KR102260033B1 - Arsenic Exploration Apparatus and Method in Pine Trees Based on Hyper-Spectral data - Google Patents

Abstract

The present invention relates to a technique for exploring the distribution and content of specific substances in pine trees, and more particularly, using hyperspectral data to measure the content of specific elements, particularly arsenic, in pine trees by analyzing images acquired using a hyperspectrometer. It relates to an apparatus and method for detecting arsenic content in pine trees.

Description

Arsenic Exploration Apparatus and Method in Pine Trees Based on Hyper-Spectral data}

The present invention relates to a technique for exploring the distribution and content of specific substances in pine trees, and more particularly, using hyperspectral data to measure the content of specific elements, particularly arsenic, in pine trees by analyzing images acquired using a hyperspectrometer. It relates to an apparatus and method for detecting arsenic content in pine trees.

Soil pollution is a phenomenon in which contaminants are contained in the soil, and wastewater, sewage, and waste mixed with contaminants are thrown into the soil or the soil is contaminated through mineralization around the soil. Major soil pollutants include cadmium, mercury, lead, zinc, arsenic, and hexavalent chromium. Soil pollutants such as above cause growth disorders of crops and harm people and livestock while passing through the food chain. Therefore, there is a demand for the development of technology to identify the actual condition of soil contamination through soil contamination survey and to improve it.

Conventionally, for soil contamination level exploration, the distribution or content of pollutants was analyzed through sampling and chemical analysis in the field. For example, the atomic absorption spectroscopy method, which determines the compound in the sample by measuring the amount of ultraviolet or visible light absorbed by atoms, after acid decomposition or pretreatment of the collected soil, atomization by injecting a flame, or the sample in a high-frequency induction coil There is an inductively coupled plasma-atomic emission spectroscopy method that performs qualitative and quantitative analysis of elements by measuring the emission lines and emission angles emitted by atoms when heated to 6000K to 8000K by injecting into an argon plasma formed by

The conventional soil contamination analysis method as described above has the disadvantage of taking a long analysis time and increasing the cost due to the complicated analysis process. There was a problem in that it was difficult to measure the pollution degree distribution.

Recently, a spectroscopic imaging technology has been developed that allows the physical properties and characteristics of a sample to be identified very accurately by dividing the received light into hundreds to hundreds of wavelength information using a spectrophotometer and a spectral image sensor to acquire an image.

This spectroscopic imaging technology was initially used for target identification by being mounted on aircraft or satellites for military use, and for civilian use in remote sensing fields such as crop cultivation status, mineral distribution, global environmental survey, job selection, and cell analysis fields. is being used for

Existing heavy metal contamination exploration has a problem that precise exploration is impossible because the measurement range is too wide, it cannot accurately detect a specific substance, and can only measure the element distribution of an approximate mixture. The development of technology to explore the content of specific substances is required.

In particular, heavy metal pollution exploration through aerial photography has a disadvantage in that heavy metal pollution exploration using soil is not possible because the soil is not exposed in areas covered with vegetation.

The present invention has been devised to solve the above problems, and an object of the present invention is to analyze hyperspectral image data of pine trees obtained using a hyperspectral meter to measure the arsenic content of pine trees using hyperspectral data. To provide an apparatus and method for detecting arsenic content.

Another object of the present invention is to provide an apparatus and method for detecting arsenic content in pine trees using hyperspectral data that can immediately analyze the content of arsenic in pine trees in the field using a portable spectrometer.

In particular, based on the spectroscopic characteristics of pines contaminated with arsenic among heavy metal elements, it is to provide a device and method for detecting arsenic content in pine trees using hyperspectral data that can accurately detect the arsenic content of pine leaves using a portable spectrometer. .

The apparatus for detecting arsenic content in pine trees using hyperspectral data according to an embodiment of the present invention measures the arsenic content in a pine tree using the reflectivity of a hyperspectral image in which light irradiated to a pine leaf in a specific wavelength region is reflected. characterized in that

In addition, the exploration device, irradiating a light source to the pine tree to obtain a reflected hyperspectral image, a detector; and an analysis unit for analyzing the content of arsenic in the pine tree by analyzing the hyperspectral image of the detection unit.

In addition, the detector includes an optical information transmitter that transmits the hyperspectral image to the analyzer by wire or wirelessly, and the analyzer includes an optical information receiver configured to receive the hyperspectral image transmitted through the optical information transmitter. include

In addition, the detection unit is configured as a portable package, and when the hyperspectral image is transmitted over a wire, the analysis unit is integrally configured with the detection unit, and when transmitted wirelessly, the analysis unit is installed in a repeater.

In addition, the exploration device is characterized in that it measures the content or distribution of arsenic through the reflectivity of light reflected through a wavelength of 550 ~ 600nm or 675 ~ 725nm region.

An exploration method using an arsenic content exploration apparatus in a pine tree using hyperspectral data according to an embodiment of the present invention includes: a light source irradiation step (S10) of irradiating a light source to the pine tree; A hyperspectral image acquisition step (S20) of acquiring a hyperspectral image in which the light source irradiated to the pine tree is reflected; an image storage step of storing the hyperspectrometer image (S40); an arsenic content calculation step (S50) of analyzing the stored hyperspectrometer image to calculate the arsenic content in the pine tree; and an arsenic content providing step (S60) of providing the calculated arsenic content.

In addition, the exploration method further includes an image transmission step (S30) of wire or wirelessly transmitting the hyperspectral image acquired by the detection unit 10 to the analysis unit 20 .

In addition, the arsenic content is calculated through the absorption depth of the hyperspectral image of the pine tree sample, and the absorption depth is characterized in that it is derived through the following equation.

In addition, the arsenic content is characterized in that it is derived through the following equation.

The apparatus and method for detecting arsenic content in pine trees using hyperspectral data of the present invention according to the above configuration has an effect of detecting arsenic contained in pine trees through hyperspectral images to explore the degree of contamination of heavy metals in pine trees. .

In particular, since the arsenic contamination level of pine trees is investigated using a portable spectrometer, it is possible to immediately analyze the contamination level of a wide area at the site.

Furthermore, there is an effect of quantitatively inferring the degree of heavy metal lure in the surrounding soil by detecting the level of heavy metal contamination of pine trees in an area where it is impossible to explore soil contamination using aerial photography due to vegetation.

1 is a block diagram of an apparatus for detecting arsenic content in pine trees using hyperspectral data according to an embodiment of the present invention;
2 is a flowchart of a method for exploring arsenic content in pine trees using hyperspectral data according to an embodiment of the present invention;
3 is a graph showing the reflectance of light reflected from a pine leaf according to the wavelength of light;
4 is a graph showing the hull index curve according to the wavelength of the pine tree sample

Before describing the present invention, the basic principle of the present invention will be briefly described. When arsenic is contained in the soil, the chlorophyll content of plants growing in the soil changes due to arsenic, and the internal structure of the leaf is destroyed, resulting in changes in spectroscopic properties. Since there is a section in which the reflectivity of leaves exposed to a specific wavelength band changes rapidly depending on the presence or absence of arsenic content in the soil, this was used to measure the distribution or content of specific substances in pine trees.

This is because the soil is not exposed in the case of the area covered with vegetation, so heavy metal contamination of the soil cannot be investigated by methods such as aerial photography, so it is used as an indicator of metal contamination of the soil by using the spectroscopic characteristics according to the arsenic content of the leaves of the plant. This is possible.

In particular, about 64% of Korea is forest, and most of the territory is covered with vegetation, and pine is the most native tree species in Korea. Therefore, if the arsenic content of pine leaves is measured, it is expected that it will be possible to explore the metal contamination of the soil in that area.

Hereinafter, an embodiment of the present invention as described above will be described in detail with reference to the drawings. 1 is a block diagram of an arsenic content exploration apparatus 100 in a pine tree using hyperspectral data according to an embodiment of the present invention (hereinafter, 'exploring apparatus').

As shown, the exploration device 100 includes a detection unit 10 for acquiring a hyperspectral image of a pine tree, and an analysis unit 20 for analyzing the arsenic content in the pine tree using the image acquired through the detection unit 10 . is composed of

The exploration device 100 of the present invention causes a variation in the content of chlorophyll when the pine tree contains arsenic, which is shown as a spectroscopic characteristic. Through this, the content of arsenic contained in the pine tree is determined using a hyperspectral image of the pine tree. characterized by detection.

More specifically, the detection unit 10 includes a light source unit 11 for irradiating light on the pine tree, and a reflectivity measurement unit 12 for receiving a hyperspectral image in which the light irradiated from the light source unit 11 is reflected and reflected from the pine tree; It is configured to include an optical information transmission unit 13 for wirelessly or wired transmission of the hyperspectral image received through the reflectivity measurement unit 12 to the analysis unit 20 .

The light source unit 11 is configured to irradiate visible light, infrared light, and a region up to short wave infrared (350-2500 nm) to the soil to be measured with a halogen light source.

The reflectivity measurement unit 12 is configured to receive a spectral image reflected by the light irradiated from the light source unit 11 by being reflected from the pine leaf. The reflectivity measuring unit 12 may be configured to measure the reflectivity by vertically arranging a leaf sample of a pine tree to a predetermined thickness and in parallel on a black-surface. The reflectivity measuring unit 12 may be configured to optimize and white-balance the measured image information to obtain accurate information. The reflectivity measurement unit 12 divides the light reception information into three domain ranges of 350 to 1000 nm, 1000 to 1800 nm, and 1800 to 2500 nm, and rearranges the light information measured with a resolution of 3 to 6 nm into reflectance values in units of 1 nm, so that the optical information is may be configured to be transmitted. The reason for dividing the light reception information into three regions is to detect a wavelength region sensitive to arsenic content. For example, the optical properties that may occur as plants accumulate arsenic in the body are due to a decrease in the amount of chlorophyll. The change in the optical properties clearly occurs in the visible-near-infrared band (350-1000 nm). The wavelength range required for the arsenic content in pine will be described in detail in the arsenic content exploration method, which will be described later.

The optical information transmission unit 13 is configured to wirelessly or wiredly transmit the spectral image received through the reflectivity measurement unit 12 to the analysis unit 20 . When the optical information transmission unit 13 is configured as a wire, it may be a fiber-optic cable connecting the detection unit 10 and the analysis unit 20 , and the detection unit 10 and the analysis unit 20 . connected to and may be integrally configured.

The detector 10 may be configured to be portable and may be configured to obtain a hyperspectral image in a state close to a pine tree. When the detection unit 10 and the analysis unit 20 are connected by wire, the detection unit 10 and the analysis unit 20 may be integrally configured, and the detection unit 10 and the analysis unit 20 are wirelessly connected. When connected, the analysis unit 20 may be installed inside a repeater such as a moving means.

The analysis unit 20 extracts the optimal wavelength region from the wavelength region measured based on the arsenic content prediction equation in the pine tree that has been built or input from the user and applies it to the standard regression equation or the prediction equation. Accordingly, an arsenic content predicted value is derived and this information can be provided to the user. The analysis unit 20 includes an optical information receiving unit 21 for receiving the hyperspectral image transmitted from the optical information transmitting unit 13 and a database 22 for storing the hyperspectral image received through the optical information receiving unit 21 . ) and the arsenic content calculation unit 23 that calculates the arsenic content in the pine tree using the hyperspectral image stored in the database 22, and the arsenic content analysis result calculated through the arsenic content calculation unit 23 to the user The display unit 24 and the light information receiving unit 21, the database 22, the arsenic content calculation unit 23, and is configured to include a control unit 25 to control the display unit 24 as a whole.

The database unit 22 may be configured to receive a standard regression equation for predicting arsenic content in pine trees provided by an existing server, or directly input by a user. The database unit 22 may store the spectral information and the predicted arsenic content measured by the detection unit 10 . The arsenic content calculating unit 23 performs mathematical transformations on the spectral information by smoothing, a first derivative of reflectance, and a Hull-quotient curve or light absorption characteristic transformation. Then, the wavelength band related to the arsenic content in the pine sample is extracted from the mathematically transformed light reception information. Finally, the calculation formula for the arsenic content in the pine tree is derived through the extracted wavelength band. According to an embodiment of the present invention, the measured spectral reflectivity is transformed into a hull index curve, and the arsenic content in the pine tree can be predicted by calculating the absorption depth and absorption area in the 576-700 nm region. The display unit 24 provides the arsenic content in the pine tree calculated by the arsenic content calculating unit 23 to the user. In this case, it may be provided to evaluate the pollution degree of the surrounding environment according to the arsenic content in the pine tree by comparing it with the arsenic content standard value of each country in the world. The control unit 25 controls the optical information receiving unit 21, the database 22, the arsenic content calculating unit 23 and the display unit 24 as a whole, and when a problem occurs, an error message is displayed on the display unit to provide accurate It can be configured to predict the content. The control unit 25 is configured to be able to determine whether the light reception information received by the light information receiving unit 21 is properly installed and measured, whether the optimization work is properly performed, and whether there is error information in the received light reception information. can The control unit 25 may be configured to check whether the memory of the database unit 22 is sufficient, and whether the previously constructed server and the analysis unit are normally connected. In addition, the control unit 25 may include reliability and error evaluation for the qualitative and quantitative evaluation of heavy metals before the arsenic content calculating unit 23 derives the arsenic content in the pine tree and transmits it to the display unit 24 .

Hereinafter, a method for detecting arsenic content in a pine tree using an apparatus for detecting an arsenic content in a pine tree using hyperspectral data according to an embodiment of the present invention as described above will be described in detail with reference to the drawings. 2 is a flowchart of a method for detecting zinc and cadmium in soil using a portable spectrometer according to an embodiment of the present invention, and FIG. 3 is a graph showing the reflectance of light reflected from pine leaves according to the wavelength of light is shown.

First, the light source irradiation step (S10) of irradiating the light source to the pine tree using the light source unit 11 of the detection unit 10 is performed.

Next, a hyperspectral image acquisition step (S20) of acquiring a hyperspectral image reflected by the light source irradiated on the pine tree through the reflectivity measuring unit 12 is performed.

Next, an image transmission step (S30) of transmitting the hyperspectral image to the analyzing unit 20 through the optical information transmitting unit 13 and receiving it through the optical information receiving unit 21 of the analyzing unit 20 is performed. .

Next, an image storage step ( S40 ) of storing the received image in the database 22 is performed.

Next, the arsenic content calculation step (S50) of calculating the arsenic content in the pine tree by analyzing the stored hyperspectrometer image is performed.

Referring to FIG. 3 , the higher the arsenic content, the higher the reflectivity according to the wavelength appears, and the wavelength band significantly changes according to the arsenic content in the wavelength range of 550 to 600 nm or 675 to 725 nm. .

Therefore, it is possible to measure the arsenic content and content of pine trees by analyzing the reflectivity in the wavelength range of 550~600nm or 675~725nm.

Wavelength bands that change significantly depending on the arsenic content can be extracted using statistical analysis. According to the present embodiment, the wavelength region that is statistically significantly changed according to the arsenic content corresponds to about 576 nm and 700 nm. The increase in reflectivity in the wavelength region indicates that the arsenic content caused physiological changes in the plant, such as a decrease in chlorophyll in the plant.

The previously extracted wavelength may be subjected to mathematical transformation such as smoothing, first derivative of reflectance, hull index curve, or absorption characteristic transformation in order to emphasize physiological changes in plants. This embodiment calculates the absorption depth (Depth) between 576 nm and 700 nm using the hull index curve, and the arsenic content in the pine tree can be calculated from the following equation.

4 is a graph showing the hull index correction curve of the reflectivity according to the wavelength of the pine tree sample.

As shown (1), the reflectivity of wavelengths of 350 to 2500 nm measured in the pine tree sample is transformed into a hull index curve. (2) Find the equation of a straight line connecting the points of 576nm and 700nm on the hull index curve. In the case of the present embodiment, the equation of the straight line may be expressed as Equation 1 below. In Equation 1, x is the position of the wavelength, y is the hull index value, and HQ means the hull index curve.

)을 구한다. (4) 최솟값을 갖는 파장(

)이 576nm와 700nm지점을 지은 직선의 방정식 상에 존재하는 위치(

)를 구한다. (5) 최종적으로 흡광깊이는 식 2를 통해 산출될 수 있다.(3) The point with the minimum value among the hull index values of 576~700nm (

) to find (4) the wavelength with the minimum (

) exists on the equation of the straight line connecting the points 576 nm and 700 nm (

) to find (5) Finally, the absorption depth can be calculated through Equation 2.

In addition, after calculating the absorption depth (Depth) through the above equation, the arsenic content in the pine tree can be calculated from the following equation (3).

The above coefficients are empirical formulas calculated using data used in the experiment, and Equation 3 is a value derived through regression analysis.

가 0.10이라고 가정하면, 식 1의 직선의 방정식에서

은 0.276,

은 0.40라고 가정할 때, 최솟값을 갖는 파장인 670nm에서의

는 0.306 이다. 식 2를 이용하면 흡광깊이는 0.206로 산출될 수 있다. 이 값을 식 3에 대입하여 비소의 함량을 유추하면, 예측된 비소의 함량은 0.949mg/kg임을 알 수 있다.For example, the wavelength having the minimum value among the hull index values of 576 to 700 nm is 670 nm,

Assuming that is 0.10, in the equation of the straight line in Equation 1,

is 0.276,

Assuming that is 0.40, at 670 nm, the wavelength with the minimum value,

is 0.306. Using Equation 2, the absorption depth can be calculated as 0.206. When the arsenic content is inferred by substituting this value into Equation 3, it can be seen that the predicted arsenic content is 0.949 mg/kg.

Finally, the arsenic content providing step S60 of providing the calculated arsenic content through the display unit 24 is performed.

The technical idea should not be construed as being limited to the above-described embodiment of the present invention. Various modifications can be made at the level of those skilled in the art without departing from the gist of the present invention as claimed in the claims. Accordingly, such improvements and modifications fall within the protection scope of the present invention as long as it is apparent to those skilled in the art.

100: arsenic content exploration device in pine
10: detection unit
11: light source unit
12: reflectance measuring unit
13: optical information transmission unit
20: analysis unit
21: optical information receiving unit
22: database
23: arsenic content calculation unit
24: display unit
25: control unit

Claims (9)

To measure the content of arsenic in a pine tree using the reflectivity of a hyperspectral image in which light irradiated to a pine leaf in a specific wavelength region is reflected.
A detection unit for obtaining a reflected hyperspectral image by irradiating a light source to the pine tree; and
an analysis unit analyzing the hyperspectral image of the detection unit to analyze the content of arsenic in the pine tree; including,
The analysis unit,
After transforming the reflectivity of wavelengths from 350 to 2500 nm measured in the pine tree sample to the Hull Index curve, the first point having the minimum value among the Hull Index values between two specific points on the Hull Index curve, and the wavelength having the minimum value Hyperspectral data for calculating the arsenic content through the absorption depth by obtaining the second point existing on the equation of the straight line connecting the specific two points, and defining the difference between the second point and the first point as the absorption depth Arsenic content exploration device in pine trees using
delete The method of claim 1,
The detection unit,
and an optical information transmitter for transmitting the hyperspectrometer image to the analyzer by wire or wirelessly,
The analysis unit,
Arsenic content exploration apparatus in a pine tree using hyperspectral data, including an optical information receiver for receiving a hyperspectral image transmitted through the optical information transmitter.
4. The method of claim 3,
The detection unit is configured in a portable package,
When the hyperspectral image is transmitted by wire, the analysis unit is integrally configured with the detection unit, and when transmitted wirelessly, the analysis unit is installed in a repeater. An apparatus for detecting arsenic content in pine trees using hyperspectral data
The method of claim 1,
The arsenic content exploration device in a pine tree using hyperspectral data, characterized in that the exploration device measures the content or distribution of arsenic through the reflectivity of light reflected through a wavelength of 550 to 600 nm or 675 to 725 nm.
Light source irradiation step of irradiating a light source to the pine tree (S10);
A hyperspectral image acquisition step (S20) of acquiring a hyperspectral image in which the light source irradiated to the pine tree is reflected;
an image storage step of storing the hyperspectrometer image (S40);
an arsenic content calculation step (S50) of analyzing the stored hyperspectrometer image to calculate the arsenic content in the pine tree; and
an arsenic content providing step of providing the calculated arsenic content (S60); including,
The arsenic content calculation step (S50) is,
After transforming the reflectivity of wavelengths from 350 to 2500 nm measured in the pine tree sample to the Hull Index curve, the first point having the minimum value among the Hull Index values between two specific points on the Hull Index curve, and the wavelength having the minimum value Hyperspectral data for calculating the arsenic content through the absorption depth by obtaining the second point existing on the equation of the straight line connecting the specific two points, and defining the difference between the second point and the first point as the absorption depth A method of arsenic content exploration in pine trees using
7. The method of claim 6,
The exploration method is
an image transmission step (S30) of wire or wirelessly transmitting the hyperspectral image acquired by the detection unit 10 to the analysis unit 20;
Further comprising, arsenic content exploration method in pine using hyperspectral data.
7. The method of claim 6,
The arsenic content is calculated through the absorption depth of the hyperspectral image of the pine tree sample, and the absorption depth is derived through the following equation, arsenic content exploration method in pine trees using hyperspectral data.

9. The method of claim 8,
The arsenic content is, characterized in that derived through the following formula, arsenic content exploration method in pine trees using hyperspectral data.

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