KR20200107351A - 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 distribution and content of specific substances in pine trees. More specifically, the present invention relates to the apparatus for detecting an arsenic content in the pine trees using hyperspectral data in which a specific element, particularly arsenic content in the pine trees is measured by analyzing images acquired using a hyperspectrometer, and a method thereof. An exploring apparatus includes a detection unit and an analysis unit.

Description

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

The present invention relates to a technology for exploring the distribution and content of a specific substance in a pine tree, and more particularly, using hyperspectral data to measure the content of specific elements, especially 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 pollutants are contained in the soil, and wastewater, sewage, and waste mixed with pollutants are thrown into the soil or the soil may be polluted through mineralization around the soil. Major soil pollutants include cadmium, mercury, lead, zinc, arsenic and hexavalent chromium. The above soil pollutants lead to disturbances in the growth of crops and harm to humans and livestock while passing through the food chain. Therefore, there is a demand for the development of technology to identify the state of soil pollution and improve it through soil pollution degree exploration.

Conventionally, the distribution or content of pollutants was analyzed through sample collection and chemical analysis in the field for soil pollution survey. For example, an atomic absorption spectroscopy method in which the collected soil is acid-decomposed or pretreated, and the amount of ultraviolet or visible light absorbed by the atom is measured after atomization by injecting a flame, or the sample is placed on a high-frequency induction coil. Inductively coupled plasma-atomic emission spectroscopy, which performs qualitative and quantitative analysis of elements by measuring the emission line and emission angle emitted by atoms when injected into the argon plasma formed by heating to 6000K ~ 8000K.

The conventional soil contamination analysis method as described above has disadvantages that the analysis process is complicated, which takes a long time to analyze and increases the cost. In order to determine the soil contamination distribution, it is necessary to collect samples for each unit area. There was a problem that it was difficult to measure the pollution degree distribution.

Recently, a spectroscopic imaging technology has been developed that enables highly accurate identification of physical properties and traits of a sample by separating the received light into hundreds to hundreds of wavelength information using a spectroscopic imaging sensor and a spectroscopic image sensor to obtain an image.

These spectroscopic imaging technologies were initially mounted on aircraft or satellites for military use and used to identify targets.For civil use, remote sensing fields such as cultivation conditions of crops, distribution of minerals, and global environmental surveys, and field selection, cell analysis It is being used for.

Existing heavy metal contamination exploration has a problem that makes precise exploration impossible because the measurement range is too wide, specific substances cannot be accurately detected, and only the approximate element distribution of a mixture can be measured. There is a need to develop a technology for exploring the content of specific substances.

In particular, the heavy metal pollution exploration through aerial photography has a disadvantage in that the soil is not exposed in the case of an area covered with vegetation, making it impossible to detect heavy metal pollution using soil.

The present invention has been conceived to solve the above problems, and an object of the present invention is to analyze the hyperspectral image data of pine trees obtained by using a hyperspectrometer to measure the arsenic content of pine trees using hyperspectral data. It is to provide an apparatus and method for exploring the arsenic content in.

In addition, it is intended to provide an apparatus and method for detecting arsenic content in pine trees using hyperspectral data that can immediately analyze the arsenic content in pine trees in the field using a portable spectrometer.

In particular, based on the spectroscopic characteristics of pine trees contaminated with arsenic among heavy metal elements, the objective is to provide an apparatus 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 pine trees by using the reflectivity of a hyperspectral image in which light irradiated to pine leaves in a specific wavelength range is reflected. It features.

In addition, the probe may include: a detector configured to obtain a reflected hyperspectral image by irradiating a light source onto 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.

In addition, the detection unit includes an optical information transmission unit for transmitting the hyperspectral image to the analysis unit by wire or wirelessly, and the analysis unit includes an optical information reception unit for receiving a hyperspectral image transmitted through the optical information transmission unit. Include.

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

In addition, the probe is characterized in that the content or distribution of arsenic is measured through reflectivity of light reflected through a wavelength of 550 to 600 nm or 675 to 725 nm.

In accordance with an embodiment of the present invention, a method for detecting an arsenic content in a pine tree using hyperspectral data includes: a light source irradiation step of irradiating a light source to the pine tree (S10); A hyperspectroscopy image acquisition step (S20) of obtaining a hyperspectroscopy image in which a light source irradiated to the pine tree is reflected; An image storage step of storing the hyperspectral image (S40); An arsenic content calculation step (S50) of analyzing the stored hyperspectroscopy image to calculate an arsenic content in the pine tree; And an arsenic content providing step (S60) of providing the calculated arsenic content.

In addition, the detection method further includes an image transmission step (S30) of wired or wirelessly transmitting the hyperspectral image obtained 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 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 having the configuration as described above has an effect of detecting the degree of heavy metal contamination of pine trees by detecting arsenic contained in pine trees through a hyperspectral image. .

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

Furthermore, there is an effect that it is possible to quantitatively infer the degree of heavy metal contamination of the surrounding soil by detecting the degree of heavy metal contamination of pine trees in areas where pollution degree using aerial photography of the soil is not possible 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 flow chart of a method of exploring arsenic content in pine trees using hyperspectral data according to an embodiment of the present invention
3 is a graph showing the reflectivity of light reflected from pine leaves according to the wavelength of light
4 is a graph showing a hull index curve according to the wavelength of a pine sample

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

In the case of vegetation-covered areas, since the soil is not exposed, it is impossible to detect heavy metal contamination in the soil 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 plants. This is possible.

In particular, about 64% of the Republic 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 metal contamination of the soil in the region can be investigated.

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 (hereinafter referred to as'exploration apparatus') in pine trees using hyperspectral data according to an embodiment of the present invention.

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 an image acquired through the detection unit 10 Consists of

When the pine tree contains arsenic, the exploration device 100 of the present invention causes a variation in the content of chlorophyll, and this is indicated by spectroscopic properties, so that the content of arsenic contained in the pine tree is determined using a hyperspectral image of the pine tree. It is characterized by detecting.

More specifically, the detection unit 10 includes a light source unit 11 that irradiates light to the pine tree, a reflectivity measurement unit 12 that receives the hyperspectral image reflected by the light irradiated from the light source unit 11 and reflected from the pine tree, It includes 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 light with a halogen light source in a region of visible light, infrared light, and short-wave infrared light (350 to 2500 nm) on the soil to be measured.

The reflectivity measurement unit 12 is configured to receive the reflected spectral image by reflecting the light irradiated from the light source unit 11 from the pine leaf. The reflectivity measuring unit 12 may be configured to measure the reflectivity by arranging pine leaf samples at a constant thickness on a black-surface and in parallel and vertically facing each other. The reflectivity measurement unit 12 may be configured to optimize the measured image information and white-balance to obtain accurate information. The reflectivity measurement unit 12 divides the light-receiving information into three ranges of 350 to 1000 nm, 1000 to 1800 nm, and 1800 to 2500 nm, and rearranges the optical information measured at a resolution of 3 to 6 nm into a reflectivity value in units of 1 nm, so that the optical information is stored. It can be configured to be transmitted. The reason why the light-receiving information is divided into three areas is to detect a wavelength area sensitive to arsenic content. For example, the optical properties that can occur as plants accumulate arsenic in the body are due to a decrease in the amount of chlorophyll. The change in optical properties clearly occurs in the visible-near infrared band (350-1000 nm). The wavelength range required for the arsenic content in pine trees will be described in detail in the arsenic content exploration method described later.

The optical information transmission unit 13 is configured to transmit the spectral image received through the reflectivity measurement unit 12 to the analysis unit 20 wirelessly or by wire. When the optical information transmission unit 13 is configured in a wired manner, 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 It can be connected to and configured integrally.

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 optimum wavelength region from the measured wavelength region based on the arsenic content prediction equation constructed in the past or input from the user and applies it to a standard regression equation or a 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 a 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 an arsenic content calculation unit 23 that calculates the content of arsenic 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 above optical information receiving unit 21, the database 22, the arsenic content calculation unit 23 and the display unit 24 is configured to include a control unit 25 that controls the overall control.

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 to be directly input by a user. The database unit 22 may store spectral information measured by the detection unit 10 and a predicted arsenic content. The arsenic content calculation unit 23 performs mathematical transformation on the spectral information by smoothing, first derivative of reflectance, and Hull-quotient curve or light absorption characteristic modification. After that, the wavelength band related to the arsenic content in the pine tree sample is extracted from the mathematically modified light-receiving information. Finally, the calculation formula for the arsenic content in pine trees is derived through the extracted wavelength band. According to an embodiment of the present invention, the measured spectral reflectance has been transformed into a hull index curve, and the absorption depth and absorption width within a range of 576 ~ 700 nm can be calculated to estimate the arsenic content in pine trees. The display unit 24 provides the user with the arsenic content in the pine tree calculated by the arsenic content calculating unit 23. In this case, it may be provided to evaluate the pollution degree of the surrounding environment according to the arsenic content in the pine trees compared to the standard values of the arsenic content of the plants in the world. The control unit 25 controls the optical information receiving unit 21, the database 22, the arsenic content calculation unit 23, and the display unit 24 as a whole, and displays an error message on the display unit when a problem occurs. It can be configured to predict the content. The control unit 25 is configured to be able to determine whether the light-receiving information received from the light-receiving unit 21 is properly installed and measured, whether the optimization work has been properly performed, and whether error information in the received light-receiving information exists I can. The control unit 25 may be configured to check whether the memory of the database unit 22 is sufficient, and whether the existing server and the analysis unit are normally connected. In addition, the control unit 25 may include reliability and error evaluation for 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 pine trees using an apparatus for detecting arsenic content in pine trees using hyperspectral data according to an embodiment of the present invention will be described in detail with reference to the drawings. FIG. 2 shows a flow chart 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 reflectivity of light reflected from pine leaves according to the wavelength of light. Is shown.

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

Next, a hyperspectroscopy image acquisition step (S20) of acquiring a hyperspectroscopy image in which the light source irradiated to the pine tree is reflected through the reflectivity measurement unit 12 is performed.

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

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

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

Referring to FIG. 3, it can be seen that the higher the reflectivity of pine leaves with a higher arsenic content depending on the wavelength, and in particular, in the wavelength range of 550 to 600 nm or 675 to 725 nm, the wavelength band significantly changes according to the arsenic content. .

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

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

The previously extracted wavelength can be mathematically modified by smoothing, first derivative of reflectance, hull index curve, or light absorption characteristic modification to emphasize the physiological change of the plant. In this embodiment, the absorption depth between 576nm and 700nm is calculated 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 a correction curve of the hull index of the reflectivity according to the wavelength of the pine sample.

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

). (4) The wavelength with the minimum value (

) Exists in the equation of the straight line between the 576nm and 700nm points (

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

In addition, after calculating the absorption depth through the above equation, the arsenic content in the pine tree can be calculated from Equation 3 below.

The above coefficients are empirical equations calculated using the data used in the experiment, and Equation 3 is the 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 with the minimum value of the hull index value of 576~700nm is 670nm,

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

Silver 0.276,

Assuming that is 0.40, at 670nm, which is the minimum wavelength,

Is 0.306. Using Equation 2, the absorption depth can be calculated as 0.206. Substituting this value into Equation 3 to infer the content of arsenic, it can be seen that the predicted content of arsenic is 0.949mg/kg.

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

The technical idea should not be interpreted as limited to the above-described embodiment of the present invention. As well as a variety of application ranges, various modifications can be made at the level of those skilled in the art without departing from the gist of the present invention claimed in the claims. Therefore, these improvements and changes will fall within the scope of protection of the present invention as long as it is apparent to those skilled in the art.

100: arsenic content detection device in pine
10: detection unit
11: light source unit
12: reflectivity measurement unit
13: Optical information transmission unit
20: analysis unit
21: optical information receiver
22: database
23: arsenic content calculation unit
24: display unit
25: control unit

Claims (9)

An apparatus for detecting arsenic content in pine trees using hyperspectral data, characterized in that measuring the content of arsenic in pine trees by using the reflectivity of a hyperspectral image in which light irradiated to pine leaves in a specific wavelength range is reflected.
The method of claim 1,
The exploration device,
A detector for obtaining a reflected hyperspectral image by irradiating a light source onto the pine tree;
An analysis unit for analyzing the content of arsenic in the pine tree by analyzing the hyperspectral image of the detection unit;
Containing, an arsenic content exploration device in pine trees using hyperspectral data.
The method of claim 2,
The detection unit,
An optical information transmission unit for transmitting the hyperspectral image to the analysis unit by wire or wirelessly,
The analysis unit,
Arsenic content detection device in pine trees using hyperspectral data, including an optical information receiving unit for receiving a hyperspectral image transmitted through the optical information transmitting unit.
The method of claim 3,
The detection unit is composed of 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 on a repeater. Arsenic content detection device in pine trees using hyperspectral data
The method of claim 1,
The exploration device is characterized in that to measure the content or distribution of arsenic through the reflectivity of light reflected through a wavelength of 550 ~ 600nm or 675 ~ 725nm range, arsenic content detection device using hyperspectral data.
In the detection method using the arsenic content detection device in pine trees using the hyperspectral data of any one of claims 1 to 5,
Light source irradiation step (S10) of irradiating a light source to the pine tree;
A hyperspectroscopy image acquisition step (S20) of obtaining a hyperspectroscopy image in which a light source irradiated to the pine tree is reflected;
An image storage step of storing the hyperspectral image (S40);
An arsenic content calculation step (S50) of analyzing the stored hyperspectroscopy image to calculate an arsenic content in the pine tree; And
Arsenic content providing step (S60) of providing the calculated arsenic content;
Containing, a method of exploring arsenic content in pine trees using hyperspectral data.
The method of claim 6,
The exploration method,
An image transmission step (S30) of wired or wirelessly transmitting the hyperspectral image obtained by the detection unit 10 to the analysis unit 20;
A method for exploring arsenic content in pine trees using hyperspectral data further comprising a.
The method of claim 6,
The arsenic content is calculated through the absorption depth of the hyperspectral image of the pine sample, and the absorption depth is derived through the equation below.

The method of claim 8,
The arsenic content, characterized in that derived through the equation below, using hyperspectral data in the arsenic content exploration method.

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