Agriculture Reference
In-Depth Information
Hummel (1993a) developed a portable spectrophotometer to measure soil organic
matter, cation-exchange capacity, and moisture content, and tested it in the field
(Sudduth and Hummel, 1993b). Ehsani et al. (1999) developed calibration models
using partial least squares (PLS) and principal component regression to estimate
soil mineral-N content using soil NIR reflectance in 1100-2500 nm. They reported
that the models were very robust, but suggested that site-specific calibration of the
models was necessary. For measuring underground soil reflectance, Shibusawa et
al. (1999) developed a portable spectrophotometer in 400-1700 nm to measure soil
moisture, soil pH, electrical conductivity, soil organic matter, and NO
3
-N. Hummel
et al. (2001) used an NIR soil sensor to predict soil moisture and organic matter
content. Mouazen et al. (2005a, 2005b) developed a portable NIR spectrophotom-
eter in 306-1711 nm to measure soil moisture content and to identify soil texture.
Bogrekci and Lee (2005a) examined the spectral characteristics of four common
soil phosphates (Al, Fe, Ca, and Mg phosphates) in Florida and reported that those
phosphates could be detected with a classification error of 1.9%. Maleki et al. (2007)
investigated a portable visible (VIS)-NIR P sensor for variable rate application of
elemental P. Maleki et al. (2008) implemented a real-time application of phosphate
(P
2
O
5
) for maize planting using an on-the-go VIS and NIR soil sensor. Christy
(2008) developed a shank-based spectrophotometer and reported that organic matter
was predicted best from field testing. However, as Ge et al. (2006) pointed out, one
of the major challenges for implementing a real-time soil property sensing is that soil
properties vary greatly from location to location since soil is a very complex mixture
of many different objects.
Soil moisture is another property to be estimated by NIR since there are very
distinct water absorption bands in the NIR region. Some of the studies include test-
ing of a soil moisture meter using NIR reflectance at 1800 and 1940 nm (Kano
et al., 1985), a global NIR calibration equation to determine soil moisture content
(Slaughter et al., 2001), and an exponential prediction model (Kaleita et al., 2005).
Other than using NIR, a commercial device is available to measure soil moisture
(EM38, Geonics Limited, Ontario, Canada) that uses electronic magnetic induction.
A transmitting coil induces magnetic field in the soil, and a receiving coil measures
induced current in the soil, which is used to measure soil conductivity, and then used
to estimate soil moisture indirectly.
Using soil diffuse reflectance in the MIR range measured by a Fourier trans-
form infrared (FTIR) spectrophotometer, Ehsani et al. (2001) estimated soil nitrate
content and found a strong nitrate absorption peak at 7194 nm. Linker et al. (2004)
also used FTIR-attenuated total reflectance spectroscopy in the MIR region to esti-
mate soil nitrate content, and found that the best root mean square prediction errors
ranged from 38 to 43 ppm N.
Another technique to detect soil properties is to use aerial and satellite images.
Among the earlier studies, Landsat TM and SPOT images were used to detect differ-
ent soil properties (Coleman et al., 1993; Agbu et al., 1990) and soil lines (Galvao and
Vitorello, 1998; Fox and Sabbagh, 2002). Some soil properties were detected using
aerial images, including soil P and organic matter (Varvel et al., 1999), soil moisture
(Muller and Decamps, 2000), and soil texture (Barnes and Baker, 2002).
