Research on Model Transfer Method of Organic Matter Content
Estimation of Different Soils Using VNIR Spectroscopy
HU Guo-tian1, 2, 3, SHANG Hui-wei1, 2, 3, TAN Rui-hong1, XU Xiang-hu1, PAN Wei-dong1
1. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, China
3. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, China
Abstract：Soil properties can be estimated accurately and quickly using visible and near-infrared (VNIR) diffuse reflectance spectroscopy. However, a key problem is the lack of universal nutrient content calibration models for different soils. To improve the universality of the soil organic matter (SOM) content calibration model for different types of soils and the speed of online detection of the SOM in farmland, sixty-six samples of soil from M107B in the United States were used to establish the SOM content. Calibration model using the particle swarm optimization-based least squares support vector machines (PSO-LSSVM) method using VNIR spectroscopy. Then this calibration model predicted 23 samples of the validation set from M107B. The results gave the coefficient of determination (R2) and the ratio of standard deviation to root mean square error of prediction (RPD) of 0.859 and 2.660, respectively. Subsequently, we predicted the SOM content of the validation set, including 20 samples from N116B, by the PSO-LSSVM calibration model of all 89 soil samples from M107B. The results showed decreases in the R2-value (0.562) and RPD (0.952). These decreases in R2 and RPD values by 34.6% and 64.2%, respectively, indicated that the prediction accuracy was significantly decreased when the PSO-LSSVM calibration model of SOM content in M107B was directly used to predict SOM content in N116B. The PSO-LSSVM calibration model established by the calibration set, a combination of some soil samples from N116B and all 89 samples from M107B was also used to predict SOM content of the previous validation set from N116B and gave the R2 values that were more than 0.80 and RPD values that were more than 2.0 when the number of soil samples from N116B was added over 35. In addition, R2 increased from 0.562 to 0.811. RPD increased from 0.952 to 2.274 when the number of soil samples from N116B added to the calibration set increased from 0 to 50. The results showed that calibration model accuracy could be effectively improved by adding some soil samples from N116B to M107B calibration set when predicting SOM content in N116B. The prediction performance of models was stable, whereas the prediction accuracy met practical requirements when the number of soil samples from N116B added to the calibration set was more than 50. In addition, the calibration model of SOM in M107B was successfully transferred to the soil in N116B, and the samples in N116B with large differences in organic matter content or spectral curve from samples in M107B are preferred to adding to the calibration set because this method can effectively avoid the mutation of model transfer performance. In conclusion, the results provided a method to improve the SOM prediction accuracy of N116B soil using the SOM calibration model of M107B soil. Furthermore, the results provided a new, economical and feasible model transfer method for real-time estimating of SOM content in farmland based on VNIR. The results also provided an effective solution to improve the universality of the SOM content calibration model for different soil types.
Key words：Visible and near-infrared spectroscopy; Precision agriculture; Soil organic matter; Particle swarm optimization-based least squares support vector machines; Model transfer
胡国田，尚会威，谭瑞虹，许翔虎，潘伟东. 不同土壤类型的有机质含量的可见-近红外光谱检测模型传递方法研究[J]. 光谱学与光谱分析, 2022, 42(10): 3148-3154.
HU Guo-tian, SHANG Hui-wei, TAN Rui-hong, XU Xiang-hu, PAN Wei-dong. Research on Model Transfer Method of Organic Matter Content
Estimation of Different Soils Using VNIR Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3148-3154.
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