Fast determination of nutritional parameters in soil based on spectroscopic techniques
The nutritional parameters (N, P and K) in two typical soils (red soil in Quzhou and purplish clayey soil in Haining) in Zhejiang Province were determined using near infrared (NIR) and middle infrared (MIR) spectroscopy. A total of 80 soil samples were collected, 60 (30 for each variety) samples of...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Zhejiang University Press
2010-07-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
| Subjects: | |
| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2010.04.015 |
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| Summary: | The nutritional parameters (N, P and K) in two typical soils (red soil in Quzhou and purplish clayey soil in Haining) in Zhejiang Province were determined using near infrared (NIR) and middle infrared (MIR) spectroscopy. A total of 80 soil samples were collected, 60 (30 for each variety) samples of which were used as calibration set, and the remaining 20 samples were used as validation set. After spectral scanning, partial least squares-least squares-support vector machine (PLS-LS-SVM) and partial least squares-back propagation neural networks (PLS-BP/ANN) were applied to develop the calibration models. The results indicated that both PLS-LS-SVM and PLS-BP/ANN achieved good prediction results, and PLS-LS-SVM were more suitable for small soil samples, both NIR and MIR achieved good prediction results for N detection by PLS-LS-SVM model with correlation coefficients r=0.876 and r=0.867, respectively. The MIR was better than NIR for the prediction of P and K, and the best results were obtained by PLS-LS-SVM model with r=0.938 for P and r=0.803 for K. It supplies a new way for the fast and accurate detection of nutritional parameters in soil. |
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| ISSN: | 1008-9209 2097-5155 |