JP3551264B2 - Method of creating evaluation image of plant vitality fluctuation - Google Patents

Method of creating evaluation image of plant vitality fluctuation Download PDF

Info

Publication number
JP3551264B2
JP3551264B2 JP28624893A JP28624893A JP3551264B2 JP 3551264 B2 JP3551264 B2 JP 3551264B2 JP 28624893 A JP28624893 A JP 28624893A JP 28624893 A JP28624893 A JP 28624893A JP 3551264 B2 JP3551264 B2 JP 3551264B2
Authority
JP
Japan
Prior art keywords
image
correction
value
plant
amount
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
JP28624893A
Other languages
Japanese (ja)
Other versions
JPH07115846A (en
Inventor
謙隆 五味
浩 伊藤
哲久 南
茂也 吉川
宗宏 大城
芳樹 山野
正和 岡田
正夫 川村
信之 水谷
秀幸 浅野
哲 西岡
Original Assignee
アジア航測株式会社
日本アビオニクス株式会社
日本電気株式会社
東急建設株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by アジア航測株式会社, 日本アビオニクス株式会社, 日本電気株式会社, 東急建設株式会社 filed Critical アジア航測株式会社
Priority to JP28624893A priority Critical patent/JP3551264B2/en
Publication of JPH07115846A publication Critical patent/JPH07115846A/en
Application granted granted Critical
Publication of JP3551264B2 publication Critical patent/JP3551264B2/en
Anticipated expiration legal-status Critical
Expired - Fee Related legal-status Critical Current

Links

Images

Description

[0001]
[Industrial applications]
The present invention relates to a method for creating an image for evaluating fluctuations in plant vitality.
[0002]
[Prior art]
2. Description of the Related Art In recent years, surveys and monitoring of vegetation coverage and vitality of plants such as crops and forests on a wide area surface have been performed by artificial satellites and aircraft.
[0003]
However, the above-mentioned plant survey and monitoring by artificial satellites and aircraft are not suitable for monitoring plant vitality and its fluctuation in a specified narrow range because the ground resolution per pixel of a captured image is several meters to several tens of meters. Is not suitable.
[0004]
Thus, the present inventors, for example, to monitor fluctuations in plant vitality in a specified narrow range, such as a green grass on a golf course, for example, to discover early the deterioration of the growth state due to pests and the like, and to disperse the pesticide. We tried to develop a technology that can effectively cope with the minimum required amount of spraying and that can effectively monitor the effects of acid rain and acid fog on plants.
[0005]
In general, plants grow by receiving energy (light or electromagnetic waves) radiated from the sun. The reflection, absorption, and transmission characteristics of plants in the wavelength range from ultraviolet to near infrared depend on the surface or internal structure of the plant, pigments contained It is known that it is affected by the type and amount of water, the state of water, and the like, and changes depending on the type and growth state of the plant.
[0006]
It is known that the reflectance of the plant with respect to sunlight (natural light) (percentage of the amount of reflected light with respect to the amount of incident light) varies not only with the wavelength of light but also with the vitality of the plant, as is clear from FIG. ing. In particular, it can be seen that the wavelength changes around 540 nm (G) and 670 nm (R) in the visible light region, and changes greatly in the near infrared region near 850 nm (IR). Thus, even when comparing the spectral reflection characteristics of the same plant, the plant with high vitality shows a remarkably high reflectance in the near-infrared region, but the plant with reduced vitality due to pests and the like in the near-infrared region. The reflectivity drops significantly, and the degree of change is much greater than in the visible range.
[0007]
The present inventors have noted that the vitality of a plant and the reflectance (spectral reflectance) in a specific wavelength range have a correlation as described above, and that the video camera has a visible light region including the specific wavelength. Utilizing the wide sensitivity characteristics from the infrared to the near-infrared region, a video camera is adopted as an imaging device for monitoring the fluctuation of plant vitality, and the reflected light of the IR and R wavelengths is reflected by the video camera. We have developed a technology that collects information images, analyzes the obtained luminance information images, and finally creates a classification image of plant vitality and an evaluation image of fluctuation in vitality.
[0008]
[Problems to be solved by the invention]
In order to grasp the change in plant vitality, it is necessary to perform a time-series evaluation. For that purpose, video images having different shooting times, that is, different spectral radiation amounts, must be correctly normalized. Therefore, it is necessary to normalize the influence of the fluctuation of the sunlight, which is the illumination light, when shooting outdoors.
[0009]
The spectral radiation amount (spectral radiation energy amount) from sunlight in the field changes every moment affected by the altitude and azimuth of the sun, the amount of cloud, and the like. Due to the radiation dose, it does not accurately reflect the vitality of the plant. Therefore, in order to collect and accurately evaluate the reflection information (brightness) from plants as a video image, normalization based on a certain standard must be performed in order to remove the effect on the video image level from fluctuations in the amount of spectral radiation. Required.
[0010]
In addition, in order to obtain a stable video image (a video image output of about 0.5 V) required for analysis, it is necessary to adjust the amount of light incident on the lens to a level suitable for IR and R reflection information (brightness) from the turf at the time of shooting. The aperture must be adjusted. However, it is very difficult to manually adjust the aperture under ever-changing sunlight. This is because, in order to adjust the amount of light entering the video camera with a manual aperture, the worker constantly adjusts the aperture of the spectral radiation (spectral energy) from the illuminating light (sunlight), which changes every moment. Adjustments have to be made, which is not a realistic method. Further, since the aperture value changes at a multiple of the square root of 2, it is practically impossible to continuously adjust the amount of incoming light. In addition, the change in sunlight outdoors is continuous, and a manual aperture cannot cope with this change in the amount of incident light, and the aperture must be automatically adjusted. Note that there is an AGC (Auto Gain Control) circuit for automatically adjusting the amount of incident light, but since this circuit generally has no linear relationship between input values and output values, it is used as a means for normalization. Can not.
[0011]
Note that while the image output value distribution width (range) of an image captured with an appropriate aperture is wide and the effective dynamic range (the width of the maximum and minimum values that can be detected) is wide, the aperture setting is inappropriate. Therefore, in the case of an image in which the amount of incoming light is insufficient, the range of the image output value is narrowed. That is, the dynamic range of the image is narrowed, and the number of pixels of the maximum frequency is increased as compared with the case of the appropriate time. As a result, small changes are difficult to catch.
[0012]
In addition, if the amount of light entering the lens is changed by changing the aperture, the brightness information from the plant cannot be evaluated at a certain level. Therefore, it is necessary to eliminate the influence of the adjustment of the aperture.
[0013]
On the other hand, the effect of shading is different for an image with an insufficient amount of incoming light than an image with an appropriate amount of incoming light. At the same time, the S / N ratio of an image photographed with an appropriate amount of incident light increases, and the influence of shading hardly appears. Conversely, the effect of shading becomes more pronounced for an image with an insufficient amount of incoming light. In other words, it has been found that overcorrection occurs when an image having an insufficient amount of incident light is corrected using a shading correction image taken at an appropriate amount of incident light. Therefore, in order to eliminate this effect, it is necessary to remove shading corresponding to the amount of light incident upon photographing.
[0014]
The shading is affected by the video lens characteristics, the density unevenness due to the non-uniformity of the bandpass filter, and the video signal processing system of the video camera, that is, the density unevenness due to the non-uniformity of the photoelectric conversion element surface. The image output value is distorted due to the various density (light / dark) unevenness, and the vitality of the plant is not accurately reflected. Therefore, it is necessary to remove the influence of these irregularities as well.
[0015]
As described above, even if the luminance information image from the plant is corrected or processed, the vitality fluctuation of the plant cannot be displayed in a state that is easy for humans to understand. Therefore, it is necessary to create an image in which the variation in vitality can be easily grasped by performing a chronological evaluation after classifying the target plant into a health state (for example, health, disease, or the like).
[0016]
The present invention has been made to solve the above-mentioned conventional problems, and an object thereof is to analyze reflection information (brightness) from a plant captured by a video camera, that is, a luminance information image. An object of the present invention is to provide a method for creating an evaluation image of plant vitality fluctuation, which can obtain an image that accurately reflects the vitality fluctuation of a plant and can obtain an evaluation image that is easily understood by humans.
[0017]
[Means for Solving the Problems]
The method for creating an evaluation image of plant vitality fluctuation according to the present invention is to capture reflected light of a specific wavelength from a plant as a luminance information image using a video camera, perform an analysis process on the obtained image, and evaluate the activity fluctuation of the plant. In order to remove the influence of output unevenness due to shading etc. according to the amount of light entering the video camera at the time of shooting, the aperture amount and spectral The video camera shoots for each radiation amount, creates a shading / incident light amount correction image for each video image output voltage value, and calculates a CCT value near the center of the created correction image.hand, The correction standardvalueage,Dividing a CCT value of each pixel of the correction image by the correction reference value to obtain a correction coefficient image for each output voltage value of the video image;Using these correction coefficient images, the correction is performed by selecting the correction coefficient image closest to the aperture amount set when the plant was photographed and the CCT value near the center thereof.
[0021]
The relational expression between the spectral radiant energy of the illumination light at the time of measurement and the voltage value for controlling the aperture of the lens of the video camera is derived, and based on the relational expression, the level of the video image signal shot by the video camera is appropriate. The aperture may be automatically adjusted so that
[0022]
【Example】
Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
FIG. 1 is a schematic diagram of a system for implementing the method of the present invention, wherein 1 is a CCD video camera. The CCD video camera 1 includes two black and white module types for industrial measurement, and has a fixed focal point lens.
[0023]
One CCD video camera 1 has a built-in band-pass filter for a wavelength band (IR) centered at 850 nm, and the other CCD video camera 1 has a band for a wavelength band (R) centered at 670 nm. Built-in pass filter. These filters are mounted on the front of the CCD element, but may be arranged in front of the lens. In this embodiment, the image is captured by the CCD video camera. However, the present invention is not limited to this, and a video camera equipped with an imaging tube may be used.
[0024]
The same vegetation area (green of a golf course in this embodiment) of plants is photographed in parallel by the two CCD video cameras 1 and 1 to obtain an IR video image and an R video image. These video video signals (NTSC signals) are switched by the visual controller 2 and sequentially sent to the transmission parabolic antenna 3. In the above embodiment, IR and R video images are obtained by two CCD video cameras, respectively. However, the present invention is not limited to this, and two types of band pass filters are switched or prisms are used. By splitting the spectrum, IR and R video images may be obtained by one CCD video camera.
[0025]
On the other hand, reference numeral 4 denotes a spectral pyranometer for measuring the amount of radiation in a specific wavelength band in the spectral radiation amount (radiation energy amount) of the sun, and two units having high waterproofness are provided. That is, these spectral pyranometers 4 are provided with the same band-pass filter for IR and the same band-pass filter for R as the band-pass filter used for the CCD video camera 1, respectively. Data on the spectral radiation amount (radiation energy amount) in the wavelength band centered on 850 nm and 670 nm corresponding to the image is obtained. The radiation amount in the two wavelength bands may be measured by one spectral pyranometer 4 by switching and using two types of band pass filters.
[0026]
As shown in FIG. 2, there is a certain relationship between the output voltage of the IR spectral pyranometer 4 and the output voltage of the R spectral pyranometer 4. Therefore, the above-mentioned spectral pyranometer 4 is either one for R or IR, and the amount of spectral radiation not to be measured can also be obtained by the equation of this relationship.
[0027]
The data signal of the spectral radiation amount measured by the spectral pyranometer 4 is integrated and recorded by the data logger 5 and sent to the transmitting parabolic antenna 3 via the modem 6 and the connection box 7.
[0028]
In the video image, when the amount of spectral radiation increases, the image output value is saturated unless the aperture of the CCD video camera 1 is closed, and conversely, when the amount of spectral radiation decreases, the aperture is opened. If not, it will be dark, so the diaphragm is automatically set according to the increase or decrease of the spectral radiation amount, and the data signal of the diaphragm amount is sent to the transmission parabolic antenna 3 together with the data signal of the spectral radiation amount. The automatic setting of the iris derives the relationship between the spectral radiant energy of the illumination light at the time of measurement and the iris control voltage value of the lens, and adjusts the video image signal level to be appropriate based on the relational expression. .
[0029]
The transmission parabolic antenna 3 transmits the video signal of the IR and R video images, the data signal of the spectral radiation amount, and the data signal of the aperture amount by, for example, a millimeter wave. The video image signal and each data signal transmitted by the transmitting parabolic antenna 3 are received by the receiving parabolic antenna 8. Note that transmission and reception of these information signals may be performed by wire if necessary.
[0030]
The information signal received wirelessly or wired as described above is taken into the image processing device 9, and first, a video image signal, which is analog data, is converted into digital data. At this time, the luminance signal as the video image signal is converted into an image output value as a CCT (Computer Compatible Tape) value displayed in 256 gradations from 0 to 255.
[0031]
On the other hand, the spectral radiation data and the aperture data of the information signal are input to the personal computer 12. The information signals (video image signals and data signals) are recorded by a recording means such as a video tape at the shooting site or on the receiving side, and input to the image processing device 9 or the personal computer 12 when necessary. Is also good.
[0032]
As shown in FIG. 3, the image captured by the image processing device 9 is a shading / incident light amount correction, an aperture correction, a spectral radiation correction, a filter / video individual difference correction, a position adjustment correction, an inter-band calculation process, and a mask process. , Various kinds of corrections and processes such as a smoothing process and a level matching correction. Hereinafter, those corrections and processing will be described in detail.
[0033]
<< Shading / Incoming light amount correction >> An artificial light source is used on a reference plate having a constant reflectance (constant density), and an image is taken by the above-mentioned CCD video camera 1 for each aperture amount and spectral radiation amount, and shading is performed for each output voltage value of a video image.・ Create an image for correcting the amount of incoming light. Next, the CCT value near the center of the created correction image is calculated.hand, The correction standardvalueage,The CCT value of each pixel of the correction image is divided by the correction reference value to obtain a correction coefficient image for each output voltage value of the video image.
[0034]
Using these correction coefficient images, the correction coefficient image closest to the aperture amount set when the plant was photographed and the CCT value near the center is selected and corrected. This correction coefficient is calculated for each CCD video camera (including a lens and a filter) used.
[0035]
《Aperture correction》
Under the same illumination light, a gray scale plate or a white plate having a constant reflectance is photographed for each aperture value, and the relationship between the CCT value and the aperture value is determined. As shown in FIG. I understand. Based on this relationship, the following correction coefficient was calculated using the aperture 5.6 as a reference aperture value.
Where IRIR and RIR are the corrected images
IR, R: image before correction
X: Aperture value
AIR, AR: Image output value of reference aperture (5.6) obtained from regression equation
This correction coefficient is calculated for each CCD video camera (including a lens and a filter) used. Using the correction coefficient, each of the IR image and the R image photographed at different aperture values is normalized to an image photographed at the reference aperture value.
[0036]
《Spectral radiation correction》
When the CCT value obtained by video shooting with the CCD video camera 1 and the output voltage value obtained by the spectral pyranometer 4 are measured in synchronization with respect to a gray scale plate or a white plate having a constant reflectance under sunlight, As shown in FIG. 5, there is a certain relationship. By setting the CCT value corresponding to the distribution average value (center value) of the output voltage values in the field of each spectral pyranometer as the illumination reference value and inputting the spectral radiation amount at the time of photographing, the necessary correction coefficient is calculated as follows. calculate.
However, IRRC, RRC: image after correction
IR, R: image before correction
IRX: IR spectrophotometer measurement
RX (= f (IRX)): the measured value of the spectral solar radiation of R or the relational expression of the output of IR and the spectral pyranometer of R
IRM, RM: Reference CCT value
This correction coefficient is calculated for each CCD video camera (including a lens and a filter) used. Using the correction coefficient, each of the IR image and the R image is normalized using an image captured with the reference radiation value.
[0037]
《Filter / Video individual difference correction》
This correction is a conversion of the transmittance between the IR filter and the R filter to 100% and a correction of an individual difference such as a difference in the relative sensitivity of the CCD element of the CCD video camera 1.
First, the equation for filter correction is as follows. This calculation is performed separately for the bandpass filter for IR and the bandpass filter for R.
TC = IC * 100 / T
Here, TC: output image after correction (IR image: IRTC R image: RTC)
T: transmittance of the filter (IR image: IRT R image: RT)
The correction formula for the individual difference of the CCD video camera 1 is as follows.
KC = TC * (100 / KS)
KC: output image after correction (IR image: IRKC R image: RKC)
KS: relative sensitivity (IR image: IRKS R image: RKS)
[0038]
《Alignment correction》
The difference between the coordinates of any four points on the IR image and the coordinates of the same four points on the R image is calculated, and one of the coordinates is moved by the difference, and the position shift due to the difference in the viewpoint between the IR image and the R image. Is corrected. Note that this correction is omitted when one CCD video camera 1 shoots.
[0039]
《Inter-band calculation processing》
The following inter-band calculation is performed using the IR image and the R image to enhance the image.
BY = N * IRNY / RNY
Where BY is the output image after the calculation between the bands
IRNY: IR image before inter-band calculation
RNY: R image before calculation between bands
N: Image enhancement coefficient
[0040]
《Mask processing》
In order to evaluate the vitality of only green grass, if a portion other than green is photographed, a mask for setting the CCT value of that portion to 0 is applied. The mask processing image is created while viewing the IR image or the R image. More specifically, a polygon (image) is created by tracing the edge of the green on the screen with, for example, a mouse cursor, and an image for mask processing input with a CCT value of 1 inside and a CCT value of 0 outside is input. It is created, loaded into a RAM or image memory, and applied to the image after the above-mentioned inter-band calculation.
The correction formula is as follows.
MBY = BY * MS
Where MBY is the output image after mask processing
MS: Mask processing image
[0041]
《Smoothing processing》
In order to remove high-frequency noise and obtain a smooth image, a 3 × 3 pixel smoothing filter is applied to the image after the mask processing. When the CCT value of the pixel is 0, the calculation is performed without counting the pixel.
[0042]
《Level adjustment correction》
Even if the above-described normalization up to the spectral radiation correction is performed, an error of about ± 5% (about ± 10 counts in CCT value) occurs in the distribution position of the corrected image. For the purpose of performing time-series evaluation, “level adjustment” is performed as a correction method for correcting this error.
[0043]
By the way, when the vitality of a plant decreases due to a disease or the like, for example, the vitality of the entire surface of the green grass of a golf course hardly decreases at a stretch in one day, and spreads over the entire surface after a local disease occurs. It is thought that it often happens. Therefore, the distribution peak of the histogram of the video image that captures the situation where the vitality decreases should gradually decrease. Considering that, even if the peak gradually decreases, the area indicating “healthy” vitality information distributed on the high output value side of the histogram of the video image is as shown in FIG. 6 after a lapse of about one day. Then, on the high output value side on the histogram, it should be present at a position having substantially the same number of pixels.
[0044]
On the high output value side of the CCT value, there is a case where electric noises other than vegetation information of a plant with high vitality (that is, healthy) are mixed and cannot be removed only by smoothing. Therefore, in order to remove these noises, the number of effective pixels, which is a reference on the high output value side of the histogram at the position where vitality information from the healthy plant is distributed, is set to a line-like value based on experience so far. Considering the possibility of noise entering one line (the number of pixels of one line of the image processing device 9 used in the present system is 512), the number is specified to be 600 or more. Once the number of effective pixels, which is the distribution position of healthy turf, is specified, the level matching between the images of the previous day and the next day is determined by comparing the CCT value of the distribution position of the number of effective pixels of the previous day with the distribution of the number of effective pixels on the shooting day. By performing the addition / subtraction operation on the entire image by the difference between the CCT values of the positions, the error of the normalization could be improved to about ± 0.5%. Therefore, in this level adjustment correction, in order to use the CCT value at the position where healthy turf is distributed as a reference for level adjustment, it is premised that healthy turf exists in the captured image in principle.
[0045]
Next, a method of creating a classified image will be described. For example, as shown in FIG. 7, between the range of image output values (distribution width) of a healthy turf to a diseased turf, the thresholds of four vitality ranks of “health”, “caution”, “warning”, and “sickness” are shown. The display color of the screen is determined according to each rank, for example, green for health, yellow for caution, orange for warning, and red for sickness to obtain a pseudo-color classified image, such as classification, and the monitor 13 Projected on.
[0046]
As described above, since the classified images are always color-coded under a constant condition by the threshold value, an objective evaluation can be performed. In this classification image, since the conversion is performed using the threshold value for each rank, stretching (for each image) which has been conventionally performed is performed by coloring (coloring so that the change in the vitality of the calculation result between bands can be easily distinguished on the monitor). In order to display a pseudo color, there is no need to change the range of 0 to 255 so as to fit within a certain width.
[0047]
In the present invention, in order to express the fluctuation state of the vitality of the green grass, a classification image based on four ranks of “health”, “caution”, “warning”, and “sickness” is created. The threshold for classifying into four ranks is based on experiments (artificial light source, ideal conditions from a short distance) using video images from damaged turf (here, dry turf) and healthy turf. By acquiring and analyzing the data, we devised the empirical classification method described below.
[0048]
The threshold for classifying "health" and "attention" in this classification method was set at a position where the variation vector of the lawn vitality began to significantly decrease. The point where the vitality of the obstacle turf and the healthy turf in FIG. 8 intersect is ranked as the "caution" rank. That is, up to this position, although there is some vertical fluctuation, the vitality variation vector does not have a downward trend, and the vitality variation vector falls beyond this position, and the range in which the vitality has started to decrease is regarded as “attention”. . Therefore, if the vitality was higher than that position, it was regarded as “healthy”.
[0049]
Next, the threshold for classifying "attention" and "warning" is set to "attention" to the extent that turf that has started to lose energy may recover in a short time if natural healing or vitality restoration is taken. And That is, as shown in FIG. 9, when the vitality was reduced due to the drying obstacle, the position was set to a position where the vitality was temporarily recovered by giving water such as dew. Beyond that position, the further reduced range is set as the “warning” rank, and it is necessary to take immediate and appropriate measures. Finally, in the range of the “warning” rank, the range in which the vitality fluctuation vector further decreased and the leaf showed brown and died was regarded as “disease”.
[0050]
FIG. 10 shows the relationship between the time-series change of the image output values from the above-mentioned obstacle turf and healthy turf and the threshold value for classification, and Table 1 shows the threshold value and the color setting for pseudo-color display. The above threshold value is a result obtained in an experiment under ideal conditions, and may not always be applied to green turf in an actual golf course. Particularly, in the present invention, since the image output value at the position of the number of effective pixels is subjected to the addition / subtraction operation in order to perform the level adjustment as described above, the absolute value of the threshold cannot be used as it is.
[0051]
[Table 1]
[0052]
Therefore, the following method was tried to use this classification method. First, a green video image and spectral data to which the method of the present invention is applied are measured in advance, and the vitality of the turf is surveyed while receiving the advice of a local green keeper. From such information, for example, the image output value of the area of the green edge where the vitality is always low is defined as the "attention" of the threshold obtained in the above experiment, and the difference between the output value and the threshold of "attention" is applied to the entire image. By performing the addition / subtraction operation, the absolute value of the threshold created in the experiment can be adapted. If the green grass to be adapted is the same kind of grass as that used in the experiment and is photographed by the same CCD video camera, the distribution width (position) of the threshold is used as it is.
[0053]
As shown in FIG. 11, the green classified image of the actual golf course using the present classification method is “attention” around the green edge and around the hole, but “other” is “healthy”. It can be evaluated from a two-dimensional perspective that the green is suitable. This evaluation was consistent with the diagnosis of the green keeper.
[0054]
Next, creation of an evaluation image will be described. The above-mentioned classification image is an image display method for daily management, but in order to obtain an evaluation image showing a chronological change in the vitality of the plant, the difference between the classification images on a regular basis every day is taken, and the classification image of the previous day is obtained. The image output value of the classification image of the day is subtracted from the image output value, and 4 is added to calculate. As shown in Table 2, the calculated image output value obtains a pseudo-colorized evaluation image indicating the degree of change in vitality in seven levels and displays it on the monitor 13. By this evaluation image, for example, by the correspondence between the vitality fluctuation and the pseudo color, the degree of progress and spread of the disease etc., the degree of recovery by spraying pesticides, etc. can be grasped on the image, and the effectiveness of coping Is easy to see.
[0055]
[Table 2]
[0056]
In the present invention, if the shooting location and shooting specifications (video camera, video lens, etc.) change, or if the type of plant to be monitored or the environment in which the plant is located is significantly different, the data storage result by the above method or the local If a threshold is set each time based on the experience of the person in charge of management, it can be applied to plant monitoring in various fields.
[0057]
【The invention's effect】
Using an artificial light source on a reference plate having a constant reflectance, shooting is performed by the video camera for each of the aperture amount and the spectral radiation amount, and an image for correcting shading and incident light amount for each output voltage value of a video image is created. CCT value near the center of the corrected imagehand, The correction standardvalueage,Dividing a CCT value of each pixel of the correction image by the correction reference value to obtain a correction coefficient image for each output voltage value of the video image;By using these correction coefficient images, a correction coefficient image closest to the aperture amount set at the time of photographing the plant and the CCT value near the center is selected, and correction is performed. Can eliminate the influence of output unevenness due to shading or the like.
[Brief description of the drawings]
FIG. 1 is a block diagram of a system for implementing the method of the present invention.
FIG. 2 is a graph showing a relationship between an IR and an output voltage of R of the spectral pyranometer.
FIG. 3 is a flowchart of a process according to the method of the present invention.
FIG. 4 is a graph showing a relationship between an aperture and an image output value.
FIG. 5 is a graph showing a relationship between output voltage values of R and IR and a CCT value.
FIG. 6 is an explanatory diagram of level adjustment.
FIG. 7 is a diagram showing a threshold value of a plant vitality rank.
FIG. 8 is a graph showing time-series changes of video image output values of healthy turf and obstacle turf.
FIG. 9 is a graph showing time-series changes of video image output values of healthy turf and obstacle turf.
FIG. 10 is a graph showing time-series changes of video image output values of healthy turf and obstacle turf.
FIG. 11 is a diagram showing a level-matched image and an example of a classified image of a green grass at a golf course.
FIG. 12 is a diagram showing a change in the spectral reflectance of a plant depending on the vitality.
[Explanation of symbols]
1 CCD video camera
2 Visual controller
3 Transmission parabolic antenna
4 Spectroradiometer
5 Data logger
6 Modem
7 Connection box
8 Receiving parabolic antenna
9 Image processing device
10 Connection box
11 Modem
12 Personal computer
13 monitors

Claims (1)

  1. In a method of imaging a reflected light of a specific wavelength from a plant as a luminance information image by a video camera, and performing an analysis process on the obtained image to create an image for evaluating a change in activity of the plant, a video camera at the time of shooting In order to remove the effects of output unevenness due to shading etc. according to the amount of light entering the camera, the video camera performs shooting by the above-mentioned video camera for each aperture and spectral radiation using an artificial light source on a reference plate with a constant reflectance. A correction image of the shading / incident light amount for each output voltage value is calculated, a CCT value near the center of the generated correction image is calculated, and the calculated value is used as a correction reference value, and each pixel of the correction image is calculated. a CCT value is divided by the correction reference value, obtain a correction coefficient images for each output voltage value of the video image, using these correction coefficients the image, set the time of performing the photographing of the plant And the aperture amount, select the closest correction coefficient image and a CCT value near the center, evaluation image creation method of the plant vigor variations and performing correction.
JP28624893A 1993-10-22 1993-10-22 Method of creating evaluation image of plant vitality fluctuation Expired - Fee Related JP3551264B2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP28624893A JP3551264B2 (en) 1993-10-22 1993-10-22 Method of creating evaluation image of plant vitality fluctuation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
JP28624893A JP3551264B2 (en) 1993-10-22 1993-10-22 Method of creating evaluation image of plant vitality fluctuation

Publications (2)

Publication Number Publication Date
JPH07115846A JPH07115846A (en) 1995-05-09
JP3551264B2 true JP3551264B2 (en) 2004-08-04

Family

ID=17701909

Family Applications (1)

Application Number Title Priority Date Filing Date
JP28624893A Expired - Fee Related JP3551264B2 (en) 1993-10-22 1993-10-22 Method of creating evaluation image of plant vitality fluctuation

Country Status (1)

Country Link
JP (1) JP3551264B2 (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3468877B2 (en) * 1994-10-27 2003-11-17 矢崎総業株式会社 Plant automatic diagnosis method and apparatus
WO2004030378A2 (en) * 2002-09-24 2004-04-08 Hasselblad A/S Image quality indicator
DE102011120858A1 (en) * 2011-12-13 2013-06-13 Yara International Asa Method and device for contactless determination of plant parameters and for processing this information
WO2017130236A1 (en) * 2016-01-29 2017-08-03 パナソニックIpマネジメント株式会社 Turf growing device, turf growing system, and turf management system
CN106680277A (en) * 2016-12-29 2017-05-17 深圳前海弘稼科技有限公司 Method and device for monitoring plant diseases and insect pests in planting equipment

Also Published As

Publication number Publication date
JPH07115846A (en) 1995-05-09

Similar Documents

Publication Publication Date Title
Rosell et al. A review of methods and applications of the geometric characterization of tree crops in agricultural activities
Saberioon et al. Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale
US10761211B2 (en) Plant treatment based on morphological and physiological measurements
Underwood et al. Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors
Sakamoto et al. An alternative method using digital cameras for continuous monitoring of crop status
KR101829850B1 (en) Systems and methods for spatially controlled scene illumination
JP2016049102A (en) Farm field management system, farm field management method, and program
US20170308750A1 (en) System and method for field variance determination
Nascimento et al. Statistics of spatial cone-excitation ratios in natural scenes
US8391565B2 (en) System and method of determining nitrogen levels from a digital image
Rossel Regional differences in photoreceptor performance in the eye of the praying mantis
US7613360B2 (en) Multi-spectral fusion for video surveillance
EP1655620B1 (en) Obstacle detection using stereo vision
Rasmussen et al. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?
US6178253B1 (en) Method of determining and treating the health of a crop
Plant et al. Relationships between remotely sensed reflectance data and cotton growth and yield
US5661817A (en) Single charge-coupled-device camera for detection and differentiation of desired objects from undesired objects
Herrmann et al. Ground-level hyperspectral imagery for detecting weeds in wheat fields
Yang A high-resolution airborne four-camera imaging system for agricultural remote sensing
CN107426958A (en) Agricultural monitoring system and method
Párraga et al. Spatiochromatic properties of natural images and human vision
Petach et al. Monitoring vegetation phenology using an infrared-enabled security camera
US6567537B1 (en) Method to assess plant stress using two narrow red spectral bands
JP6365668B2 (en) Information processing apparatus, device, information processing system, control signal production method, program
US5130545A (en) Video imaging plant management system

Legal Events

Date Code Title Description
A521 Written amendment

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20031128

A131 Notification of reasons for refusal

Free format text: JAPANESE INTERMEDIATE CODE: A131

Effective date: 20031224

RD04 Notification of resignation of power of attorney

Free format text: JAPANESE INTERMEDIATE CODE: A7424

Effective date: 20040213

A521 Written amendment

Free format text: JAPANESE INTERMEDIATE CODE: A821

Effective date: 20040213

A521 Written amendment

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20040219

TRDD Decision of grant or rejection written
RD02 Notification of acceptance of power of attorney

Free format text: JAPANESE INTERMEDIATE CODE: A7422

Effective date: 20040310

A01 Written decision to grant a patent or to grant a registration (utility model)

Free format text: JAPANESE INTERMEDIATE CODE: A01

Effective date: 20040316

A61 First payment of annual fees (during grant procedure)

Free format text: JAPANESE INTERMEDIATE CODE: A61

Effective date: 20040414

R150 Certificate of patent or registration of utility model

Free format text: JAPANESE INTERMEDIATE CODE: R150

S531 Written request for registration of change of domicile

Free format text: JAPANESE INTERMEDIATE CODE: R313531

R350 Written notification of registration of transfer

Free format text: JAPANESE INTERMEDIATE CODE: R350

R250 Receipt of annual fees

Free format text: JAPANESE INTERMEDIATE CODE: R250

FPAY Renewal fee payment (event date is renewal date of database)

Free format text: PAYMENT UNTIL: 20090514

Year of fee payment: 5

FPAY Renewal fee payment (event date is renewal date of database)

Free format text: PAYMENT UNTIL: 20090514

Year of fee payment: 5

S111 Request for change of ownership or part of ownership

Free format text: JAPANESE INTERMEDIATE CODE: R313117

R360 Written notification for declining of transfer of rights

Free format text: JAPANESE INTERMEDIATE CODE: R360

FPAY Renewal fee payment (event date is renewal date of database)

Free format text: PAYMENT UNTIL: 20090514

Year of fee payment: 5

FPAY Renewal fee payment (event date is renewal date of database)

Free format text: PAYMENT UNTIL: 20090514

Year of fee payment: 5

R370 Written measure of declining of transfer procedure

Free format text: JAPANESE INTERMEDIATE CODE: R370

S111 Request for change of ownership or part of ownership

Free format text: JAPANESE INTERMEDIATE CODE: R313117

R350 Written notification of registration of transfer

Free format text: JAPANESE INTERMEDIATE CODE: R350

FPAY Renewal fee payment (event date is renewal date of database)

Free format text: PAYMENT UNTIL: 20090514

Year of fee payment: 5

FPAY Renewal fee payment (event date is renewal date of database)

Free format text: PAYMENT UNTIL: 20100514

Year of fee payment: 6

FPAY Renewal fee payment (event date is renewal date of database)

Free format text: PAYMENT UNTIL: 20100514

Year of fee payment: 6

FPAY Renewal fee payment (event date is renewal date of database)

Free format text: PAYMENT UNTIL: 20110514

Year of fee payment: 7

LAPS Cancellation because of no payment of annual fees