Method, device, and computer program for managing non-native plants

Abstract

A method for managing non-native plants according to an embodiment of the present application is characterized by comprising the steps of: obtaining an image of an expected non-native plant distribution area captured by a drone; generating a point cloud model for plants in the expected non-native plant distribution area by using the image; generating, on the basis of the point cloud model, a metric including classification variables based on vegetation characteristics and environmental characteristics; and classifying non-native plants and native plants on the basis of the classification variables by using the metric, and managing the non-native plants. According to an embodiment of the present invention, non-native plants can be effectively classified and managed.

Description

Methods, devices, and computer programs for managing alien plants

[0001] The present application relates to a method, apparatus, and computer program for managing alien plants, and specifically to a method, apparatus, and computer program for managing alien plants that can classify and manage alien plants using drone images.

[0002] Existing alien plant classification and management systems have primarily relied on RGB-based drone imagery or field surveys. While LiDAR-based 3D data is highly useful for classifying alien plants, its high cost prevents its widespread use, limiting its application across various projects. Additionally, although RGB-based drone imagery is cost-effective, it fails to adequately reflect 3D terrain information and plant characteristics, thus limiting its ability to accurately distinguish between alien and native species. Furthermore, field surveys require significant manpower and time, making it difficult to effectively monitor large areas in real-time. Moreover, existing methods fail to precisely reflect the distinct characteristics of alien and native plants, resulting in limitations in accurate classification and response. Finally, conventional approaches suffer from a lack of continuous monitoring and management following alien plant removal, leading to the problem of recurring alien plant outbreaks in the same areas.

[0003] The problem that the present invention aims to solve is to provide a method, device, and computer program for managing alien plants that can classify alien plants with very high accuracy using high-resolution drone images and 3D point clouds.

[0004] In addition, the problem that the present invention aims to solve is to provide a method, device, and computer program for managing alien plants that can more precisely distinguish between alien plants and native plants by accurately reflecting the unique characteristics of the terrain and plants through a 3D model.

[0005] In addition, the problem that the present invention aims to solve is to provide a method, device, and computer program for managing alien plants that can clearly evaluate the distribution and influence of alien plants through 3D-based analysis and prepare effective countermeasures against them.

[0006] The problems that the present invention aims to solve are not limited to those described above, and problems not mentioned will be clearly understood by those skilled in the art from this specification and the attached drawings.

[0007] A method for managing alien plants according to one embodiment of the present invention comprises the steps of: acquiring an image of an area where alien plants are expected to be distributed, captured by a drone; generating a point cloud model for plants in the area where alien plants are expected to be distributed, using the image; generating a metric based on the point cloud model, including classification variables based on vegetation characteristics and environmental characteristics; and classifying alien plants and native plants based on the classification variables using the metric and managing the alien plants.

[0008] An alien plant management device according to one embodiment of the present invention includes a processor for managing alien plants based on an image of an alien plant distribution area obtained from a drone, wherein the processor includes an image acquisition module for acquiring the image captured from the drone, a point cloud model generation module for generating a point cloud model for plants in the alien plant distribution area using the image, a metric generation module for generating a metric including classification variables based on vegetation characteristics and environmental characteristics based on the point cloud model, and a management module for classifying alien plants and native plants based on the classification variables using the metric and managing the alien plants.

[0009] The means for solving the problem of the present invention are not limited to the means for solving the problem described above, and unmentioned means for solving the problem will be clearly understood by those skilled in the art from this specification and the attached drawings.

[0010] According to one embodiment of the present invention, alien plants and native plants can be precisely classified by comprehensively considering not only the structural characteristics of plants (e.g., height, leaf width) and reflectance intensity, but also the characteristics of the habitat environment (e.g., slope, orientation, altitude).

[0011] In addition, according to one embodiment of the present invention, by utilizing 2.5D and 3D models instead of 2D image-based analysis, it is possible to perform precise analysis that reflects not only the spatial distribution and structural characteristics of plants but also the characteristics of their habitat. In particular, through analysis that considers the interaction between topography and plants, high accuracy can be provided for plant classification in complex terrain.

[0012] Furthermore, according to one embodiment of the present invention, the distribution, habitat characteristics, and potential diffusion pathways of alien plants can be predicted, and removal and management strategies can be effectively established accordingly. Through this, efficient allocation of resources and strategic response required for the management of alien plants are possible.

[0013] In addition, according to one embodiment of the present invention, objective and reliable decision-making based on quantitative data is supported. This allows for the evaluation of the effectiveness of alien plant removal operations and the optimization of management strategies.

[0014] In addition, according to one embodiment of the present invention, the results of the analysis through 2.5D and 3D models can be visualized to intuitively verify and utilize the results of alien plant classification. This enables more accurate judgment and response during the alien plant management process.

[0015] FIG. 1 is a diagram showing the schematic components of an alien plant management system according to one embodiment of the present application.

[0016] FIG. 2 is a schematic block diagram of an exotic plant management device according to one embodiment of the present application.

[0017] FIG. 3 is a schematic block diagram of a processor equipped in an exotic plant management device according to one embodiment of the present application.

[0018] FIG. 4 is a flowchart illustrating a method for managing exotic plants according to one embodiment of the present application.

[0019] FIG. 5 is a drawing for illustrating an example of a 2.5D point cloud model according to one embodiment of the present application.

[0020] FIG. 6 is a drawing for illustrating an example of a 3D point cloud model according to one embodiment of the present application.

[0021] FIGS. 7a to 7d are drawings illustrating an example of a metric generated by reflecting vegetation characteristics and environmental characteristics for a point cloud model according to an embodiment of the present application.

[0022]

[0023] The aforementioned objectives, features, and advantages of the present application will become more apparent from the following detailed description in conjunction with the accompanying drawings. However, as the present application is subject to various modifications and may have various embodiments, specific embodiments are illustrated in the drawings and described in detail below.

[0024] Throughout the specification, identical reference numbers generally represent identical components. Additionally, components with identical functions within the same scope of concept appearing in the drawings of each embodiment are described using the same reference numeral, and redundant descriptions thereof are omitted.

[0025] If it is determined that a detailed description of known functions or configurations related to this application could unnecessarily obscure the essence of this application, such detailed description is omitted. Furthermore, numbers used in the description of this specification (e.g., First, Second, etc.) are merely identifiers to distinguish one component from another.

[0026] Furthermore, the suffixes "module" and "part" for components used in the following embodiments are assigned or used interchangeably solely for the ease of drafting the specification, and do not inherently possess distinct meanings or roles.

[0027] In the following examples, singular expressions include plural expressions unless the context clearly indicates otherwise.

[0028] In the following embodiments, terms such as "include" or "have" mean that the features or components described in the specification are present, and do not preclude the possibility that one or more other features or components may be added.

[0029] In the drawings, the size of components may be exaggerated or reduced for convenience of explanation. For example, the size and thickness of each component shown in the drawings are arbitrarily depicted for convenience of explanation, and the present invention is not necessarily limited to what is illustrated.

[0030] Where an embodiment can be implemented differently, the order of a particular process may be performed differently from the order described. For example, two processes described consecutively may be performed substantially simultaneously or proceed in the reverse order of the description.

[0031] In the following embodiments, when components are described as being connected, the case includes not only instances where the components are directly connected but also instances where components are indirectly connected by interposing them in between.

[0032] For example, when it is stated in this specification that components, etc. are electrically connected, it includes not only cases where the components, etc. are directly electrically connected, but also cases where components, etc. are interposed in between and are indirectly electrically connected.

[0033] Hereinafter, the method, apparatus, and system for managing alien plants of the present application will be described with reference to FIGS. 1 to 7.

[0034] FIG. 1 is a diagram showing the schematic components of an alien plant management system according to one embodiment of the present application.

[0035] Referring to FIG. 1, an alien plant management system (1) according to one embodiment of the present application may include a drone (200) and an alien plant management device (100). However, the present invention is not limited thereto, and the alien plant management system (1) may include additional components or some components may be omitted. Some components of the alien plant management system (1) may be separated into a plurality of devices, or a plurality of components may be merged into a single device. For example, the alien plant management device (100) may represent a server device.

[0036] The drone (200) may represent a drone device that captures an area where alien plants are expected to be distributed and acquires an image of the area where alien plants are expected to be distributed. For example, the drone (200) may be equipped with an RGB sensor. For example, the drone (200) may acquire an RGB image using the RGB sensor. For example, the drone (200) may be an unmanned aerial vehicle (UAV) equipped with a shooting device. For example, although one drone (200) is shown in FIG. 1, the alien plant management system (1) may include two or more multiple drones (200).

[0037] The drone (200) and the alien plant management device (100) can be connected to each other via a network to exchange data. For example, the alien plant management device (100) can obtain an image of the expected alien plant distribution area from the drone (200).

[0038] The alien plant management device (100) can convert 2D RGB images collected from a drone into 2.5D or 3D, generate various metrics based on this, and establish an alien plant response strategy based on the metrics. The alien plant management device (100) can generate a 2.5D point cloud model or a 3D point cloud model based on the RGB images acquired from the drone. Additionally, the alien plant management device (100) can analyze the point cloud to generate metrics based on environmental characteristics such as altitude, slope, and aspect, as well as vegetation characteristics of the plants. Additionally, the alien plant management device (100) can classify alien plants and native plants using the metrics. Furthermore, the alien plant management device (100) can visualize the alien plant classification results and, if necessary, perform additional data analysis and alien plant management measures.

[0039] FIG. 2 is a schematic block diagram of an alien plant management device according to one embodiment of the present application. FIG. 3 is also a schematic block diagram of a processor equipped in an alien plant management device according to one embodiment of the present application.

[0040] First, referring to FIG. 2, an alien plant management device (100) according to one embodiment of the present application may include a memory (110), a processor (120), and a communication module (130). The alien plant management device (100) may be a server device provided in an alien plant management system (1).

[0041] The communication module (130) can provide a function for communicating with an external device through a network. For example, the alien plant management device (100) can connect to a network through the communication module (130) to transmit and receive various data. The transmitting and receiving unit may include a wired type and a wireless type. Since the wired type and the wireless type each have their own advantages and disadvantages, the alien plant management device (100) may be equipped with both a wired type and a wireless type depending on the case. Here, in the case of the wireless type, communication methods of the WLAN (Wireless Local Area Network) series, such as Wi-Fi, can be mainly used. Alternatively, in the case of the wireless type, cellular communication, such as LTE or 5G series communication methods, can be used. However, wireless communication protocols are not limited to the examples described above, and it is possible to use any appropriate wireless type of communication method. In the case of the wired type, LAN (Local Area Network) or USB (Universal Serial Bus) communication are representative examples, and other methods are also possible.

[0042] Memory (110) is a computer-readable recording medium, and memory (110) can store various information. Various data can be stored in memory (110) temporarily or semi-permanently. Examples of memory (110) may include a hard disk drive (HDD), a solid state drive (SSD), flash memory, ROM (Read-Only Memory), and RAM (Random Access Memory). Memory (110) may be provided in a form that is embedded in the alien plant management device (100) or in a detachable form. Memory (110) may store various data necessary for the operation of the alien plant management device (100), including an operating system (OS) for operating the alien plant management device (100) and a program for operating each component of the alien plant management device (100).

[0043] Referring to FIG. 2 and FIG. 3 together, a processor (120) according to one embodiment of the present application can control the overall operation of an alien plant management device (100). For example, the processor (120) can manage alien plants based on an image of an alien plant distribution area obtained from a drone (200). More specifically, the processor (120) may include an image acquisition module (121), a point cloud model generation module (122), a metric generation module (123), and a management module (124).

[0044] The processor (120) can load and execute a program for the overall operation of the alien plant management device (100) from memory (110). The processor (120) may be implemented as an Application Processor (AP), a Central Processing Unit (CPU), a Microcontroller Unit (MCU), or a similar device depending on hardware, software, or a combination thereof. In this case, hardware-wise, it may be provided in the form of an electronic circuit that processes electrical signals to perform control functions, and software-wise, it may be provided in the form of a program or code that drives the hardware circuit.

[0045] The image acquisition module (121) can acquire an image of an area where alien plants are expected to be distributed, captured by a drone. Additionally, the point cloud model generation module (122) can generate a point cloud model of plants in the area where alien plants are expected to be distributed using the image of the area where alien plants are expected to be distributed. Additionally, the metric generation module (123) can generate a metric that includes classification variables based on vegetation characteristics and environmental characteristics based on the point cloud model. Additionally, the management module (124) can classify alien plants and native plants based on the classification variables using the metric and manage alien plants.

[0046] Hereinafter, with reference to FIGS. 4 to 7, a method for managing alien plants according to one embodiment of the present application will be described. The method for managing alien plants according to one embodiment of the present application can be performed by an alien plant management device (100) shown in FIG. 2.

[0047] First, FIG. 4 is a flowchart illustrating a method for managing alien plants according to one embodiment of the present application. Referring to FIG. 4, in step S110, an alien plant management device according to one embodiment of the present application can acquire an image of an area where alien plants are expected to be distributed, captured by a drone. For example, an alien plant management device according to one embodiment of the present application can acquire an RGB image captured by a drone equipped with an RGB sensor.

[0048] In step S120, an alien plant management device according to one embodiment of the present application may generate a point cloud model for plants in an alien plant distribution area using an image of an alien plant distribution area. For example, an alien plant management device according to one embodiment of the present application may generate a 2.5D point cloud model or a 3D point cloud model based on a 2D RGB image acquired from a drone.

[0049] In step S130, an alien plant management device according to one embodiment of the present application may generate a metric including classification variables based on vegetation characteristics and environmental characteristics based on a point cloud model. For example, the metric may include a vegetation metric including classification variables based on the vegetation characteristics of alien plants. Additionally, the metric may include an environmental metric including classification variables based on the environmental characteristics of alien plants. For example, the vegetation metric may include a reflection intensity representing the intensity of light reflected according to the structure and surface characteristics of each plant's leaves. For example, the environmental metric may include slope, aspect, and elevation based on X, Y, and Z coordinates extracted from a 3D point cloud.

[0050] For example, the metrics generated by the alien plant management device according to one embodiment of the present application may include a dispersion metric that analyzes the spatial structure of a plant through the distribution of points on a point cloud model, a Leaf Area Density (LAD) metric that numerically represents the leaf structure of a plant, a Percentiles metric that divides the height of a plant into various ratios, a Canopy Density metric that calculates the density of the upper layer of a plant, a Rumple Index metric that measures the unevenness of the plant surface, and a Kernel Density Estimation (KDE) metric that analyzes the plant distribution based on the density of the point cloud model.

[0051] In step S140, an alien plant management device according to one embodiment of the present application can classify alien plants and native plants based on classification variables using metrics and manage alien plants. For example, an alien plant management device according to one embodiment of the present application can classify alien plants using vegetation metrics and environmental metrics. In addition, an alien plant management device according to one embodiment of the present application can predict the distribution and diffusion paths of alien plants using vegetation metrics and environmental metrics. Furthermore, an alien plant management device according to one embodiment of the present application can establish removal and management strategies for alien plants based on the distribution and diffusion paths of alien plants.

[0052] FIG. 5 is a drawing for illustrating an example of a 2.5D point cloud model according to one embodiment of the present application. FIG. 6 is also a drawing for illustrating an example of a 3D point cloud model according to one embodiment of the present application.

[0053] FIGS. 5A and 5B illustrate an example of a 2.5D point cloud model generated based on RGB images of plants in an area where alien plants are expected to be distributed, according to an embodiment of the present invention. Additionally, FIG. 6 illustrates an example of a 3D point cloud model generated based on RGB images of plants in an area where alien plants are expected to be distributed, according to an embodiment of the present invention. For example, a point cloud represents a data set consisting of a plurality of points distributed in 3D space.

[0054] An alien plant management device according to one embodiment of the present invention can acquire RGB images from a drone. In addition, an alien plant management device according to one embodiment of the present invention may undergo an image preprocessing process to remove noise and correct the acquired RGB images. Furthermore, an alien plant management device according to one embodiment of the present invention may generate a 2.5D point cloud model or a 3D point cloud model based on the preprocessed images.

[0055] FIGS. 7a and 7b are drawings illustrating an example of a metric generated by reflecting vegetation characteristics and environmental characteristics for a point cloud model according to one embodiment of the present application.

[0056] An alien plant management device according to one embodiment of the present invention can generate a metric including classification variables for classifying alien plants based on a point cloud model. Additionally, an alien plant management device according to one embodiment of the present invention can classify alien plants based on the metric including classification variables. Furthermore, an alien plant management device according to one embodiment of the present invention can visualize alien plant classification results by displaying them on a map or a 3D viewer. Additionally, an alien plant management device according to one embodiment of the present invention can verify classification results by analyzing the accuracy of the alien plant classification results.

[0057] A metric according to one embodiment of the present invention may include a classification variable representing a statistical value that can be calculated in 2.5D or 3D.

[0058] This invention utilizes various metrics to precisely classify alien and native plants. One of the important metrics is reflection intensity, which refers to the intensity of light reflected back from the plant surface by a LiDAR signal; this intensity varies depending on the plant's surface characteristics and structural differences. Generally, plants with broad, thick leaves tend to reflect more light and exhibit high reflection intensity, while plants with small leaves and numerous branches, which scatter light, may show relatively low reflection intensity. Additionally, plants with smooth surfaces possess high reflectivity, whereas plants with rough surfaces or strong light-absorbing properties may exhibit low reflection intensity. As such, differences in reflection intensity arise depending on the structural and surface characteristics of plants, and these differences provide important information for distinguishing between alien and native plants. By analyzing the distribution and fluctuation of reflection intensity, characteristics of different plant species can be effectively distinguished, which can then be utilized to improve the accuracy of plant classification.

[0059] In addition, environmental metrics such as slope, aspect, and elevation are used based on X, Y, and Z coordinates extracted from a 3D point cloud. For example, since the vine-like plant *Xanthium strumarium* has the characteristic of growing well even on steep embankments or slopes, utilizing the slope metric helps predict the distribution of alien plants in such environments. Meanwhile, for plants such as *Scutellaria baicalensis*, which thrive in shade, growth patterns related to the direction of sunlight can be analyzed using the aspect metric, thereby identifying the characteristics of alien plants that grow better in specific environments. The elevation metric analyzes the elevation range in which specific plants grow, enabling plant classification that reflects habitat characteristics based on elevation. For example, as illustrated in Fig. 7a, ZMAX is a metric representing the highest height value of a plant, and ZMEAN is a metric representing the average height value of a plant.

[0060] In this invention, various additional metrics are utilized to analyze the distribution and characteristics of alien plants more accurately. Dispersion metrics are used to analyze the spatial structure of plants through the distribution of points on a point cloud, and Leaf Area Density (LAD) metrics can measure characteristics such as whether leaves are dense or sparse by numerically representing the leaf structure of plants. Additionally, percentile metrics allow for the evaluation of distribution by dividing the height of plants into various ratios, which is useful for distinguishing plants of various sizes and shapes within a plant community.

[0061] The Canopy Density metric calculates the density of the upper layer of plants, enabling differentiation between plants with and without a dense canopy structure, while the Rumple Index measures surface unevenness and is used to analyze complex plant structures. Finally, the Kernel Density Estimation (KDE) metric can analyze plant distribution in a specific area based on the density of a point cloud, which is effective for tracking the invasion paths of alien plants or analyzing their distribution patterns.

[0062] By comprehensively utilizing such various metrics, the present invention can more precisely distinguish between alien and native plants, enable data-based analysis considering the growth environment and characteristics of each plant, and provide important information for establishing management and response strategies.

[0063] The features, structures, effects, etc. described in the embodiments above are included in at least one embodiment of the present invention and are not necessarily limited to only one embodiment. Furthermore, the features, structures, effects, etc. exemplified in each embodiment may be combined or modified and implemented in other embodiments by a person skilled in the art to which the embodiments belong. Accordingly, details regarding such combinations and modifications should be interpreted as being included within the scope of the present invention.

[0064] Furthermore, although the embodiments have been described above, this is merely illustrative and does not limit the invention. Those skilled in the art will understand that various modifications and applications not exemplified above are possible within the scope of the essential characteristics of the embodiments. In other words, each component specifically shown in the embodiments may be modified and implemented. Differences related to such modifications and applications should be interpreted as being included within the scope of the invention as defined in the appended claims.

Claims

1. A step of generating a point cloud model for plants in the predicted alien plant distribution area using the above image; A step of generating a metric including classification variables based on vegetation characteristics and environmental characteristics based on the above point cloud model; and A step of classifying alien plants and native plants based on the classification variables using the above metric and managing the alien plants; A method for managing alien plants, including 2. In Paragraph 1, A method for managing alien plants, wherein the step of acquiring the above image includes the step of acquiring an RGB image captured from an RGB drone.

3. In Paragraph 1, A method for managing alien plants, wherein the step of generating the point cloud model includes the step of generating a 2.5D point cloud model or a 3D point cloud model based on the 2D image.

4. In Paragraph 3, A method for managing alien plants, wherein the step of generating the above metric includes generating a vegetation metric that includes a reflection intensity representing the intensity of light reflected according to the structure and surface characteristics of each plant's leaf.

5. In Paragraph 4, A method for managing alien plants, wherein the step of generating the above metric includes the step of generating an environmental metric including slope, aspect, and elevation based on X, Y, and Z coordinates extracted from the 3D point cloud.

6. In Paragraph 5, A method for managing alien plants, comprising the step of generating the above metrics, which includes generating a dispersion metric that analyzes the spatial structure of a plant through the distribution of points on the point cloud model, a Leaf Area Density (LAD) metric that numerically expresses the leaf structure of a plant, a Percentiles metric that divides the height of a plant into various ratios, a Canopy Density metric that calculates the density of the upper layer of a plant, a Rumple Index metric that measures the unevenness of the plant surface, and a Kernel Density Estimation (KDE) metric that analyzes the plant distribution based on the density of the point cloud model.

7. In Paragraph 5, The step of managing the above-mentioned alien plants is, A step of classifying the alien plants using the vegetation metric and the environment metric; A step of predicting the distribution and diffusion path of the alien plant using the vegetation metric and the environment metric; and A method for managing alien plants, comprising the step of establishing a removal and management strategy for the alien plants based on the distribution and diffusion paths of the alien plants.

8. A computer-readable recording medium storing a program for executing a method according to any one of claims 1 through 7.

9. In a device for managing alien plants, A processor for managing alien plants based on images of predicted alien plant distribution areas obtained from a drone; The above processor is, An image acquisition module for acquiring the above image captured from a drone; A point cloud model generation module that generates a point cloud model for plants in the predicted alien plant distribution area using the above image; A metric generation module that generates a metric including classification variables based on vegetation characteristics and environmental characteristics based on the above point cloud model; and A management module that classifies alien plants and native plants based on the classification variables using the above metric and manages the alien plants; An alien plant management device including 10. In a system for managing alien plants, A drone for acquiring images of areas expected to have alien plant distribution; and A processor for managing alien plants based on the image obtained from the drone; comprising The above processor is, An image acquisition module for acquiring images captured from a drone; A point cloud model generation module that generates a point cloud model for plants in the predicted alien plant distribution area using the above image; A metric generation module that generates a metric including classification variables based on vegetation characteristics and environmental characteristics based on the above point cloud model; and A management module that classifies alien plants and native plants based on the classification variables using the above metric and manages the alien plants; An alien plant management system including

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