Nie Pengcheng, Dong Tao, He Yong, Qu Fangfang
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China.
Sensors (Basel). 2017 May 11;17(5):1102. doi: 10.3390/s17051102.
Soil nitrogen content is one of the important growth nutrient parameters of crops. It is a prerequisite for scientific fertilization to accurately grasp soil nutrient information in precision agriculture. The information about nutrients such as nitrogen in the soil can be obtained quickly by using a near-infrared sensor. The data can be analyzed in the detection process, which is nondestructive and non-polluting. In order to investigate the effect of soil pretreatment on nitrogen content by near infrared sensor, 16 nitrogen concentrations were mixed with soil and the soil samples were divided into three groups with different pretreatment. The first group of soil samples with strict pretreatment were dried, ground, sieved and pressed. The second group of soil samples were dried and ground. The third group of soil samples were simply dried. Three linear different modeling methods are used to analyze the spectrum, including partial least squares (PLS), uninformative variable elimination (UVE), competitive adaptive reweighted algorithm (CARS). The model of nonlinear partial least squares which supports vector machine (LS-SVM) is also used to analyze the soil reflectance spectrum. The results show that the soil samples with strict pretreatment have the best accuracy in predicting nitrogen content by near-infrared sensor, and the pretreatment method is suitable for practical application.
土壤氮含量是作物重要的生长养分参数之一。在精准农业中，准确掌握土壤养分信息是科学施肥的前提。利用近红外传感器能够快速获取土壤中氮等养分信息，且检测过程可对数据进行分析，具有无损、无污染的特点。为研究土壤预处理对近红外传感器测定氮含量的影响，将16种氮浓度与土壤混合，并将土壤样品分为三组进行不同预处理。第一组经过严格预处理的土壤样品进行干燥、研磨、筛分和压制；第二组土壤样品进行干燥和研磨；第三组土壤样品仅进行干燥。采用三种线性不同的建模方法对光谱进行分析，包括偏最小二乘法（PLS）、无信息变量消除法（UVE）、竞争性自适应重加权算法（CARS）。还采用支持向量机的非线性偏最小二乘模型（LS-SVM）分析土壤反射光谱。结果表明，经过严格预处理的土壤样品利用近红外传感器预测氮含量的准确性最高，该预处理方法适用于实际应用。
