Agriculture Reference
In-Depth Information
and structured light scanning systems. FastSCAN is the ultimate laser scanner. With a
simple sweep of the FastSCAN wand, you can create instant real-time 3-D images and
databases—anytime, anywhere. FastSCAN instantly acquires 3-D surface images when
you sweep the handheld laser scanning wand over an object, in a manner similar to spray
painting. FastSCAN works by projecting a fan of laser light on the object while the cam-
era views the laser to record cross-sectional depth profiles. The object's image immedi-
ately appears on your computer screen. Because FastSCAN provides real-time visual
feedback, monitoring and controlling the scan process is straightforward. Unlike other
scanners, FastSCAN automatically stitches your scans together, saving a great deal of
time. The sweeps list enables turning individual sweeps on and off to facilitate optimiz-
ing the amount of data in your final output. The FastSCAN has an embedded FASTRAK
unit, which is used to determine position and orientation, enabling the computer to recon-
struct the full 3-D surface of the object.
Recently, several new developed methods and technologies were introduced into
plant growing information detection, such as NIR spectroscopy, multispectral imaging
technology, hyperspectral imaging technology, and machine vision or computer vision
technology. These methods have the characteristics of fast, nondestructive, low-cost,
and reliable detection methods for both quantitative and qualitative analysis. Fang et al.
(2007) studied the relationship between spectral properties of oilseed rape leaves and
their chlorophyll content. Wang et al. (2008) predicted the nitrogen concentrations from
hyperspectral reflectance at leaf and canopy for rape. Yi et al. (2007) monitored the rice
nitrogen status using hyperspectral reflectance and artificial neural network. Müller et
al. (2008) studied the vegetation indices derived from hyperspectral reflection measure-
ments for estimating crop canopy parameters of oilseed rape. Zhang et al. (2009) studied
the nitrogen information measurement of canola leaves based on multispectral vision.
Qiu et al. (2007) studied the nitrogen content of the oilseed rape at growth stage using
SPAD and visible-NIR. Feng et al. (2006) studied nitrogen stress measurement of canola
based on multispectral CCD imaging sensor. Liu et al. (2011a) studied N, P, and K in oil-
seed rape leaves using visible and NIR spectroscopy. Liu et al. (2011b) studied the nonde-
structive estimation of nitrogen status and vegetation index of oilseed rape canopy using
multispectral imaging technology. Liu et al. (2008, 2011c, 2012) studied the acetolactate
synthase, soluble protein content, unsoluble protein content, total protein content, and
total amino acids in oilseed rape under herbicide stress using spectroscopic techniques.
Kong et al. (2011) studied the fast determination of malondialdehyde in oilseed rape
leaves using NIR spectroscopy. Cui et al. (2009) developed a handheld spectroscopy-
based optical sensing device for measuring crop leaf NDVI values under in-field natural
light conditions. Kim et al. (2008) developed a fuzzy logic control algorithm that was
applied to automatically adjust the camera exposure and gain to control image brightness
within a targeted gray level in an image quality controller. An application of in-field plant
sensing using the fuzzy logic image controller was evaluated on corn crops for nitrogen
detection.
9.2.6 S
OIL
I
NFORMATION
A
CQUISITION
M
ETHODS
AND
I
NSTRUMENTS
Soil material consists of a variable and often complex mixture of organic matter,
sand, silt, and clay particles, or is composed of dominantly organic debris. Soil
