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Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms

机译:基于土壤预处理和算法的近红外传感器检测土壤氮素

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摘要

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.
机译:土壤氮含量是农作物重要的生长养分参数之一。准确掌握精准农业中的土壤养分信息是科学施肥的前提。使用近红外传感器可以快速获取有关土壤中氮等养分的信息。可以在检测过程中分析数据,这是无损且无污染的。为了研究近红外传感器对土壤预处理对氮含量的影响,将16种氮浓度与土壤混合,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进行干燥,研磨,筛分和压榨。将第二组土壤样品干燥并研磨。简单干燥第三组土壤样品。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),竞争性自适应加权算法(CARS)。支持向量机(LS-SVM)的非线性偏最小二乘模型也用于分析土壤反射光谱。结果表明,经过严格预处理的土壤样品在利用近红外传感器预测氮含量方面具有最佳的准确性,并且该预处理方法适合实际应用。

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