RESEARCH ON PREDICTION OF SOIL ORGANIC MATTER CONTENT BASED ON HYPERSPECTRAL IMAGING

Soil nutrient content is an important index to evaluate the growing environment of crops. Rapid access to soil nutrient information is an important requirement for the development of modern precision agriculture, while the detection of soil organic matter content is a necessary condition for understanding basic soil fertility and implementing crop precision cultivation. In this paper, the soil of rural fields in the southeast of Shanxi Province before sowing was taken as the research object. 111 soil samples to be tested were collected. After the process of drying, impurity removal, and grinding, the hyperspectral data of the Region of interest (ROI) of the samples were collected, and then the chemical determination of soil organic matter content was conducted. The original spectral data matrix was pretreated by numerical transformation operations, such as arithmetic mean, average deviation, 1st derivation, natural logarithm, and mixed multiplication, and a Partial least square regression (PLSR) quantitative analysis model was established. In these models, the obtained prediction set RP value under the pretreatment of F(A) *ln(AD) was the highest, reaching 0.8859. For spectral data preprocessed by F(A)*Ln(AD), the Competitive adaptive reweighted sampling (CARS) algorithm and Random frog (RF) algorithm were used to select key variables. The PLSR model was established by using F(A)*Ln(AD)&CARS data processing method and the RP value was increased to 0.9545. The prediction results can accurately reflect the real content of soil organic matter. The results of this study can provide theoretical support for the application of hyperspectral imaging technology in the determination of soil organic matter content, and provide a reference for the rapid detection of other soil components.