DOI: 10.5061/dryad.9cnp5hqtr
title: Data from: Selection of appropriate multispectral camera exposure settings and radiometric calibration methods for applications in phenotyping and precision agriculture

description: The radiometric accuracy of multispectral images is crucial for
 quantitative precision agriculture and phenotyping applications. Very
 often the effect of exposure time and gain on radiometric accuracy is
 overlooked, and most application acquire images using the camera's
 auto-exposure settings. However, the large scene-to-scene variations in
 exposure time and gain in response to the reflective range of objects on
 ground significantly affects the radiometic accuracy of the auto-exposure
 images. On the other hand, the use of fixed-exposure settings result in
 spatiotemporally consistent radiometric measurements from multispectral
 images. This dataset will provide the necessary information to replicate
 the results of our published work (https://doi.org/10.1002/ppj2.70000),
 which includes studying (1) the variations in exposure settings for
 different objects in the scene, (2) potential radiometric errors when
 using auto-exposure settings, (3) the ideal exposure range  for
 the different multispectral bands, (4) the spaiotemporal radiometric
 accuracy of auto- and fixed-exposure settings, and (3) the performance of
 auto- and fixed-exposure settings for cotton nitrogen
 monitoring. 
description: The images were collected with a Micasense RedEdge-3
 multispectral camera mounted on a DJI Matrice-100 unmanned aerial vehicle.
 The script to process the data can be accessed from
 https://github.com/VaishSwami/Mutlispectral-camera-exposure-settings
description: # Selection of appropriate exposure settings for Micasense RedEdge cameras
 [https://doi.org/10.5061/dryad.9cnp5hqtr](https://doi.org/10.5061/dryad.9cnp5hqtr) ## Description of the data and file structure This dataset consists of Micasense RedEdge-3 multispectral images acquired from a UAV platform at 30 m above ground altitude. Its purpose was to assess the exposure settings appropriate for acquiring images of plants, soil, and other objects of interest in agriculture without radiometric distortion. This was done to facilitate the use of fixed exposure settings in multispectral data acquisition, as opposed to the conventionally used auto-exposure settings that is prone to radiometric inaccuracies. More details on the experiments can be found in [https://doi.org/10.1002/ppj2.70000](https://doi.org/10.1002/ppj2.70000). The dataset contains the following folders: 1. Object based exposure - contains raw images and processed files (.xlsx) for 17 June 2021 and 16 June 2022 to analyze the changes in exposure time and gain in an auto-exposure flight.  * Canopy subfolder - contains raw images that predominantly consisted of plant canopy pixels * Soil subfolder - contains raw images that predominantly consisted of soil pixels * Tarps subfolder - contains raw images that included calibration targets * Exposure variations-comprehensive.xlsx - metadata file that contains the image name, latitude, longitude, irradiance intercepted at the time of data capture, exposure time, gain, object type (Canopy, Soil, or Tarps), and multispectral band.  2. Cross-calibration dataset - contains raw images and processed data (.xlsx) to study potential radiometric errors from auto-exposure settings. * Sorted Samples-Batch X (X= 1 to 5) - raw images of in-field calibration targets acquired with different exposure settings for the blue, gree, red, red edge, and NIR bands (sub-folders).  * Sorted DLS Reflectance-Batch X (X= 1 to 5) - processed reflectance images of in-field calibration targets acquired with different exposure settings for the blue, green, red, red edge, and NIR bands (sub-folders).  * TarpRefl-Batch X.xlsx (X= 1 to 5) - metadata folder containing the gain, exposure time, image name,, uncalibrated (UNC) reflectance of the in-field reflectance targets, correction factor, calibrated reflectance of the infield targets, and ground truth (ASD) reflectance of the in-field targets. This data can be used to derive the ideal exposure settings for fixed-exposure flights.  * TarpRefl Regression piecewise-5Batches.xlsx - Contains the linear regression (LR) slope and intercept derived from all five batches combined.  * TarpRefl Cross Calibration MAPE-OELM 5Batches.xlsx - Contains the cross-calibration mean absolute percent error (MAPE) matrices for the five multispectral bands, which demonstrates the potential radiometric errors from calibration images with calibration equations derived from reference targets captured under different illumination conditions.  3. Radiometric accuracy targets - * TarpRefl-Fixed vs Auto-Date.xlsx - assess the radiometric accuracy (R^2 and MAPE) for fixed- and auto-exposure orthomosaics captured on 17 June 2021 and 16 June 2022 (separate files).  * Date.tif - orthomosaic to derive the different target reflectance values * Date_Tarps.shp - shape files to extract panel reflectance from orthomosaics. If the positions are off, they can be adjusted on ArcMap.  4. Vegetation Indices  * Vegetation Index-Fixed vs Auto-N plots gm2 - contains plot-wise total N(g/m2) values and vegetation indices derived from calibrated and uncalibrated fixed- and auto-exposure orthomosaics for 17 June 2021 and 16 June 2022.  Work referencing this dataset: Swaminathan, V.,Thomasson, J. A., Hardin, R. G., Rajan, N., & Raman,R. (2024). Selection of appropriate multispectralcamera exposure settings and radiometric calibrationmethods for applications in phenotyping and precisionagriculture. The Plant Phenome Journal, 7, e70000.[https://doi.org/10.1002/ppj2.70000](https://doi.org/10.1002/ppj2.70000) ## Code/software Codes: [https://github.com/VaishSwami/Mutlispectral-camera-exposure-settings](https://github.com/VaishSwami/Mutlispectral-camera-exposure-settings) Software: Python, ArcGIS, and Agisoft Metashape