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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| Language: | English |
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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 |
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| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2010.04.015 |
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| author | JIANG Lu-lu ZHANG Yu WANG Yan-yan TAN Li-hong HE Yong |
| author_facet | JIANG Lu-lu ZHANG Yu WANG Yan-yan TAN Li-hong HE Yong |
| author_sort | JIANG Lu-lu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8ffc76b054334abdb401244a1a0a9dbb |
| institution | Kabale University |
| issn | 1008-9209 2097-5155 |
| language | English |
| publishDate | 2010-07-01 |
| publisher | Zhejiang University Press |
| record_format | Article |
| series | 浙江大学学报. 农业与生命科学版 |
| spelling | doaj-art-8ffc76b054334abdb401244a1a0a9dbb2025-08-20T03:58:14ZengZhejiang University Press浙江大学学报. 农业与生命科学版1008-92092097-51552010-07-013644545010.3785/j.issn.1008-9209.2010.04.01510089209Fast determination of nutritional parameters in soil based on spectroscopic techniquesJIANG Lu-luZHANG YuWANG Yan-yanTAN Li-hongHE YongThe 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.https://www.academax.com/doi/10.3785/j.issn.1008-9209.2010.04.015spectroscopysoilnutritionartificial neural networkssupport vector machinespartial least squares analysis |
| spellingShingle | JIANG Lu-lu ZHANG Yu WANG Yan-yan TAN Li-hong HE Yong Fast determination of nutritional parameters in soil based on spectroscopic techniques 浙江大学学报. 农业与生命科学版 spectroscopy soil nutrition artificial neural networks support vector machines partial least squares analysis |
| title | Fast determination of nutritional parameters in soil based on spectroscopic techniques |
| title_full | Fast determination of nutritional parameters in soil based on spectroscopic techniques |
| title_fullStr | Fast determination of nutritional parameters in soil based on spectroscopic techniques |
| title_full_unstemmed | Fast determination of nutritional parameters in soil based on spectroscopic techniques |
| title_short | Fast determination of nutritional parameters in soil based on spectroscopic techniques |
| title_sort | fast determination of nutritional parameters in soil based on spectroscopic techniques |
| topic | spectroscopy soil nutrition artificial neural networks support vector machines partial least squares analysis |
| url | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2010.04.015 |
| work_keys_str_mv | AT jianglulu fastdeterminationofnutritionalparametersinsoilbasedonspectroscopictechniques AT zhangyu fastdeterminationofnutritionalparametersinsoilbasedonspectroscopictechniques AT wangyanyan fastdeterminationofnutritionalparametersinsoilbasedonspectroscopictechniques AT tanlihong fastdeterminationofnutritionalparametersinsoilbasedonspectroscopictechniques AT heyong fastdeterminationofnutritionalparametersinsoilbasedonspectroscopictechniques |