TIFF Image - 13.7 MB - MD5: edc68fe3fb04cb25dbdfc6349a9518af
Comparison of actual and predicted sodium (Na) content of organic amendment (OA) samples based on whole spectrum
forest regressions for DRIFT-MIR spectra from the Bruker Alpha KBr instrument at ICRAF. The dotted line indicates the expected 1:1 ratio forestimates and known values... |
Unknown - 14.7 MB - MD5: 98d025104365df48f79a66dca730d89e
Spectra are saved in this files in RDS format and they work with open-source code repositories (CloudCal and cloudFTIR, both open github projects). The CloudFTIR can be found at: https://github.com/leedrake5/cloudFTIR." |
Tab-Delimited - 2.9 MB - MD5: 8362ced45ea5d1bdce9773d08bb21e9a
MIR spectra of the manure standards using Alpha KBr instrument |
Tab-Delimited - 1.8 MB - MD5: b56570fc695ece2feb1d58627985846c
MIR spectra of the manure standards using Alpha ZnSe instrument |
Tab-Delimited - 27.2 MB - MD5: 07a83a619303878809426e2e7a3bb1c3
MIR Spectral data |
Unknown - 81.6 MB - MD5: a6b8d8504ce61253cae2079c2f3885ed
Using forest regression machine learning models, excellent calibration functions could be established using the MIR spectra. Forest models for there DRIFT-MIR spectra were run using 1500 trees with 200 resampling events using k-fold cross validation over 25 iterations based on th... |
Unknown - 29.3 MB - MD5: ac2c20646882bb5b387f26ed9ed53cae
Extreme gradient boosting machine learning (XGBoost) models were used with data from
DRIFT-MIR spectroscopy and these differ from forest in that trees can be weighted differently, have fixed depths, and different resembling. XGBoost (0.82.1) models were run using 400 rounds with... |
MS Excel Spreadsheet - 37.5 KB - MD5: 7574c100d79b5ec612c974d10515b353
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Jul 18, 2021 -
ICRAF-ISRIC Soil VNIR Spectral Library
Tab-Delimited - 932.4 KB - MD5: cf5a614f29869239d2eb635ce1b92ff0
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Jul 18, 2021 -
ICRAF-ISRIC Soil VNIR Spectral Library
Tab-Delimited - 741.3 KB - MD5: 246d11c640313ab7fc6a2e65b26ddcab
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