Authors

Abstract

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 the 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.

Abstract in Chinese

土壤养分含量是评价作物生长环境的重要指标，能够快速获取土壤养分信息是发展现代精准农业重要要求，而对土壤有机质含量检测，是了解土壤基础地力，实施农作物精细化耕作必要条件。本文以晋东南地区农家田播种前土壤为研究对象，采集待测土壤样本111份，经晾晒、去杂及研磨等工序后采集样本兴趣区域(Region of interest，ROI)高光谱数据，随后进行土壤有机质含量化学测定。对采集到原始光谱数据矩阵进行均值、均差、求导、取自然对数及混合相乘等数值变换运算预处理，并建立土壤有机质偏最小二乘回归(Partial least square regression，PLSR)定量分析模型，其中运用均值一阶导数乘以均差值取自然对数(F(A)*ln(AD))建立PLSR模型这种预处理方式，所得预测集Rp值最高，达到0.8859。对F(A)*ln(AD)预处理下光谱数据分别采用竞争性自适应重加权采样法(Competitive adaptive reweighted sampling，CARS)及随机蛙跳(Random frog，RF)法选择特征波段处理，并建立偏PLSR模型，采用F(A)*ln(AD)&CARS数据处理方式建立的PLSR模型，Rp值提高为0.9545，预测结果能够较为准确反映土壤有机质真实含量水平。本研究结果可以为高光谱成像技术应用于土壤有机质含量检测提供理论支撑，并为土壤其他成分快速检测提供参考。