniformly spaced grid of plots which is iteratively aligned with regions of high vegetation index, i.e., the underlying plots. The approach is validated and tested on two different orthomosaic images of fields containing wheat plots with simulated and real alignment problems, respectively. The result of alignment is compared to manually located ground truth position of plots and the errors are quantitatively analyzed. The effectiveness of the proposed method is confirmed in accurately estimating the phenotypic trait of canopy coverage compared to the common methods of extraction from uniform grids or trimmed grids. The software developed in this study is available from SourceForge, https://sourceforge.net/projects/phenalysis/.
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