Method for monitoring nitrogen status using a multi-spectral imaging system

A multi-spectral imaging system and method for producing an image is disclosed. More specifically, the imaging system produces an image of vegetation for analysis of crop characteristics, such as nitrogen levels, from an area having vegetation and a non-vegetation background. A light sensing unit detects light reflected at multiple wavelengths. The image is segmented into images at different wavelengths such as at the red, green and near infrared wavelengths. The images are combined into a multi-spectral image and segmented into a vegetation image by eliminating all non-vegetation images by using the images at two wavelengths. The vegetation image is analyzed for nitrogen levels by calculating reflectance values at the green wavelength. The images may be stored for further analysis of crop characteristics.

FIELD OF INVENTION 
This invention relates to an apparatus and method for producing a 
multi-spectral image of selected objects in an area and more specifically, 
to an apparatus and method for using a multi-spectral sensor which detects 
light reflected at multiple wavelengths from an area having vegetation and 
non-vegetation to produce a vegetation image for analysis of 
characteristics such as nitrogen. 
BACKGROUND OF INVENTION 
Monitoring of crops in agriculture is necessary to determine optimal 
growing conditions to improve and maximize yields. Maximization of crop 
yields is critical to the agricultural industry due to the relatively low 
profit margins involved. Crop conditions in a particular field or area are 
analyzed for factors such as plant growth, irrigation, pesticides etc. The 
results of the analyses may be used to identify planting problems, 
estimate yields, adjust irrigation schedules and plan fertilizer 
application. The status of the crops is monitored throughout the growing 
cycle in order to insure that maximum crop yields may be achieved. 
Optimum crop development requires maintenance of high levels of both 
chlorophyll and nitrogen in plants. As it is known that plant growth 
correlates with chlorophyll concentration, finding of low chlorophyll 
concentration levels is indicative of slower growth and ultimately a yield 
loss. Since there is a direct relationship between the nitrogen and 
chlorophyll levels in plants, a finding of low chlorophyll may signal the 
existence of low levels of nitrogen. Thus, in order to improve crop 
growth, farmers add nitrogen fertilizers to the soil to increase 
chlorophyll concentration and stimulate crop growth. Fertilizer 
treatments, if applied early in the crop growth cycle, can insure that 
slower growing crops achieve normal levels of growth. 
Monitoring nitrogen levels in crops, vis-a-vis chlorophyll levels, allows a 
farmer to adjust application of fertilizer to compensate for shortages of 
nitrogen and increase crop growth. 
Accurate recommendations for fertilizer nitrogen are desired to avoid 
inadequate or excessive application of nitrogen fertilizers. Excessive 
amounts of fertilizer may reduce yields and quality of certain crops. 
Additionally, over application of fertilizer results in added costs to a 
farmer, as well as increasing the potential for nitrate contamination of 
the environment. Thus, it is critical to obtain both accurate and timely 
information on nitrogen levels. 
One known method of determining the nitrogen content in plants and soil 
involves taking samples of plants and soil and performing chemical 
testing. However this method requires considerable time and repeated 
sampling during the growing season. Additionally, a time delay exists from 
the time the samples are taken to the time when the nitrogen levels are 
ascertained and when fertilizer may be applied due to the time required 
for laboratory analysis. Such delay may result in the delayed application 
of corrective amounts of fertilizer, which may then be too late to prevent 
stunted crop growth. 
In an effort to eliminate the delay between the times of nitrogen 
measurement and the application of corrective fertilizer, it has been 
previously suggested to utilize aerial or satellite photographs to obtain 
timely data on field conditions. This method involves taking a photograph 
from a camera mounted on an airplane or a satellite. Such photos are 
compared with those of areas which do not have nitrogen stress. Such a 
method provides improvement in analysis time but is still not real time. 
Additionally it requires human intervention and judgment. Information 
about crop status is limited to the resolution of the images. When such 
aerial images are digitized, a single pixel may represent an area such as 
a square meter. Insufficient resolution prevents accurate crop assessment. 
Other information which might be gleaned from higher resolution images 
cannot be measured. 
Another approach uses a photodiode mounted on ground based platforms to 
monitor light reflected from a sensed area. The image is analyzed to 
determine the quantity of light reflected at specific wavelengths within 
the light spectrum of the field of view. Nitrogen levels in the crops have 
been related to the amount of light reflected in specific parts of the 
light spectrum, most notably the green and near infrared wavelength bands. 
Thus, the reflectance of a crop may be used to estimate the nitrogen for 
the plants in that crop area. 
In contradistinction, however, the photodiode sensing methods suffer from 
inaccuracies in the early part of the crop growth cycle because the 
overall reflectance values are partially derived from significant areas of 
non-vegetation backgrounds, such as soil, which skew the reflectance value 
and hence the nitrogen measurements. Additionally, since one value is 
used, this method cannot account for deviations in reflectance readings 
due to shadows, tassels and row orientation of the crops. 
Increasing spatial and spectral resolution can produce a more accurate 
image, which provides improved reflectance analysis as well as being able 
to differentiate individual rows or plants. However, current high 
resolution remote sensing approaches have met with little success because 
of the tremendous volumes of data generated when used over large areas at 
the necessary high resolutions. These methods are difficult to implement 
because of the large amount of data which must be stored or transferred 
for each image. 
Thus a need exists for an image sensor which is capable of producing crop 
images which may be analyzed in real time for substances such as nitrogen. 
Furthermore, there is a need for an image sensor which accurately analyzes 
nitrogen content in crops independent of the stage of crop growth. Also, 
there is a need for a sensor which can isolate vegetation regions from an 
image comprising vegetation and non-vegetation areas for analysis. There 
is also a need for an image sensor which can determine amounts of nitrogen 
in discrete areas of an imaged crop area such as for a particular row. 
Also, there is a need for a sensor which can produce and store images of 
crop areas for later analysis. There is a need for an image sensor which 
can correct for the effects of variable ambient light on reflectance. 
Finally, a system is desired which may be calibrated to provide accurate 
prediction of additional nitrogen fertilizer required for optimum yields. 
SUMMARY OF THE INVENTION 
The present invention is embodied in an imaging system for analyzing an 
image of vegetation from an area having vegetation and a non-vegetation 
background. The imaging system includes an light receiving unit for 
receiving light reflected from the vegetation and non-vegetation 
background at a plurality of wavelength ranges. A multi-spectral sensor is 
coupled to the light receiving unit to produce an image of the vegetation 
and non-vegetation based on the light reflected at the plurality of 
wavelength ranges. An image processor is coupled to the multi-spectral 
sensor to produce a vegetation image by separating the non-vegetation 
portion of the image from the vegetation portion of the image as a 
function of light reflected at a first wavelength range. A means for 
analyzing the vegetation image to determine crop characteristics of the 
vegetation is coupled to the image processor. The crop characteristics of 
the vegetation may include nitrogen levels which may be used to control 
corrective fertilizer treatments. 
The present invention is further embodied in a method for determining crop 
characteristics in an area with vegetation and non-vegetation. First, 
light reflected from the area at a plurality of wavelength ranges is 
sensed. An image is formed based on the sensed light at the plurality of 
wavelength ranges. A vegetation image is separated from the image by 
analyzing light reflected at a first wavelength range. The light reflected 
by the vegetation image is determined at a third wavelength range. Crop 
characteristics are calculated in the vegetation image. This information 
may be used to determine nitrogen status of the vegetation. 
It is to be understood that both the foregoing general description and the 
following detailed description are not limiting but are intended to 
provide further explanation of the invention claimed. The accompanying 
drawings, which are incorporated in and constitute part of this 
specification, are included to illustrate and provide a further 
understanding of the method and system of the invention. Together with the 
description, the drawings serve to explain the principles of the 
invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT 
While the present invention is capable of embodiment in various forms, 
there is shown in the drawings and will hereinafter be described a 
presently preferred embodiment with the understanding that the present 
disclosure is to be considered as an exemplification of the invention, and 
is not intended to limit the invention to the specific embodiment 
illustrated. 
FIG. 1 shows a block diagram of an imaging system 10 which embodies the 
principles of the present invention. The imaging system 10 produces an 
image of vegetation from an area 12 having vegetation 14 and a 
non-vegetation background 16. The area 12 may be a field of any dimension 
in which analysis of the vegetation 14 for crop growth characteristics is 
desired. The present imaging system 10 is directed toward determination of 
nitrogen levels in the vegetation 14, although other crop growth 
characteristics may be determined as will be explained below. 
The vegetation 14 are typically crops which are planted in rows or other 
patterns in the area 12. The vegetation 14 in the preferred embodiment 
includes all parts of the crops such as the green parts of crops which are 
exposed to light, non-green parts of crops such as corn tassels and green 
parts which are not exposed to light (shadowed). In certain applications 
of the preferred embodiment such as nitrogen characterization, the images 
of vegetation 14 will only include green parts of crops which are exposed 
to light particularly direct light. Other plant parts are not considered 
parts of the vegetation 14 which will be imaged. Other applications such 
as crop canopy analysis will include all parts of the crops as the image 
of vegetation 14. 
The imaging system 10 has a light receiving unit 18 which detects light 
reflected from the vegetation 14 and the non-vegetation background 16 at a 
plurality of wavelength ranges. In the preferred embodiment, the light 
receiving unit 18 senses light reflected in three wavelength ranges, near 
infrared, red and green. The optimal wavelengths for crop characterization 
are green in the wavelength range of 550 nm (+/-20 nm), red in the 
wavelength range of 670 nm (+/-40 nm) and near infrared in the wavelength 
range of 800 nm (+/-40 nm). Of course, different bandwidths may be used. 
Additionally, the specific optimized wavelengths may depend on the type of 
vegetation being sensed. 
The size of the area of view of the area 12 depends on the proximity of the 
imaging system 10 to the area 12 and the focal length of light receiving 
unit 18. A more detailed image may be obtained if the system 10 is in 
closer proximity to the area 12 and or a smaller focal length lens is 
used. In the preferred embodiment, the imaging system 10 is mounted on a 
stable platform such as a tractor and the area of view is approximately 20 
by 15 feet. 
Larger areas of land may be imaged if the system 10 is mounted on an aerial 
platform such as an airplane, helicopter or a satellite. When the system 
10 is mounted on an aerial platform a larger imaging array may be used in 
order to capture large areas with sufficient spatial and spectral 
resolution. Alternatively, several small images of a large area can be 
combined into an image map when used in conjunction with global 
positioning system (GPS) data. 
Light receiving unit 18 is coupled to a multi-spectral sensor 20 to produce 
a multi-spectral image of the vegetation and non-vegetation based on the 
light reflected at the various wavelength ranges. An image processor 22 is 
coupled to the multi-spectral sensor 20 to produce a vegetation image by 
separating the non-vegetation portion from the vegetation portion of the 
multi-spectral image as a function of light reflected at the first 
wavelength range (near infrared) and light reflected at the second 
wavelength range (red). 
The vegetation image is analyzed based on the third wavelength range 
(green). The image processor 22 includes a program for analyzing the 
vegetation image to determine the nitrogen status of the crop. This 
analysis may convert the observed reflectance levels to determine the 
amount of a substance such as nitrogen or chlorophyll in the vegetation 
and the amount of crop growth. Alternatively, one wavelength range may be 
used for both separating the non-vegetation portion from the vegetation 
portion as well as performing analysis on the vegetation image. 
A storage device 24 is coupled to the image processor 22 for storing the 
vegetation image. The storage device 24 may be any form of memory device 
such as random access memory (RAM) or a magnetic disk. A geographic 
information system (GIS) 26 is coupled to the storage device 24 and serves 
to store location data with the stored vegetation images. Geographic 
information system 26 is coupled to a geographic position sensor 28 which 
provides location data. The position sensor 28, in the preferred 
embodiment, is a global positioning system receiver although other types 
of position sensors may be used. 
The geographic information system 26 takes the location data and correlates 
the data to the stored image. The location data may be used to produce a 
crop map which indicates the location of individual plants or rows. The 
location data may be also used to produce a vegetation map. Alternatively, 
if the system 10 is mounted aerially, the location data may be used to 
assemble a detailed vegetation map using smaller images. 
The image processor 22 may also be coupled to a corrective nitrogen 
application controller 30. Since the above analysis may be performed in 
real time, the resulting data may be used to add fertilizer to areas which 
do not have sufficient levels of nitrogen as the sensor system 10 passes 
over the deficient area. The controller 30 is connected to a fertilizer 
source 32. The controller 30 uses the information regarding nitrogen 
levels in the vegetation 14 from image processor 22 and determines whether 
corrective nitrogen treatments in the form of fertilizer are necessary. 
The controller 30 then applies fertilizer in these amounts from the 
fertilizer source 32. The fertilizer source includes any fertilizer 
application device including those pulled by tractor or self propelled. 
The fertilizer source may also be applied using irrigation systems. 
FIG. 2 shows the components of the light receiving unit 18, the 
multi-spectral sensor 20, and the image processor 22. The light receiving 
unit 18 in the preferred embodiment has a front section 36, a lens body 38 
and an optional section 40 for housing an electronic iris. The electronic 
iris may be used to control the amount of light exposed to the 
multi-spectral sensor 20. The scene viewed through the lens 38 of the area 
12 is transmitted to a prism box 42. The prism box 42 splits the light 
passing through the lens 38 to a near infrared filter 44, a red filter 46 
and a green filter 48. Thus the light passed through the lens 38 is broken 
up into light reflected at each of the three wavelengths. The light at 
each of the three wavelengths from the prism box 42 is transmitted to 
other components of the multi-spectral sensor 20. 
The multi-spectral sensor 20 contains three charge coupled device (CCD) 
arrays 50, 52 and 54. The light passes through near infrared filter 44, 
red filter 46, and green filter 48 then is radiated upon charge coupled 
device (CCD) arrays 52, 50, and 54, respectively. The CCD arrays 50, 52 
and 54 convert photon to electron energy when they are charged in response 
to signals received from integrated control circuits 58, described below. 
The CCD arrays 50, 52 and 54 may be exposed to light for individually 
varying exposure period by preventing photon transmission after a certain 
exposure duty cycle. 
The CCD arrays 50, 52 and 54 convert the scene viewed through the lens 38 
of the vegetation 14 and non-vegetation 16 of the area 12 into a pixel 
image corresponding to each of the three wavelength ranges. The CCD arrays 
50, 52 and 54 therefore individually detect the same scene in three 
different wavelength ranges: red, green and near infrared ranges in the 
preferred embodiment. Accordingly, multi-spectral sensor 20 is adapted to 
provide two or more images in two or more wavelength bands or spectrums, 
and each of the images are taken by the same scene by light receiving unit 
18. 
In the preferred embodiment, each of the CCD arrays 50, 52 and 54 have 
307,200 detector elements or pixels which are contained in 640.times.480 
arrays. Each detector element or pixel in the CCD arrays 50, 52 and 54 is 
a photosite where photons from the impacting light are converted to 
electrical signals. Each photosite thus produces a corresponding analog 
signal proportional to the amount of light at the wavelength impacting 
that photosite. 
While the CCD arrays preferably have a resolution of 640 by 480 pixels, 
arrays having a resolution equal to or greater than 10 by 10 pixels may 
prove satisfactory depending upon the size of the area to be imaged. 
Larger CCD arrays may be used for greater spatial or spectral resolution. 
Alternatively, larger areas may be imaged using larger CCD arrays. For 
example, if the system 10 is mounted on an airplane or a satellite, an 
expanded CCD array may be desirable. 
Each pixel in the array of pixels receives light from only a small portion 
of the total scene viewed by the sensor. The portion of the scene from 
which each pixel receives light is that pixel's viewing area. The size of 
each pixel's viewing area depends upon the pixel resolution of the CCD 
array of which it is a part, the optics (including lens 38) used to focus 
reflected light from the imaged area to the CCD array, and the distance 
between unit 18 and the imaged areas. For particular crops, there are 
preferred pixel viewing areas and system 10 should be configured to 
provide that particular viewing area. For crops such as corn and similar 
leafy plants, when the system is used to measure crop characteristics at 
later growth stages, the area in the field of view of each pixel should be 
less than 100 square inches. More preferably, the area should be less than 
24 square inches. Most preferably, the area should be less than 6 square 
inches. For the same crops at early growth stages, the area in the field 
of view of each pixel should be no more than 24 square inches. More 
preferably, the area should be no more than 6 square inches, and most 
preferably, the area should be no more than 1 square inch. 
CCD arrays 50, 52 and 54 are positioned in multi-spectral sensor 20 to send 
the analog signals generated by the CCD arrays representative of the 
green, red and near infrared radiation to a sensor control circuit 56 
(electronically coupled to the CCD arrays) which converts the three analog 
signals into three video signals (red, near infrared and green) 
representative of the red, near infrared and green analog signals, 
respectively. The video signals are transmitted to the image processor 22. 
The data from these signals is used for analysis of crop characteristics 
of the imaged vegetation, i.e. vegetation 14 in the area 12. If desired, 
these signals may be stored in storage device 24 for further processing 
and analysis. 
Sensor Control Circuit 56 includes three integration control circuits 58 
which have control outputs coupled to the CCD arrays 50, 52 and 54 to 
control the duty cycle of the pixels' collection charge and prevent 
oversaturation and/or the number of pixels at noise equivalent level of 
the pixels in the CCD arrays 50, 52 and 54. The noise equivalent level is 
the CCD output level when no light radiates upon the light-receiving 
surfaces of a CCD array. Such levels are not a function of light received, 
and therefore are considered noise. One or more integration control 
circuits 58 include an input coupled to the CCD array 54. The input 
measures the level of saturation of the pixels in CCD array 54 and the 
integration control circuit 58 determines the duty cycle for all three CCD 
arrays 50, 52 and 54 based on this input. The green wavelength light 
detected by CCD array 54 provides the best indication of oversaturation of 
pixel elements. 
The exposure time of the CCD arrays 50, 52 and 54 is typically varied 
between one sixtieth and one ten thousandth of a second in order to keep 
the CCD dynamic range below the saturation exposure but above the noise 
equivalent exposure. Alternatively, the duty cycle for the other two CCD 
arrays 50 and 52 may be determined independently of the saturation level 
of CCD array 54. This may be accomplished by separate inputs to 
integration control circuits 58 and separate control lines to CCD arrays 
50 and 52. 
One or more integration control circuits 58 may also control the electronic 
iris of section 40. The electronic iris of section 40 has a variable 
aperture to allow more or less light to be passed through to the CCD 
arrays 50, 52 and 54 according to the control signal sent from at least 
one integration control circuit 58. Thus, the exposure of the CCD arrays 
50, 52 and 54 may be controlled by the iris 40 to shutter light or the 
duty cycle of the pixels or a combination depending on the application. 
The analog signals are converted into digital values for each of the pixels 
for each of the three images at green, red and near infrared. These 
digital values form digital images that are combined into a multi-spectral 
image which has a green, red and near infrared value for each pixel. The 
analog values of each pixel may be digitized using, for example, an 8 bit 
analog to digital converter to obtain reflectance values (256 colors) at 
each wavelength for each pixel in the composite image, if desired. Of 
course, higher levels of color resolution may be obtained with a 24 bit 
analog to digital converter (16.7 million colors). 
The light receiving unit 18 can also include a light source 62 which 
illuminates the area 12 of vegetation 14 and non-vegetation 16 sensed by 
the light receiving unit 18. The light source 62 may be a conventional 
lamp which generates light throughout the spectrum range of the CCD 
arrays. The light source 62 is used to generate a consistent source of 
light to eliminate the effect of background conditions such as shade, 
clouds etc. on the ambient light levels reaching the area 12. 
Additionally, the imaging system 10 can include an ambient light sensor 64. 
The ambient light sensor 64 is coupled to the image processing circuit 22 
and provides three output signals representative of the ambient red, near 
infrared and green light, respectively, around the area 12. The output of 
the ambient light sensor 64 may be used to quantify reflectance 
measurement in environments in which the overall light levels change. In 
particular, the output of the ambient light sensor may be used to enable 
correction of the observed reflectance to account for changes in ambient 
light. A change in reflectance may be caused either by a change in the 
vegetation characteristics or to a change in ambient light intensity. 
Although primary control of CCD duty cycle is based upon direct CCD 
response, the processing circuit 22 may control the integration control 
circuits 58 to adjust the exposure time of the CCD arrays 50, 52 and 54 to 
changes in reflectance and therefore maintain the output within a dynamic 
range. 
The operation and analysis procedure of the imaging system 10 will now be 
explained with reference to FIGS. 1-4. The imaging system 10 is used to 
determine crop characteristics. The imaging system 10 first senses light 
reflected from the vegetation 14 and the non-vegetation 16 of the area 12 
at a plurality of wavelength ranges using the light receiving unit 18 as 
described above. The light receiving unit 18 separates the light reflected 
from the area 12 into a plurality of wavelength ranges. As explained 
above, there are three wavelengths and images are formed for light 
reflected at each of the wavelengths. As FIG. 3 shows, a red image 70, a 
near infrared image 72, and a green image 74 are formed from the CCD 
arrays 50, 52 and 54, respectively, of the multi-spectral sensor 20. 
After the light is sensed at the three wavelength ranges, a multi-spectral 
image 76 is formed based on the sensed light at the plurality of 
wavelength ranges by the image processing circuit 22. The multi-spectral 
image 76 is a combination of the three separate images 70, 72 and 74 at 
the red, near infrared and green wavelengths. A vegetation image 78 is 
obtained from the multi-spectral image 76 by analyzing light reflected at 
a first wavelength range and light reflected at the second wavelength 
range. Light reflected by the vegetation image 78 is determined at the 
third wavelength range to form a green vegetation image 80. Alternatively, 
the vegetation image 78 may be obtained by analyzing light reflected at a 
first wavelength range alone. 
The quantity of a substance in the vegetation 14 is determined as a 
function of the light reflected by the vegetation image 78 at the third 
wavelength range such as the green vegetation image 80. Light reflectance 
in the visible spectrum (400-700 nm) increases with nitrogen deficiency in 
vegetation. Thus, sensing light reflectance allows a determination of the 
nitrogen in vegetation areas. Alternatively, the quantity of a substance 
such as nitrogen may be determined as a function of the light reflected by 
the vegetation image 78 at the first wavelength range alone. 
Thus, the individual images 70, 72 and 74 at each of the three wavelengths 
may be combined to make a single multi-spectral image 76 by the image 
processing circuit 22 or may be transmitted or stored separately in 
storage device 24 for further image processing and analysis. Additional 
processing 8 may be performed on the vegetation image 78 to further 
distinguish features such as individual plants, shaded areas etc. 
Alternatively, the present invention may be used with present images 
captured using color or color NIR film. Such film based images are then 
digitized to provide the necessary spatial resolution. Such digitization 
may take an entire image. Alternatively, a portion of an image or several 
portions of an image may be scanned to assemble a map from different 
segments. 
The image processor 22 is used to enhance the multi-spectral image 76, 
compute a threshold value for the image and produce the vegetation image 
78. The enhancement step is performed in order to differentiate the 
vegetation and non-vegetation images in the composite image. As explained 
above, for purposes of characterizing crop nitrogen status, the vegetation 
includes only the green parts of a plant which are exposed to light, while 
the non-vegetation includes soil, tassels, shaded parts of plants etc. 
Enhancement may be achieved by calculating an index using reflectance 
information from multiple wavelengths. The index is dependant on the type 
of feature which is desired to be enhanced. In the preferred embodiment, 
the vegetation features of the image are enhanced in order to perform crop 
analysis. However, other enhancements may include evaluation of soil, 
specific parts of plants etc. 
The index value for image enhancement is calculated for each pixel in the 
multi-spectral image 76. The index value in the preferred embodiment is 
derived from a formula which is optimal for separating vegetation from 
non-vegetation i.e. soil areas. The preferred embodiment calculates a 
normalized difference vegetative index (NDVI) as an index value to 
separate the vegetation pixels from non-vegetation pixels. The NDVI index 
for each pixel is calculated by subtracting the red value from the near 
infrared value and dividing the result from the addition of the red value 
and the near infrared value. The vegetation image map is generated using 
the NDVI value for each pixel in the multi-spectral image. 
A threshold value is computed based on the NDVI data for each pixel. An 
algorithm is chosen to compute a point that separates the vegetation areas 
from the non-vegetation areas. This point is termed the threshold and may 
be calculated using a variety of different techniques. In the preferred 
embodiment a histogram of the NDVI values is calculated for all the pixels 
in the multi-spectral image. The NDVI values constitute a gray scale image 
composed of each of the pixels in the multi-spectral image. 
The histogram representing an NDVI gray scale image for multi-spectral 
image 76 is shown in FIG. 4. The histogram in FIG. 4 demonstrates the 
normal binary distribution between the soil (&lt;64 gray level) and 
vegetation (&gt;64 gray level). The threshold value is then calculated by an 
algorithm which best computes the gray level that separates the vegetation 
from non-vegetation areas. In the preferred embodiment the mean value for 
the gray scale for all the pixels in the multi-spectral image 76 is 
calculated. The mean is modified by an offset value to produce the 
threshold value. The offset value is obtained from a look up table having 
empirically derived gray scale values for different vegetation and 
non-vegetation areas obtained under comparable conditions. In FIG. 4, the 
threshold value is computed near gray level 64. Each pixel's NDVI value is 
compared with the threshold value. If the NDVI value is below the 
threshold value, the pixel is determined to be non-vegetation and its 
reflectance values for all three wavelengths are set to zero which 
correspond to a black color. The pixels which have NDVI values above the 
threshold do not have their reflectance values altered. Thus, the 
resulting vegetation image 78 has only vegetation pixels representing the 
vegetation 14. 
The image processor 22 then performs additional image analysis on the 
resulting vegetation image 78. The image analysis may be used to evaluate 
crop status in a number of ways. For example, plant nitrogen levels, plant 
population and percent canopy measurements may be characterized depending 
on how the vegetation image is filtered. 
Crop nitrogen status may be estimated by the above described process since 
reflected green light is closely correlated with plant chlorophyll content 
and nitrogen concentration. Thus determination of the average reflected 
green light over a given region provides the nitrogen and chlorophyll 
concentration. In this case, the NDVI values are used to select pixels 
which represent the green parts of the plants which are exposed to light. 
The reflective index may be computed from an entire image or it may be 
computed for selected areas within each image. The reflective index is 
computed for each pixel of an image in the preferred embodiment. 
The average green reflective index (G.sub.avg n) values for a particular 
area is computed as follows. 
EQU G.sub.avg.sbsb.n =.SIGMA.G.sub.n (x.sub.c,y.sub.c) 
In this equation, G.sub.n is the green reflectance value for each of the 
individual pixels (x.sub.c and y.sub.c) in the vegetation area, n, for 
which the reflectance index is calculated and c.sub.n is the total number 
of pixels in the vegetation area. 
Crop nitrogen status can also be estimated for a selected area of the 
vegetation image by calculating the ratio of light intensity at the third 
wavelength band to light intensity at the first wavelength band. This 
ratio is indicative of the crop nitrogen status. This ratio may be 
calculated by taking the ratio of the pixel value of a pixel receiving 
light in the third wavelength band and dividing this by a pixel value of a 
pixel receiving light in the first wavelength band. Alternatively, several 
such ratios may be calculated and the average taken of these ratios. 
Alternatively, an average value of pixels in the third wavelength band may 
be determined and an average value of pixels in the first wavelength band 
may be determined. The average pixel value for the third wavelength band 
may then be divided by the average pixel value for the first wavelength 
band. If this process is performed to estimate the nitrogen status for a 
selected area of the image, only those pixels that form the selected area 
would be employed. 
A normalized nitrogen status may be obtained by using a nitrogen 
classification algorithm. This algorithm uses the computed reflective 
index and also incorporates ambient light measurements from the ambient 
light sensor 64 and settings such as the duty cycle of arrays 50, 52 and 
54. Including these non-vegetation parameters enables the system to 
correct for changes in observed reflectance due to ambient light levels 
and sensor system parameters. 
Another corrective measure for vegetation factors involves sensing a 
reference strip of vegetation having a greater supply of nitrogen. This 
reference strip may consist of rows of plants which are given 10-20% more 
nitrogen than is typically recommended for the crop, thus insuring that 
the lack of nitrogen does not limit crop growth and chlorophyll levels. 
The reference plants are located at specific intervals depending on the 
regions or areas where the reflective indexes are to be calculated. 
A reference reflectance value is calculated from the reference strip by the 
process described above. The reflective index of the other areas can be 
compared directly to the reference N reflectance value. Direct comparison 
of the crop reflectance at the green wavelength with reflectance from an 
adjacent reference strip will ensure that differences in observed 
reflectance are due solely to nitrogen deficiency and not to low light 
levels or other stress factors that may have impacted reflectance from the 
crop. 
The system 10 may be used to compile a larger crop map of a field in which 
a crop is growing. To create this map, the system receives and stores a 
succession of individual images of the crop each taken at a different 
position in the field. The position sensor 28 is used to obtain location 
coordinates, substantially simultaneous to receiving each image, 
indicative of the location at which each of the images was received. The 
location coordinates are stored in a manner that preserves the 
relationship between each image and its corresponding location 
coordinates. As each vegetation image is processed it is combined with 
other vegetation images to form a vegetation map of a larger area. 
Crop growth may also be determined by system 10. To provide this 
determination, a first image may be taken of the crop at a particular 
location and recorded. Subsequent images may be taken and recorded at 
varying time intervals, such as weekly, biweekly or monthly. The amount of 
crop growth over each such interval may then be determined by comparing 
the first recorded images with subsequent recorded images at the same 
location. 
The stored vegetation images may be used for further analysis, such as to 
determine plant population. Additionally, in conjunction with the location 
data obtained from the position sensor 28, the positions of individual 
plants from the vegetation image may be determined. Further analysis may 
be performed by isolating an image of a specific row of vegetation. This 
analysis may be performed using the stored digital images and software 
tailored to enhance images. 
The above identified data may then be used for comparison of crop factors 
such as tillage, genotype used and fertilizer effects. 
It will be apparent to those skilled in the art that various modifications 
and variations can be made in the method and system of the present 
invention without departing from the spirit or scope of the invention. For 
example, the imaging sensor may be used in conjunction with soil property 
measurements such as type, texture, fertility and moisture analysis. 
Additionally, it may be used in residue measurements such as type or 
residue or percentage of residue coverage. Images can also be analyzed for 
weed detection or identification purposes. 
The invention is not limited to crop sensing application such as nitrogen 
analysis. The light receiving unit and image processor arrangement may be 
used in vehicle guidance by using processed images to follow crop rows, 
recognize row width, follow implement markers and follow crop edges in 
tillage operations. The sonsor arrangement may also be used in harvesting 
by measuring factors such as grain tailings, harvester swath width, 
numbers of rows, cutter bar width or header width and monitoring factors 
such as yield, quality of yield, loss percentage, number of rows. 
In other words, the reference strip of vegetation is provided with a 
non-limiting supply of nitrogen. The imaging system of the present 
invention may also be used to aid vision by providing rear or alternate 
views or guidance error checking. The system may also be used in 
conjunction with obstacle avoidance. Additionally, the system may be used 
to monitor operator status such as human presence or human alertness. 
Thus, it is intended that the present invention cover modifications and 
variations that come within the scope of the spirit of the invention and 
the claims that follow.