首页|Estimation of biomass and nutritive value of grass and clover mixtures by analyzing spectral and crop height data using chemometric methods
Estimation of biomass and nutritive value of grass and clover mixtures by analyzing spectral and crop height data using chemometric methods
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NSTL
Elsevier
? 2021 Elsevier B.V.The study aims to estimate forage yield and quality parameters by fusing field spectroscopy data and crop height with regression-based mathematical models. Field experiments were carried out to obtain canopy spectral reflectance (CSR) of grass and clover mixtures. Additionally, grass height (Hgrass) and clover height (Hclover) were used as auxiliary explanatory variables with CSR to estimate forage yield and quality. Variable importance in projection (VIP) was utilized for sensitive wavelength selection. Two chemometric methods, namely partial least squares regression (PLSR) and support vector machine (SVM), were implemented to build models using full spectra and sensitive wavelengths for estimating dry matter yield (DMY), in vitro true digestibility (IVTD), neutral detergent fiber (NDF), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), crude protein (CP), crude protein yield (CPY), and botanical composition (BC). Of the total 235 samples, 157 samples were randomly selected for model calibration while the remaining 78 samples were used for model validation. Results showed that both PLSR and SVM could reasonably estimate forage yield and quality variables, although performances of PLSR were more stable in terms of R2 and relative root mean square error (RRMSE) for both calibration and validation. Prediction performances of models using only full spectra data (PLSRspec) and models also using crop height information (PLSRspec+H) as model inputs were compared in this study. PLSRspec+H presented higher R2 and lower RRMSE than PLSRspec models (e.g. R2 improved from 0.83 to 0.90 for NDF and from 0.56 to 0.73 for IVTD, and RRMSE decreased from 8.14% to 6.58% for NDF and from 2.55% to 2.02% for IVTD). In addition, PLSR that used sensitive wavelengths and crop height (PLSRwave+H) as model inputs also had good performance, although slightly worse than PLSRspec+H. The results suggest that there is good potential to predict forage biomass and nutritive value by combining spectral and height variables with chemometric methods.
Crop heightField spectroscopyForage cropPartial least squares regressionSupport vector machineYield and quality
Sun S.、Cen H.、He Y.、Li X.、Zhou Z.、Zuo Z.、Yue W.、Morel J.、Parsons D.、Peng J.、Liu J.、Shi J.
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College of Biosystems Engineering and Food Science Zhejiang University
School of Agricultural Engineering Jiangsu University
College of Water Resources and Environmental Engineering Zhejiang University of Water Resources and Electric Power
Department of Agricultural Research for Northern Sweden Swedish University of Agricultural Sciences
The Agriculture and Rural Affairs Committee of Wuxi County
NSTL
Elsevier
