Optimum three-point linkage set up for improving the quality of soil spectra and the accuracy of soil phosphorus measured using an on-line visible and near infrared sensor

Abstract

On-line measurement of soil properties using the visible (Vis) and near infrared (NIR) spectroscopy is sensitive to soil-to-sensor distance (D) and angle () variations, which have prevented the successful development of on-line soil sensors so far. This study was undertaken to minimise these variations through optimising the three-point linkage of the tractor to improve the quality of soil spectra and the accuracy of plant available phosphorus (P-avl) measured with an on-line soil sensor. The sensor consisted of a tine, to the back of which an optical probe was attached to acquire soil spectra in diffusive reflectance mode from the bottom of the trench opened by the tine. A mobile, fibre-type, Vis-NIR spectrophotometer (Zeiss Corona 45 visnir fibre, Germany), with a measurement range of 306.5-1710.9 nm was used. Five lengths of the third point link (L) of the tractor of 545, 550, 555, 560 and 565 mm were selected to evaluate the quality of spectra collected on-line at 0.15 m tine depth. The on-line measured spectra were corrected to remove the effect of D and . The correction was evaluated by estimating the

Visible and Near Infrared Spectroscopy in Soil Science

“…In addition, topographical variation and slope changes across the field are contributing factors. Mouazen et al (2009) proposed a methodology to optimize the length of the tractor three-point link aiming at minimizing soil-to-probe distance variations, reporting improved prediction of extractable P. Recently, the potential of field, point to point, vis-NIR spectroscopy to monitor soil properties has attracted interest for applications involving three-dimensional soil variation. Ben-Dor et al (2008) developed a so-called penetrating optical sensor to measure soil properties down the soil profile to provide data that can be used for soil type classification.…”

Wheat yield prediction using machine learning and advanced sensing techniques

“…Mouazen, 2006), although the sensor was proved to successfully measure key soil properties affecting crop yield with different degree of accuracy. These include total nitrogen (TN), organic carbon (OC), moisture content (MC), phosphorous (P), pH, calcium (Ca), magnesium (Mg), clay (CC), cation exchange capacity (CEC), soil organic matter content (SOMC) and plasticity index (PI) (Mouazen et al, 2009;Marin-González et al, 2013;Kuang and Mouazen, 2013;Mouazen et al, 2014). Furthermore, none of the previous work attempted to fuse high resolution data on key soil properties with crop growth indicated as NDVI to predict crop yield in arable crops.…”

On-line measurement of soil properties without direct spectral response in near infrared spectral range

“…Although Mouazen et al (2007) demonstrated spatial similarities between measured and on-line predicted pH, no robust conclusions on accuracy of measurement could be drawn. Later Mouazen et al (2009) conducted on-line measurement of available P with remarkable accuracy (RPD = 1.42; r 2 = 0.62). To our knowledge none of the previous studies has reported on the on-line measurement of CEC, Ca ex and Mg ex .…”