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Estimation of yield and quality of legume and grass mixtures using partial least squares and support vector machine analysis of spectral data

Zhou, Zhenjiang and Morel, Julien and Parsons, David and Kucheryavskiy, Sergey V. and Gustavsson, Anne-Maj (2019). Estimation of yield and quality of legume and grass mixtures using partial least squares and support vector machine analysis of spectral data. Computers and Electronics in Agriculture. 162, 246-253
[Journal article]

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Official URL: https://doi.org/10.1016/j.compag.2019.03.038

Abstract

The project aim was to estimate N uptake (Nup), dry matter yield (DMY) and crude protein concentration (CP) of forage crops both during typical harvest times and at a very early developmental stage. Canopy spectral reflectance of legume and grass mixtures was measured in Sweden using a commercialized radiometer (400–1000 nm range). In total, 377 plant samples were tested in-situ in different grass and legume mixtures (6 grass species and 2 clover species) across two years, two locations and five N rates. Two mathematical methods, namely partial least squares(PLS) and support vector machine (SVM) were used to build prediction models between Nup, DMY and CP, and canopy spectral reflectance. Of the total 377 samples, 251 were randomly selected and used for calibration, and the remaining 126 samples were used as an independent dataset for validation. Results showed that the performance of SVM was better than PLS (based on mean absolute error (MAE) for both calibration and validation datasets) for the estimation of all investigated variables. Results for the validation set showed that the MAEs of PLS and SVM for Nup estimation were 17 and 9.2 kg/ha, respectively. The MAEs of PLS and SVM for DMY estimation were 587 and 283 kg/ha, respectively. The MAEs of PLS and SVM for CP estimation were 2.8 and 1.8%, respectively. In addition, a subsample, which corresponded to an early developmental stage, was analysed separately with PLS and SVM as for the whole dataset. Results showed that SVM was better than PLS for the estimation of all investigated variables. The high performance of SVM to estimate legume and grass mixture N uptake and dry matter yield could provide support for varying management decisions including fertilization and timing of harvest.

Authors/Creators:Zhou, Zhenjiang and Morel, Julien and Parsons, David and Kucheryavskiy, Sergey V. and Gustavsson, Anne-Maj
Title:Estimation of yield and quality of legume and grass mixtures using partial least squares and support vector machine analysis of spectral data
Year of publishing :2019
Volume:162
Page range:246-253
Number of Pages:8
Publisher:Elsevier
ISSN:0168-1699
Language:English
Publication Type:Journal article
Refereed:Yes
Article category:Scientific peer reviewed
Version:Accepted version
Full Text Status:Restricted
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Agricultural Science
Keywords:Dry matter yield, Forage crop, Grass, Hyperspectral reflectance, Nitrogen uptake, Nutritive value, Partial least squares, Red and white clover, Support vector machine
URN:NBN:urn:nbn:se:slu:epsilon-p-99969
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-99969
ID Code:16120
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Agricultural Research for Northern Sweden
(VH) > Dept. of Agricultural Research for Northern Sweden
Deposited By: SLUpub Connector
Deposited On:13 Jun 2019 10:19
Metadata Last Modified:13 Jun 2019 10:19

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