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A prototype measuring system of soil bulk density with combined frequency domain reflectometry and visible and near infrared spectroscopy

机译:具有组合频域反射率和可见近红外光谱法的土壤堆积密度原型测量系统

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摘要

A combined-penetrometer sensor prototype (CPSP) for the measurement of topsoil bulk density (BD) was developed and tested under field conditions. The prototype consisted of a standard penetrometer, equipped with a near infrared spectrophotometer (NIRS) (1650-2500 nm) to measure gravimetric moisture content (omega) and a frequency domain reflectometry (FDR) to measure volumetric moisture content (theta v), while BD was assessed by the combination of both sensors' data. The CPSP was tested in situ at five arable and two grassland fields of different soil texture classes in Silsoe, Bedfordshire, UK, during the period from August to December 2013. Artificial neural networks (ANN) were used to predict to and Ov based on data fusion of NIBS diffuse reflectance spectra and FDR output voltage (V), and the predicted values were substituted in a model to predict BD. The CPSP showed more accurate BD assessment in grass fields with root mean square error of prediction (RMSEp) of 0.077 g cm(-3) , compared to arable fields (RMSEp = 0.104 g cm(-3)). A collective BD model produced for arable and grass fields provided a moderate accuracy with a RMSEp of 0.102 g cm(-3) . It can be concluded that the new CPSP can be used successfully to measure BD in the topsoil by combining the NIRS and FDR techniques through ANN-data fusion approach.
机译:在现场条件下开发并测试了用于测量Topsoil堆积密度(BD)的组合孔径计传感器原型(CPSP)。原型由标准的渗透度值组成,配备有近红外分光光度计(NIRS)(1650-2500nm)来测量重量湿度含量(OMEGA)和频域反射测量(FDR),以测量体积水分含量(THETA V),而通过传感器数据的组合评估BD。在2013年8月至12月,在英国斯洛萨伊,贝索德郡的不同土壤纹理课程的5个耕地和两种草原领域的测试中,CPSP在斯特洛伊的不同土壤纹理课程进行了测试。人工神经网络(ANN)用于基于数据预测和ov Nibs弥补散射反射谱和FDR输出电压(V),并且预测值被替换为模型以预测BD。 CPSP与耕地相比,CPSP在具有0.077g cm(-3)的预测(Rmsep)的均线平方误差的草地上进行了更准确的BD评估(RMSEP)(RMSEP = 0.104g cm(-3))。为可耕和草地生产的集体BD模型提供了适度的精度,具有0.102g cm(-3)的RMSEP。可以得出结论,通过通过Ann-Data Fusion方法将NIR和FDR技术组合,可以成功地使用新的CPSP来测量表土中的BD。

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