高光谱成像的褐土土壤速效钾含量预测

doi:10.3964/j.issn.1000-0593(2019)05-1579-07

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Abstract Variable rate fertilization in precision agriculture depends on the understanding of the distribution of soil nutrients in the farmland. The rapid acquisition of soil information is the basis for the application of precision agriculture. Available potassium is an important parameter of soil fertility, and it is a necessary nutrient element for plant growth. The measurement of the content of available potassium in soil is an important way to understand the soil fertility, and it is a precondition of the realization of precision agriculture. In this paper, a total of 169 farmland plough cinnamon soil samples were collected in Shanxi province. All samples were air dried, with the larger soil particles crumbled and impurities removed manually and directly used for measuring soil near infrared hyperspectral without grinding and sieving. According to the measuring results of the available potassium content in the laboratory, all soil samples were divided into two parts. There were 144 samples of available potassium content less than 100 mg·kg^-1, and 108 samples were randomly selected as the low content modeling sets (L_c), and the remaining 36 samples as the low content validation sets (L_p). There were 25 samples of available potassium content more than 100 mg·kg^-1, and 19 samples were randomly selected as the high content modeling sets (H_c), and the remaining 6 samples as the high content validation sets (H_p). L_c and H_c were collectively known as all modeling sets (T_c), and L_p and H_p as all validation sets (T_p). Near infrared hyperspectral imaging technology was used to obtain near infrared hyperspectral images in the range of 950~1 650 nm of all soil samples. There were five different spectral data preprocessing methods used in this paper: the average spectral curve (R), the first derivative of the average spectral curve (FD), the average spectral curve and the first derivative co-modeled (R&FD), the product of the average spectral curve and the first derivative (R*FD) and the quotient of average spectral curve and first derivative (R/FD). Combined with partial least squares (PLS) method, the models were built using the modeling set T_c, L_c and H_c respectively. The validation sets T_p, L_p and H_p were verified respectively. The results showed that: along with the increase of available potassium content, the average spectral reflectance of soil increased first and then decreased. When the content of available potassium was less than 100 mg·kg^-1, the spectral reflectance of all bands increased with the increase of available potassium content. When the content of available potassium was between 100~200 mg·kg^-1, the spectral reflectance of all bands reached the maximum. When the available potassium content was more than 200 mg·kg^-1, the spectral reflectance of 950~1 400 nm decreased sharply, but the overall slope of the curve increased significantly. The higher the available potassium content was, the larger the overall slope of the curve was. When the content of available potassium was higher than 100 mg·kg^-1, the first derivative of the average spectral curve increased significantly, and increased with the increase of available potassium content. The PLS models proposed in this paper could predict the whole (all available potassium content) and high content (≥100 mg·kg^-1) of available potassium effectively; but could not predict the low content (≤100 mg·kg^-1) of available potassium. The best spectral preprocessing method was: R*FD, followed by FD and R. The predict results of R&FD and R/FD were relatively poor. The optimal modeling method was R*FD combined with T_c. The number of PLS principal factors was 2, RMSE_c=29.293, RPD_c=4.669, R²_c=0.956; RMSE_p=29.438，RPD_p=4.740，R²_p=0.958 for the validation sets of T_p; RMSE_p=23.033，RPD_p=3.199，R²_p=0.915 for the validation sets of H_p. This model could classify soil according to the content of available potassium. When the predicted value was less than 100 mg·kg^-1, it indicated that the content of available potassium in soil was less than 100 mg·kg^-1, and the specific content was uncertain; while when the predicted value was higher than 100 mg·kg^-1, the predicted value could reflect the real content of soil available potassium well. Because the soil samples selected in this paper were used without ground or sifted, the time of sample preparation could be greatly shortened and the prediction efficiency could be improved greatly. The results in this study can provide a reference for the rapid prediction of nutrients including available potassium content in cinnamon soil using near infrared hyperspectral imaging technology.

Key words：Near infrared; Hyperspectral imaging; Available potassium; Partial least square

Received: 2018-03-30 Accepted: 2018-08-12

ZTFLH:

O657.3

Corresponding Authors: ZHENG De-cong, DU Hui-ling E-mail: zhengdecong@126.com; duhuiling66@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)05-1579-07 OR https://www.gpxygpfx.com/EN/Y2019/V39/I05/1579