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

Al-Asadi, Raed A., Mouazen, Abdul M.
Computers and electronics in agriculture 2018 v.151 pp. 485-491
bulk density, electric power, grasses, grasslands, near-infrared spectroscopy, neural networks, prediction, prototypes, reflectance spectroscopy, reflectometry, soil density, soil texture, spectrophotometers, topsoil, water content, United Kingdom
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 (ω) and a frequency domain reflectometry (FDR) to measure volumetric moisture content (θv), 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 ω and θv based on data fusion of NIRS 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.