Postdoctoral Research Associate University of Wisconsin-Madison
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Remote sensing is revolutionizing the phenotyping of agricultural field trials, but for many researchers, the extraction of plot-level results is a bottleneck. We have developed the R package FIELDimageRas a user-friendly tool to analyze orthomosaic images containing many plots. The basic workflow involves cropping and rotating the image, followed by the creation of a shapefile based on the experimental design. The package includes functions to calculate the number of plants per plot, canopy cover percentage, vegetation indices, and plant height. FIELDimageRis being distributed under the GNU General Public License v2 at https://github.com/filipematias23/FIELDimageR. As an example, we illustrate key features using three different trials from the University of Wisconsin-Madison potato breeding program. The images were collected with a multi-rotor DJI Phantom-4 V2 PRO and the Sentera Multispectral Double 4K Sensor with flight altitude of 60 m above ground, 24 km h-1flight speed, and 75% image overlap. Pix4D software was used to make the orthomosaics. Traditionally, vine maturity of potato clones has been assessed by a visual rating (1–9) at 100 days after planting. In 2019, the broad-sense heritability (h2) of the visual rating ranged from 0.36 to 0.53 across the three trials. For the Russet and Chip trials, NDVI measurements improved h2by 0.13 and 0.31, respectively. The NDRE measurements had higher h2than the visual rating in all three trials (increases of 0.2, 0.2, and 0.3 for Reds, Chips, and Russets, respectively). These results suggest remote sensing can improve the reliability and efficiency of maturity assessment in potato. In conclusion, the FIELDimageRpackage offers plant scientists a convenient set of utilities to extract remote–sensing phenotypes from images of field trials.