PDF(Pubmed)
Abstract：
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.
摘要：
泥土氮含量是作物成长的重要养分参数之一。精准农业准确掌握土壤养分信息是科学施肥的前提。利用近红外传感器可以快速获得土壤中的氮等养分信息。在检测过程中可以对数据进行分析,它是非破坏性和无污染的。利用近红外传感器研究土壤预处理对氮素含量的影响,将16个氮浓度与土壤混合，将土壤样品分为三组，进行不同的预处理。将经过严格预处理的第一组土壤样品干燥，地面,过筛并压榨。将第二组土壤样品干燥并研磨。将第三组土壤样品简单干燥。三种线性不同的建模方法用于分析频谱，包括偏最小二乘(PLS)，无信息变量消除(UVE)，竞争自适应重加权算法(CARS)。利用支持向量机(LS-SVM)的非线性偏最小二乘模型对土壤反射谱进行了分析。结果表明,经过严格预处理的土壤样品在近红外传感器预测氮含量时具有最好的准确性,预处理方法适合实际应用。
