Multiple ortho‐mosaicking software pipelines produce comparable imagery‐derived wheat phenotypes

Abstract Unmanned aerial systems (UAS) equipped with multispectral and RGB sensors offer valuable data for monitoring crop health and assessing disease severity. However, the wide range of available photogrammetric software complicates software selection for high‐throughput plant phenotyping. This study compares the consistency of three widely used software packages, OpenDroneMap (ODM), Agisoft Metashape, and Pix4Dmapper in processing UAS‐acquired imagery for wheat ( Triticum aestivum L.) phenotyping. Over two seasons at Oklahoma State University research sites, imagery from a DJI Phantom 4 Pro Multispectral was used to generate eight vegetation indices (VIs), which were evaluated using correlation () and root mean square error (RMSE). Normalized VIs showed strong consistency across software, with values between 0.85 and 0.99 and RMSEs ranging from 0.004 to 0.07. Non‐normalized indices exhibited greater variability but retained high correlations ( > 0.76). Ground‐truth validation used single‐view imagery and disease severity ratings. Bayesian models quantified spectral measurement differences, their distributions (mean, standard deviation, skewness, and excess kurtosis) across processing approaches, and evaluated disease classification performance using ordinal logistic regression. Normalized VIs were highly consistent across software (posterior median differences <0.01 units, overlapping 95% highest density intervals), while single‐view imagery showed 15%–25% higher pixel‐level variability than software outputs. Non‐normalized indices showed greater processing sensitivity. RGB indices demonstrated near to perfect consistency. Disease classification accuracy ranged from 35% to 48% with minimal software differences (<2%). All three software produce biologically consistent results, ensuring stable genotype rankings regardless of processing choice. ODM performed comparably to proprietary alternatives while offering cost‐effectiveness, transparency, and reproducibility advantages.

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Publication Details

Journal
The Plant Phenome Journal
Published
2026-02-05
DOI
https://doi.org/10.1002/ppj2.70064
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Multiple ortho‐mosaicking software pipelines produce comparable imagery‐derived wheat phenotypes

Brett F. Carver, Phillip D. Alderman, Grishma Ojha
The Plant Phenome Journal
Remote Sensing in Agriculture
article

Multiple ortho‐mosaicking software pipelines produce comparable imagery‐derived wheat phenotypes

Brett F. Carver, Phillip D. Alderman, Grishma Ojha
article en

Abstract

Abstract Unmanned aerial systems (UAS) equipped with multispectral and RGB sensors offer valuable data for monitoring crop health and assessing disease severity. However, the wide range of available photogrammetric software complicates software selection for high‐throughput plant phenotyping. This study compares the consistency of three widely used software packages, OpenDroneMap (ODM), Agisoft Metashape, and Pix4Dmapper in processing UAS‐acquired imagery for wheat ( Triticum aestivum L.) phenotyping. Over two seasons at Oklahoma State University research sites, imagery from a DJI Phantom 4 Pro Multispectral was used to generate eight vegetation indices (VIs), which were evaluated using correlation () and root mean square error (RMSE). Normalized VIs showed strong consistency across software, with values between 0.85 and 0.99 and RMSEs ranging from 0.004 to 0.07. Non‐normalized indices exhibited greater variability but retained high correlations ( > 0.76). Ground‐truth validation used single‐view imagery and disease severity ratings. Bayesian models quantified spectral measurement differences, their distributions (mean, standard deviation, skewness, and excess kurtosis) across processing approaches, and evaluated disease classification performance using ordinal logistic regression. Normalized VIs were highly consistent across software (posterior median differences <0.01 units, overlapping 95% highest density intervals), while single‐view imagery showed 15%–25% higher pixel‐level variability than software outputs. Non‐normalized indices showed greater processing sensitivity. RGB indices demonstrated near to perfect consistency. Disease classification accuracy ranged from 35% to 48% with minimal software differences (<2%). All three software produce biologically consistent results, ensuring stable genotype rankings regardless of processing choice. ODM performed comparably to proprietary alternatives while offering cost‐effectiveness, transparency, and reproducibility advantages.

The Plant Phenome JournalVol. 9(1)
Oklahoma State University (US)
U.S. Department of Agriculture, National Institute of Food and Agriculture
Openalex Percentile: Top 69%
Remote Sensing in Agriculture
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