Data and imagery for optimization of soil background removal to improve the prediction of wheat traits with UAV imagery

Published: 16 December 2022| Version 1 | DOI: 10.17632/6v8syv3khp.1
Contributors:
Miguel Quemada,
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Description

This dataset includes agronomic and spectral information from field experiments conducted in Aranjuez (Central Spain) with winter wheat (Triticum aestivum L.) in 2019. The total number of experiments was four and comprises 133 plots with various combinations of water and nitrogen applications. The agronomic data are summarized in an Excel file and contains the crop yield, protein content and N exported in the wheat grain (N output) at harvest in July. The Excel file includes also the average of the spectral indices (NDVI, MSAVI, NDRE and BRI) for each plot. The spectral indices were extracted from the images acquired with a multiespectral camera mounted on an unmanned aerial vehicle in March, April and May. The original images are available under reasonable request to the authors. More detail about these data can be found in the article entitled 'Optimization of soil background removal to improve the prediction of wheat traits with UAV imagery' published in Computers and Electronics in Agriculture by Almeida et al. in 2023.

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The yield data were obtained by harvesting the central fringe (1.4 m wide) of the plot with and experimental combine. A subsample of the harvested grain from each plot was taken to determine nitrogen concentration in the laboratory by the Dumas method. Protein content was obtained from the nitrogen concentration and the N output (kg N/ha) was calculated by multiplying the yield by the N concentration of the grain. The images were acquired with a multiespectral camera mounted on an unmanned aerial vehicle and had a ground sample distance of 4.65 cm/pixel. The UAV was equipped with a Micro-MCA multispectral sensor, Tetracam Inc. (Shenzhen and Chatsworth, CA, USA), a configurable camera matrix from 5.2 to 15.6 MP of six multispectral channels; the sensor systems installed are 1.3 MP CMOS sensors. The camera had six independent image sensors that captured narrow wavelength bands centered at blue (490 nm), green (550 nm), red (671 nm), red edge (700 nm), far red (760 nm), and near infrared (NIR; 800 nm) with a bandwidth of 10.0 ±2 nm. Reflectance was calibrated by an incident light sensor (ILS) integrated with the camera, which corrected the incident radiation for each shot in each band during the multispectral survey. The images were processed with PixelWrench2 for correction and calibration, and then Agisoft PhotoScan (St. Petersburg, Russia) was used to make the orthomosaic image of the entire plot. The ortho-image and R statistical software (version 4.0.3; R Core Team, 2021) were used to extract and calculate the spectral indices from the different experimental plots and to perform statistical analysis. The equations for calculating the spectral indices can be found in the Table 2 of the original article.

Institutions

Universidad Politecnica de Madrid

Categories

Remote Sensing, Precision Agriculture

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