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| Study on Soil Organic Matter Prediction Model Based on Moisture Correction Algorithm and Near Infrared Spectroscopy |
| HU Xiao-yan, CUI Xu, HAN Xiao-ping, ZHANG Zhi-yong, QIN Gang, SONG Hai-yan* |
| College of Engineering, Shanxi Agricultural University, Taigu 030801, China |
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Abstract Soil organic matter (SOM) is a necessary nutrient for plant growth and an important parameter for Soil property detection. Rapid and efficient acquisition of soil organic matter information is of great importance to the development of fine agriculture. Near infrared spectrum technology, which has the advantages such as rapidness and low cost, is widely applied to the measurement of soil organic matter, however, the soil moisture in the near infrared spectrum (780～2 500 nm), has a strong absorption properties in detection of soil organic matter formed certain interference. This study analyzed the characteristics of near-infrared absorbance spectra of 50 soil samples at different moisture contents (about 17%, 15%, 10%, 5%, and dry soil), and constructed MDI (Moisture determination index) using moisture sensitive bands 2 210, 1 415, and 1 929 nm. On this basis, soil samples with different moisture contents were reconstructed to eliminate the effect of water on the prediction model of soil organic matter. The results are as follows: (1) the absorbance spectrogram after MDI correction and reconstruction is similar to the corresponding absorbance spectrogram of dry soil samples, which can reflect the characteristics of dry soil samples. (2) By using Partial least square (Partial further squares, PLS) method to establish the dry soil organic matter of soil sample quantitative prediction model, and the reconstruction after the soil samples obtained from different moisture content prediction, the statistical parameters are: prediction correlation coefficient (RP) 0.90, standard error (SEP) 0.802 and the root mean square prediction error (RMSEP) 1.09; Compared with the original prediction results without MDI correction, the correlation coefficient increased by 0.032, the prediction standard error decreased by 0.113, and the prediction root mean square error decreased by 0.25. Results showed that the moisture correction algorithm proposed in this study can reduce the moisture content of soil organic matter prediction of interference, improve the use of dry soil of soil organic matter quantitative prediction model to predict the precision of different moisture content of soil samples, can be based on near infrared spectrum technology spread and provide theoretical basis for real-time measurement of soil organic matter.
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Received: 2018-03-08
Accepted: 2018-08-12
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Corresponding Authors:
SONG Hai-yan
E-mail: yybbao@163.com
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