| 光谱学与光谱分析 |
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| Applying Local Neural Network and Visible/Near-Infrared Spectroscopy to Estimating Available Nitrogen, Phosphorus and Potassium in Soil |
| WU Qian1, YANG Yu-hong2, XU Zhao-li2, JIN Yan2, GUO Yan1, LAO Cai-lian3* |
1. College of Resources and Environment, China Agricultural University, Beijing 100193, China
2. Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, China
3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China |
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Abstract To establish the quantitative relationship between soil spectrum and the concentration of available nitrogen, phosphorus and potassium in soil, the critical procedures of a new analysis method were examined, involving spectral preprocessing, wavebands selection and adoption of regression methods. As a result, a soil spectral analysis model was built using VIS/NIRS bands, with multiplicative scatter correction and first-derivative for spectral preprocessing, and local nonlinear regression method (Local regression method of BP neural network). The coefficients of correlation between the chemically determined and the modeled available nitrogen, phosphorus and potassium for predicted samples were 0.90, 0.82 and 0.94, respectively. It is proved that the prediction of local regression method of BP neural network has better accuracy and stability than that of global regression methods. In addition, the estimation accuracy of soil available nitrogen, phosphorus and potassium was increased by 40.63%, 28.64% and 28.64%, respectively. Thus, the quantitative analysis model established by the local regression method of BP neural network could be used to estimate the concentration of available nitrogen, phosphorus and potassium rapidly. It is innovative for using local nonlinear method to improve the stability and reliability of the soil spectrum model for nutrient diagnosis, which provides technical support for dynamic monitoring and process control for the soil nutrient under different growth stages of field-growing crops.
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Received: 2013-09-26
Accepted: 2014-01-20
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Corresponding Authors:
LAO Cai-lian
E-mail: laowan@cau.edu.cn
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