Agriculture Reference
In-Depth Information
et al. (2008) used ISEs to measure soil pH, soluble potassium, and residual nitrate
contents, and achieved stable calibration for pH and K electrodes, but not for nitrate,
demonstrating the potential on-the-go soil property sensing. Lee et al. (2010)
reported that commercial electrodes are currently available for measuring different
soil properties including moisture, pH, nitrate, potassium, bromide, and chloride,
manufactured by London-Phoenix Company (Houston, TX), Cole-Parmer (Vernon
Hills, IL), and Zhejiang Top Instrument Co. Ltd. (Hangzhou, Zhejiang, China).
4.2.2.4 Microwave
Microwave is also used to measure soil moisture content. Since Schmugge (1978)
investigated thermal IR approach, passive and active microwave sensing methods
have been studied to detect soil moisture including large-scale measurements of soil
moisture (Jackson and Schmugge, 1989; Vinnikov et al., 1999; Tien et al., 2007).
Judge (2007) presented a brief review of different techniques and models to measure
soil moisture using microwave remote sensing, and reported that major challenges of
the microwave sensing would include lack of long-wavelength satellite-borne radi-
ometers, seasonal components in theoretical models, and integration of hydrologic
and microwave measurements.
4.2.3 C
ROP
S
ENSORS
Crop sensors include sensing systems for yield, nutrients, water, weed detection, crop
biomass, and health. Many different sensing techniques have been developed and
tested, and some of them have become commercially available.
NIR spectral reflectance and thermal imaging are used to monitor crop health and
nutrient/water contents. NIR reflectance has been used extensively by many research-
ers. Thomas and Oerther (1972) reported a strong relationship between reflectance
at 550 nm and sweet pepper leaf N content. Blackmer et al. (1996) reported signifi-
cant wavelengths (450, 630, 690, 710, and 760 nm) to estimate nitrogen contents of
corn canopies. Min and Lee (2005) developed prediction models for citrus nitrogen
concentrations using multivariate statistical analyses and reported 0.12% prediction
error in the validation set. They also reported several important wavelengths (448,
669, 719, 1377, 1773, and 2231 nm) for citrus N detection.
Thermal imaging and multispectral/hyperspectral imaging are used to identify
crop status. For example, Alchanatis et al. (2006) investigated mapping of water
status in a vineyard using thermal and VIS images, and reported that stomatal con-
ductance and stem water potential were highly correlated with the crop water stress
index. Cui et al. (2010) investigated automatic soybean rust detection using the
ratio of infected area and rust color index extracted from multispectral images, and
demonstrated the feasibility of detecting the disease under laboratory conditions.
Moshou et al. (2011) developed a multisensor decision system using hyperspectral
reflectance and multispectral imaging along with neural networks, and demonstrated
the functionality of automatic disease (yellow rust disease in winter wheat) detection
through field tests.
Lee et al. (2010) reviewed different methods for crop canopy and biomass detec-
tion, including laser scanning, ultrasonic sensing, light penetration of the canopy,
