DOI: 10.5061/dryad.rn8pk0pnm
title: Data from: Evaluating UAV captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut

description: Leaf spot is a devastating disease in cultivated peanut (Arachis hypogaea
 L.) that can lead to significant yield losses without chemical controls.
 Multiple disease symptoms, two causal organisms, inconsistent testing
 environments, and genotype-by-environment interactions are all components
 which make breeding for leaf spot resistant peanuts challenging. To better
 understand this disease, and make gains in breeding for disease
 resistance, an accurate and objective phenotyping strategy must be
 implemented. In this work, data derived from leaf scans, UAV-captured RGB,
 and multispectral imagery were evaluated as a replacement for the
 subjective visual rating scale used at present. Standard operating
 procedures are detailed for all digital methods evaluated in this paper,
 and all digital phenotypes are fully characterized with descriptive
 statistics. Feature importance and post hoc proof of concept studies are
 conducted to further evaluate the new digital methods. Ultimately,
 ‘Visible Atmospherically Resistant Index’ or VARI was selected as the most
 appropriate proxy for visual ratings and should be deployed by researchers
 and plant breeders in the peanut community for the objective evaluation of
 leaf spot resistance.
description: <strong>Germplasm</strong> A
 population of 264 allotetraploid peanut genotypes were selected for this
 study because of their value to the North Carolina State University (NCSU)
 Peanut Breeding and Genetics (PB&G) program. The population
 consists of 12 NCSU PB&G cultivars, 165 breeding lines, 85 lines
 with wild species introgressions, and 2 plant introduction lines
 (Supplemental Table 1). By choosing advanced and highly related genotypes,
 any findings from this study can be quickly incorporated into PB&G
 breeding activities. Moreover, the 264 peanut lines represent a gradient
 of resistant to susceptible phenotypes in response to leaf spot disease
 pressure. <strong>Field
 Design</strong> The population of 264 genotypes
 was evaluated at the Peanut Belt Research Station (PBRS,
 Lewiston-Woodville, NC) and the Upper Coastal Plains Research Station
 (UCPRS, Rocky Mount, NC), over the summers of 2021 and 2022; creating 4
 unique combinations of year and location, hereafter referred to as
 experiments. Each experiment had two replications and each replication was
 planted in a randomized complete block design. Each genotype was planted
 as a two-row plot with the following dimensions: 90 cm row centers and 7.3
 meter row length. An alleyway of 2.1 meters separated the ranges. Besides
 the absence of fungicides, fields were managed with standard cultural
 practices for the region. To facilitate georeferencing, within 30 days
 after planting, 6 to 9 ground control points were installed in the field
 in a uniform manner. Ground control points were constructed of Polyvinyl
 Chloride (PVC) pipe of 5.08 cm diameter and 100 cm length with a Polyvinyl
 Chloride 5.08 cm cap. The pipe was driven into the soil until the cap was
 flush with the ground surface. An Emlid Reach RS2 (Emlid, Hong Kong,
 China) Real-Time Kinematic (RTK) receiver was used to measure the x, y,
 and z coordinates of each ground control point.
 <strong>Phenotyping Pipelines</strong>
 The phenotypes presented in this study were collected when
 peanuts were in growth stage R8 (Subrahmanyam et al., 1995). For the
 calculation of defoliation only, the UAV was flown at R7 to capture change
 relative to the R8 stage. Visual ratings, leaf scans and UAV data were all
 captured within 48 hours of each other. Early and Late Leaf Spot were
 rated as one composite disease. <strong>Visual
 Ratings</strong> All visual ratings were
 collected by the same experienced rater. Visual ratings were collected on
 leaf spot symptoms using the modified 9-point scale as presented by
 (Subrahmanyam et al., 1995) in every plot of every experiment.
 <strong>Low Throughput Leaf Scans</strong>
 For every genotype, eight leaves (equaling 32 leaflets) were
 randomly selected from non-border vegetation. Leaves were placed in whirl
 packs from Weber Scientific (Hamilton, NJ) and refrigerated until
 scanning. Leaves were scanned within seven days of field collection to
 avoid desiccation and discoloration. Leaflets were arranged so that no
 overlapping occurred and the adaxial side of the leaf was scanned in color
 with an Epson DS-50000 flatbed Scanner (Los Alamitos, CA). Lesion count
 and lesion area on a plot-basis were calculated in Python version 3.6.10
 using Supplemental Script 1. <strong>UAV-Captured
 RGB Images</strong> Before each flight, 15 cm
 square PVC targets that were colored in a black and white checker pattern,
 were centered over all RTK-surveyed ground control points present in the
 field. A DJI (Shenzhen, China) Matrice 300 RTK UAV was flown with a DJI
 Zenmuse P1 35mm lens to capture true color Red Green Blue (RGB) images.
 The UAV was flown at an altitude of 30 meters, with a speed of 7.8 miles
 per hour (ground sample distance of 0.38 cm/px). The DJI Pilot flight plan
 ensured 80/80 image overlap to facilitate image stitching. Images from
 each flight were imported into Metashape version 1.6 (St. Petersburg,
 Russia). Using the default workflow, images were aligned, a dense point
 cloud and digital surface model were built, and the orthomosaic was
 created. The known RTK positions of the ground control points were
 imported and used to geometrically rectify the orthomosaic product.
 Orthomosaics were exported into the EPSG:2264 projected coordinate system
 and imported into ArcGIS Pro (Esri, Redlands, California) version 2.3.0
 for plot segmentation, plot labeling, removing soil pixels, calculation of
 percentage defoliation, and extracting reflectance values on a plot-basis
 (Supplemental Note 1). <strong>UAV-Captured
 Multispectral Images</strong> In a separate
 flight from the RGB image collection, the same DJI Matrice 300 RTK UAV was
 flown with a 10-band RedEdge-MX dual sensor from Micasense (Seattle,
 WA). Figure 1 shows the wavelength location and width captured by each
 band. Immediately before each UAV flight, a Micasense RP06 calibrated
 reflectance panel was imaged with the RedEdge-P dual sensor. The UAV was
 flown at an altitude of 40 meters, with a speed of 7.8 miles per hour
 (ground sample distance of 2.73 cm/px). The DJI Pilot flight plan ensured
 80/80 image overlap to facilitate image stitching. Metashape version 1.6
 was used as in section 2.2.3, with the following modification; images were
 color calibrated to produce an absolute surface reflectance using the
 preflight image of the calibrated reflectance panel prior to image
 alignment. ArcGIS Pro version 2.3.0 was used for plot segmentation, plot
 labeling, removing soil pixels and extracting reflectance values on a
 plot-basis (Supplemental Note 1). In Python version 3.6.10 with
 statsmodels version 0.13.2, analysis of variance was used to determine if
 any of the nine classes of visual ratings were significantly different at
 the ten wavelengths recorded by the multispectral camera.
 <strong>Multispectral and RGB based Vegetation
 Indices</strong> Digital Numbers (DNs)
 representing reflectance were calculated and summarized on a plot mean
 basis from the UAV imagery (section 2.2.3 and 2.2.4). These values were
 read into Python version 3.6.10 for Vegetation Index (VI) calculation
 (Supplemental Script 2). Table 1 lists all VIs calculated and the
 associated bands used (Blackburn, 1998; Broge & Leblanc, 2001;
 Chappelle et al., 1992; Daughtry et al., 2000; Elvidge & Chen,
 1995; Gitelson et al., 2002; Gitelson & Merzlyak, 1994; Haboudane
 et al., 2004; Huete et al., 2002; Huete, 1988; Jordan, 1969).
description: # Supplementary raw dataset from: Evaluating UAV captured RGB and
 multispectral imagery as a proxy for visual rating of leaf spot in
 cultivated peanut Dataset DOI:
 [10.5061/dryad.rn8pk0pnm](10.5061/dryad.rn8pk0pnm) ## Description of the
 data and file structure #### File:
 Supplemental\_Data\_Raw\_Phenotypic.xlsx ##### Variables * UNIQ_ID -
 Genotype of peanut * Location - 1 = Peanut Belt Research Station (PBRS),
 Lewiston-Woodville, NC, 2 = Upper Coastal Plains Research Station (UCPRS),
 Rocky Mount, NC * Year - a unit of time measuring the number of times the
 Earth has revolved around the sun in the Common Era (CE) of the Gregorian
 calendar. One year is roughly equivalent to 365 and ¼ days. * PBGID - Plot
 Number * LastRate_DAP - The number of days after planting (DAP) the last
 visual rating was performed. * VisualStand - Visual stand count, the
 number of plants that germinated in each plot  * RGBFirstArea - Percent of
 the 1st image that is foliage, taken with the RGB sensor * RGBLastArea -
 Percent of the last image that is foliage, taken with the RGB sensor *
 RGB_Defol - Percent defoliation, calculated as (RGBFirstArea -
 RGBLastArea) / RGBFirstArea x 100 * RGB_R/G/B - Intensity of the
 red/green/blue light channel in the RGB color model. Each channel uses 8
 bits (or 1 byte) to store its color value. Eight bits can represent 256
 different binary values ranging from 00000000 to 11111111 which, when
 converted to their decimal equivalent, range from 0 to 255. Zero
 represents the lowest intensity of the color (i.e. black) and 255
 represents the highest intensity (i.e. the pure red/green/blue color). *
 RD\_ A/BVW_XYZ - RD refers to the MicaSense RedEdge-MX Dual (RD) camera
 system mounted on the drone, A or B is a way to group the bands to the two
 sensors that are part of the dual system, VW is a two-digit number
 corresponding to the band width in nanometers (nm), XYZ is a three-digit
 number corresponding to the band center in nm * VisualLast - Last human
 visual rating score. * LS_PLA - Leaf spot percent lesion area, percentage
 of the leaf surface that is covered by a leaf spot lesion as determined
 from the hand collected and scanned leaves. * LS_CPL - Leaf spot lesion
 count per leaf (each leaf consists of four leaflets) as determined from
 the hand collected and scanned leaves. ## Code/software **Description of
 Code/Software** Supplemental Script 1. Python jupyter notebook for
 calculating leaf spot lesion count and area. This script reads in joint
 photographic expert group image (.jpg) files from a folder. Then lesion
 area and average lesion count per leaf is written out to an excel file.
 Supplemental Script 2. Python jupyter notebook for calculating vegetative
 indices on a per-plot basis. The script reads in a flat file of mean band
 values per plot. It uses standard formulas to calculate vegetative indices
 and write out a new flat file. The Supplemental Data Raw Phenotypic.xslx
 can be used as an example input to Supplemental Script 2. See GitHub
 Repository for additional details.
 [https://github.com/cassondranewman/LS-imagery/tree/main/Supplementary_Material](https://github.com/cassondranewman/LS-imagery/tree/main/Supplementary_Material) ## Access information Other publicly accessible locations of the data: * [https://github.com/cassondranewman/LS-imagery/tree/main/Supplementary_Material](https://github.com/cassondranewman/LS-imagery/tree/main/Supplementary_Material)