WO2022065367A1

WO2022065367A1 - Logging time deteremination program

Info

Publication number: WO2022065367A1
Application number: PCT/JP2021/034808
Authority: WO
Inventors: 綾子澤田
Original assignee: Ａｓｓｅｓｔ株式会社
Priority date: 2020-09-24
Filing date: 2021-09-22
Publication date: 2022-03-31

Abstract

[Problem] To determine, with high accuracy, the time to log trees in a forest. [Solution] This logging time determination program determines the time to log trees in a forest, wherein the program is characterized by causing a computer to execute: an information acquisition step for acquiring aerial image information obtained by imaging, from the air, a forest for which a tree logging time is to be newly determined; and a determination step for referencing three or more stages of linkage between reference aerial image information obtained by imaging the forest from the air in the past and a tree logging time for the forest, and determining a tree logging time by prioritizing the higher linkage, on the basis of the reference aerial image information corresponding to the aerial image information acquired in the information acquisition step.

Description

Logging time determination program

The present invention relates to a logging time determination program for determining the logging time in a forest.

In recent years, technology for analyzing the state of forests based on images of forests taken aerial by artificial satellites or drones has been attracting attention. If the state of the forest can be analyzed, it is possible to formulate a forest cultivation plan, predict the amount of timber production, and formulate each work plan for felling and lumbering.

However, in the past, there has been no proposal for a technique for determining the degree of growth of trees and the cutting time suitable for cutting with higher accuracy among the forest conditions. If the degree of tree growth and logging time cannot be specified, it will be difficult to formulate a forest growth plan and predict the amount of timber produced.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is to provide a logging time determination program capable of determining the logging time of trees in a forest with higher accuracy. To do.

In order to solve the above-mentioned problems, in the logging time determination program that determines the logging time of trees in the forest, the information acquisition step of acquiring the aerial image information of the forest that newly determines the logging time of the trees from the air, and the information acquisition step. Reference aerial image information obtained by capturing a forest from the air in the past and reference aerial image information obtained in the above information acquisition step by referring to the degree of association between the forest and the logging time of trees in three or more stages. Based on the image information, priority is given to the one having a higher degree of association, and the computer is made to execute a determination step for determining the cutting time of the tree.

Even if you do not have any special skills or experience, it is possible to determine the cutting time of trees in the forest with high accuracy.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the logging time determination program for determining the logging time of a tree to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a logging time determination system 1 to which a logging time determination program to which the present invention is applied is implemented. The logging time determination system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person using this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of taking an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the discrimination device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be configured by a communication interface for acquiring data about the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and the body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the body sensor may acquire biological data of not only humans but also animals. Further, the information acquisition unit 9 may be configured as a device for acquiring information such as drawings by scanning or reading from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects odors and scents.

Database 3 stores various information necessary for determining the logging time. Database 3 refers to reference aerial image information of forests captured from the air in the past, reference ground image information of forests captured from the ground in the past, and classification of land with forests of reference aerial image information captured in the past. Land classification information, reference terrain information about the terrain of the land where the forest that captured the reference aerial image information in the past, and the distance from the measurement point of the forest that captured the reference aerial image information in the past are measured by the distance measuring sensor. Reference distance information, reference shape information consisting of the thickness and height of trees by measuring the distance from the measurement point for trees in the forest that captured the reference aerial image information in the past, in the past The data set of the reference climate information regarding the climate of the area where the forest where the aerial image information for reference is captured, the reference type information consisting of the felling time of the discriminant target, and the felling time determined in the past is stored.

That is, in the database 3, such reference aerial image information, reference ground image information, reference land classification information, reference topography information, reference distance information, reference shape information, reference climate information, and reference Any one or more of the type information and the cutting time determined in the past are stored in association with each other.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be the one to be converted. The user can obtain a search solution by the search device 2.

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via an operation button, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 according to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technology.

The display unit 23 is configured by a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program will be read and executed by the control unit 24.

In the logging time determination system 1, for example, as shown in FIG. 3, it is premised that three or more levels of association between the reference aerial image information and the logging time are set and acquired in advance. The reference aerial image information is an image of a forest taken from the air or the sky. In order to acquire this reference aerial image information, it is possible to obtain an image of the forest from the sky by an aircraft, a helicopter, or an unmanned aerial vehicle such as a drone. Further, the reference aerial image information may be composed of satellite images captured by artificial satellites. The reference aerial image information may be composed of a so-called spectral image color-coded for each spectrum, in addition to the normal RGB image.

The logging time indicates a suitable time for logging. If trees are cut down while they are too young, they may not meet the desired quality and quantity of wood. On the other hand, if the trees are too old, the desired quality of wood will not be obtained, and if further extreme growth is not expected, the amount of wood that can be particularly obtained even if logging is early. It will not decrease. From this point of view, the optimal logging time is specified. Regarding this logging time, a forestry expert will actually observe each tree that makes up the forest, and then determine how many years to years (months to months) the logging time is appropriate. It may be obtained from the determined result. At this time, it is premised that the logging time is set based on the imaging time of the above-mentioned reference aerial image information. The logging time may be indicated from the earliest to the latest, but is not limited to this, and may be indicated only at the earliest.

In addition to obtaining a suitable logging time by an expert who observes the trees in the forest where the reference aerial image information was captured based on the reference aerial image information imaging time, the reference aerial image information is captured. After that, the day of actual logging may be obtained as the logging time.

In other words, through this reference aerial image information and the logging time data set, the characteristics of various images generated in the reference aerial image information and the relationship between the logging time can be understood. In other words, the data set is the characteristics of the image and the logging time of the reference aerial image information. Therefore, by collecting the reference aerial image information and the data set of the logging time, it is possible to know what kind of image features have been used in the past and how the logging time was determined.

When constructing this data set, an aerial image for reference is acquired by taking an image from the sky of a forest area where the logging time is known in advance, and the known logging time and data set are created.

In the example of FIG. 3, it is assumed that the input data is, for example, reference aerial image information P01 to P03. The reference aerial image information as such input data is linked to the output. In this output, the logging time as the output solution is displayed.

The reference aerial image information is associated with each other through three or more levels of association with the logging time as this output solution. The reference aerial image information is arranged on the left side through this degree of association, and the logging time is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to which logging time is higher with respect to the reference aerial image information arranged on the left side. In other words, this degree of association is an indicator of what logging time each reference aerial image information is likely to be associated with, and is used to select the most probable logging time from the reference aerial image information. It shows the accuracy in. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relation between each combination as an intermediate node and the logging time as an output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node and the logging time as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference aerial image information and the logging time in that case was adopted in determining the actual search solution, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 3 is created.

For example, it is assumed that a certain reference aerial image information is determined to be logging time A (for example, 3 to 5 years later). In such a situation, it is assumed that the reference aerial image information of a similar pattern is similarly determined to be the logging time A. In such a case, the degree of association with the logging time A becomes stronger. On the other hand, in the exact same pattern (classification) of aerial image information for reference, many were determined to be logging time B (for example, after 10 months), and few were determined to be logging time A. And. In such a case, the degree of association with the logging time B becomes stronger, and the degree of association with the logging time A becomes lower.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference aerial image information P01, analysis is performed from the data of the determination result of the past logging time. In the case of reference aerial image information P01, if there are many cases of logging time A, the degree of association leading to this logging time A is set higher, and if there are many cases of logging time B, this logging time B Set a higher degree of association that leads to. For example, in the example of the aerial image information P01 for reference, the logging time A and the logging time B are linked. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 4, reference aerial image information is input as input data, each felling time is output as output data, and at least one hidden layer is provided between the input node and the output node. It may be machine-learned. On the contrary, the logging time may be input and the reference aerial image information may be output.

Such degree of association is what is called learned data in artificial intelligence. After creating such learned data, the logging time will be predicted using the above-mentioned learned data when actually determining the logging time. In such a case, the aerial image information of the forest for which the logging time is to be newly determined is acquired. The method for acquiring the aerial image information is the same as the above-mentioned reference aerial image information.

The newly acquired aerial image information is input by the above-mentioned information acquisition unit 9. The information acquisition unit 9 may acquire such aerial image information as electronic data.

Based on the newly acquired aerial image information in this way, the logging time that is likely to be determined is actually searched for the aerial image information. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired aerial image information is the same as or similar to P02, the logging time B is associated with w15 and the logging time C is associated with the association degree w16 via the degree of association. In such a case, the logging time B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the logging time C in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, it is possible to search for the logging time to be determined from the newly acquired aerial image information and display it to the user. By seeing this search result, the user can formulate a logging plan and a growing plan for trees based on the searched logging time.

In the example of FIG. 5, it is assumed that the input data is, for example, reference aerial image information P01 to P03 and reference ground image information P14 to 17. The intermediate node shown in FIG. 5 is a combination of the reference aerial image information and the reference ground image information as such input data. Each intermediate node is further linked to the output. In this output, each logging time as an output solution is displayed.

In the example of FIG. 5, it is premised that a combination of the reference aerial image information and the reference ground image information is formed. The reference ground image information is an image of an actual forest imaged from the ground. The reference ground image information may be composed of a so-called spectral image color-coded for each spectrum, in addition to the normal RGB image. Further, the reference ground image information may be an image of a branch or leaf portion of a tree, but may be an image of a trunk portion or a root portion of a tree.

In the example of FIG. 5, it is assumed that the input data is, for example, reference aerial image information P01 to P03 and reference ground image information P14 to 17. The intermediate node shown in FIG. 5 is a combination of the reference aerial image information and the reference ground image information as such input data. Each intermediate node is further linked to the output. In this output, the logging time as the output solution is displayed.

Each combination of reference aerial image information and reference ground image information (intermediate node) is associated with each other through three or more levels of association with the logging time as this output solution. The reference aerial image information and the reference ground image information are arranged on the left side through this degree of association, and the logging time is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to each logging time with respect to the reference aerial image information and the reference ground image information arranged on the left side. In other words, this degree of association is an index indicating at what logging time each reference aerial image information and reference ground image information are likely to be linked, and the reference aerial image information and reference ground image information. It shows the accuracy in selecting each logging time that is most probable from the image information. In addition to the aerial image information, it is possible to improve the accuracy by discriminating according to the image actually captured from the ground. Therefore, the optimum logging time will be searched for by combining these reference aerial image information and reference ground image information.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference aerial image information, the reference ground image information, and the logging time in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference aerial image information P01 and the reference ground image information P16, the logging time is analyzed from the past data. If there are many cases of logging time A, the degree of association that leads to this logging time A is set higher, and if there are many cases of logging time B and there are few cases of logging time A, the linkage that leads to logging time B is set. Increase the degree and set the degree of association that leads to logging time A low. For example, in the example of the intermediate node 61a, the output of logging time A and logging time B is linked, but from the previous case, the degree of association of w13 connected to logging time A is set to 7 points, and the association of w14 connected to logging time B is set to 7 points. The degree is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 5, the node 61b is a node in which the reference ground image information P14 is combined with the reference aerial image information P01, and the logging time C has a connection degree of w15 and the logging time E. The degree of association is w16. The node 61c is a node of a combination of the reference ground image information P15 and P17 with respect to the reference aerial image information P02, and the degree of association of the logging time B is w17 and the degree of association of the logging time D is w18. ..

Such degree of association is what is called learned data in artificial intelligence. After creating such learned data, the above-mentioned learned data will be used when actually performing a search for determining the logging time. In such a case, in addition to the aerial image information, the ground image information is acquired from the company that newly determines the logging time. This ground image information corresponds to the above-mentioned reference ground image information, and the acquisition method thereof is also the same.

Search for the optimal logging time based on the newly acquired aerial image information and ground image information in this way. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, if the newly acquired aerial image information is the same as or similar to P02, and the ground image information is the same as or similar to P17, the node 61d will use the degree of association. The node 61d is associated with a logging time C of w19 and a logging time D of association w20. In such a case, the logging time C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the logging time D in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Table 2 below shows an example of the degree of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

FIG. 6 shows an example in which the combination of the above-mentioned reference aerial image information, the reference land classification information, and the logging time for the combination are set to three or more levels of association.

The reference land classification information is composed of, for example, land classification survey data posted and announced by the Ministry of Land, Infrastructure, Transport and Tourism, and is information on the surface geology, soil, land usage status, and disaster history. This reference land classification information is composed of information on the surface geology and soil.

In the example of FIG. 6, it is assumed that the input data is, for example, reference aerial image information P01 to P03 and reference land classification information P18 to 21. The intermediate node shown in FIG. 6 is a combination of the reference land classification information and the reference aerial image information as such input data. Each intermediate node is further linked to the output. In this output, the logging time as the output solution is displayed.

Each combination of reference aerial image information and reference land classification information (intermediate node) is associated with each other through three or more levels of association with the logging time as this output solution. The reference aerial image information and the reference land classification information are arranged on the left side through this degree of association, and the logging time is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the logging time with respect to the reference aerial image information and the reference land classification information arranged on the left side. In other words, this degree of association is an indicator of what logging time each reference aerial image information and reference land classification information is likely to be associated with, and is a reference aerial image information and reference land classification. It shows the accuracy in selecting each logging time that is most probable from the information. In addition to the aerial image information, the logging time can be narrowed down according to the relationship with the actual soil. Therefore, the optimum logging time will be searched for by combining these reference aerial image information and reference land classification information.

In the example of FIG. 6, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which is more suitable for the reference aerial image information, the reference land classification information, and the logging time in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference aerial image information P01 and the reference land classification information P20, the logging time is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of the logging time A and the logging time B is linked. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 6, the node 61b is a node of the combination of the reference land classification information P18 with respect to the reference aerial image information P01, and the degree of association of the logging time C is w15 and the logging time E. The degree of association is w16. The node 61c is a node of a combination of the reference land classification information P19 and P21 with respect to the reference aerial image information P02, and the degree of association of the logging time B is w17 and the degree of association of the logging time D is w18. ..

Such degree of association is what is called learned data in artificial intelligence. After creating such learned data, the above-mentioned learned data will be used when actually determining the logging time of the forest. In such a case, in addition to the above-mentioned aerial image information, the land classification information of the forest for determining the logging time is newly acquired. The land classification information corresponds to the reference land classification information, and may be acquired from data stored by, for example, the Ministry of Land, Infrastructure, Transport and Tourism or other organizations.

Search for the logging time based on the newly acquired aerial image information and land classification information in this way. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, if the newly acquired aerial image information is the same as or similar to P02 and the land classification information is P21, the node 61d is associated via the degree of association. The node 61d is associated with a logging time C of w19 and a logging time D of association w20. In such a case, the logging time C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the logging time D in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In the embodiment shown in FIG. 6 described above, the reference land classification information is used as the reference information, but the present invention is not limited to this, and the reference topography information is used as a substitute for the reference land classification information. May be trained together with the reference aerial image information. This reference terrain information can also be obtained from data held by the Ministry of Land, Infrastructure, Transport and Tourism, and is all information regarding the terrain of the land where the forest is imaged from the reference aerial image information. Examples of this reference terrain information are elevation data, contour data, slopes of trees growing on mountain slopes, positional relationships with rivers if there are rivers, and anything else related to terrain. Contains data.

In such a case, in FIG. 6, a combination having reference topographical information and reference aerial image information as a substitute for reference land classification information and a logging time in the forest form a degree of association of three or more levels. Keep it. Then, as input data, in addition to the aerial image information, new topographical information is acquired. This terrain information is information on the terrain of the forest that captures the aerial image information, and the content of the information corresponds to the terrain information for reference. Based on the same or similar reference terrain information as the acquired terrain information, priority is given to the one with a higher degree of association, and the logging time is determined.

The embodiment shown in FIG. 6 described above uses the reference land classification information as reference information, but is not limited to this, and is used as a substitute for the reference land classification information in the past. The reference climate information regarding the climate of the area where the forest where the aerial image information is captured may be learned together with the reference aerial image information. This reference climate information can be obtained from data held by, for example, the Japan Meteorological Agency or a private weather forecaster, and is all information about the climate in the area where the forest is imaged with the reference aerial image information. Examples of this reference climate information are information on past temperature and humidity, weather, precipitation, wind direction, wind speed, etc. in the area. The time of logging in the forest may correlate with the climate in the area, so this is added to the training data.

In such a case, in FIG. 6, a combination having reference climate information and reference aerial image information as an alternative to the reference land classification information and the logging time in the forest form a degree of association of three or more levels. Keep it. Then, as input data, in addition to the aerial image information, new geoclimate information is acquired. This climate information is information on the climate in the forest area where the aerial image information is captured, and the content of the information corresponds to the reference climate information. Based on the same or similar reference climate information as the acquired climate information, priority is given to those with a higher degree of association, and the logging time is determined.

Further, the embodiment shown in FIG. 6 described above uses the reference land classification information as reference information, but is not limited to this, and constitutes a forest as a substitute for the reference land classification information. The reference type information regarding the tree type may be learned together with the reference aerial image information. This reference type information can be obtained by inputting data if the type of forest tree for which the reference aerial image information is captured is known in advance. Since the forest was actually planted and the type was naturally known at that time, the type of tree may be known. In such a case, it can be obtained by reading out the information about the type of the tree in the forest from the database stored in association with each position information.

If the type of forest tree for which reference aerial image information is captured is unknown, the reference aerial image information is captured, and an expert or the like is asked to determine the type of forest tree each time, and data is input. By doing so, the reference type information may be obtained. As an example of the reference type information, it is composed of tree types such as sugi, oak, pine, and cypress.

In such a case, in FIG. 6, a combination having reference type information and reference aerial image information as an alternative to the reference land classification information and the logging time in the forest form a degree of association of three or more levels. Keep it. Then, as input data, in addition to the aerial image information, new type information is acquired. This type information is information on the type of trees in the forest that captures aerial image information, and the content and acquisition method of the information correspond to the reference type information. Based on the same or similar reference type information as the acquired type information, priority is given to the information having a higher degree of association, and the logging time is determined.

FIG. 7 shows an example in which the combination of the above-mentioned reference aerial image information, the reference distance information, and the logging time for the combination are set to three or more levels of association.

The reference distance information is information obtained by measuring the distance from the measurement point of the forest where the reference aerial image information was captured by the distance measurement sensor in the past. The range finder may be, for example, a known range finder using infrared rays, or a known laser range finder may be used. Further, such as LiDAR (Light Detection And Ringing), an optical radar or a laser radar may be used. That is, distance sensing and two-dimensional or three-dimensional spatial imaging may be acquired via a laser image by using laser light instead of electromagnetic waves.

When measuring the distance with such a distance measuring sensor, sensing is performed from a certain measurement point on the ground. The target of sensing may focus on one tree or may capture a plurality of trees. When focusing on one tree, it may be decided in advance whether the target is the branches and leaves of the tree or the trunk of the tree.

In the example of FIG. 7, it is assumed that the input data is, for example, reference aerial image information P01 to P03 and reference distance information P18 to 21. The intermediate node shown in FIG. 7 is a combination of reference distance information and reference aerial image information as such input data. Each intermediate node is further linked to the output. In this output, the logging time as the output solution is displayed.

Each combination of reference aerial image information and reference distance information (intermediate node) is associated with each other through three or more levels of association with the logging time as this output solution. The reference aerial image information and the reference distance information are arranged on the left side through this degree of association, and the logging time is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the logging time with respect to the reference aerial image information and the reference distance information arranged on the left side. In other words, this degree of association is an index indicating at what logging time each reference aerial image information and reference distance information are likely to be associated, and from the reference aerial image information and reference distance information. It shows the accuracy in selecting each logging time that is most likely. Two-dimensional and three-dimensional shapes of trees can be detected through distance information, the age of trees can be estimated through the shapes, and the cutting time can be narrowed down through these information. Therefore, the optimum logging time will be searched for by combining these reference aerial image information and reference distance information.

In the example of FIG. 7, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the search device 2 accumulates past data as to which is more suitable for the reference aerial image information, the reference distance information, and the logging time in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference aerial image information P01 and the reference distance information P20, the logging time is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of the logging time A and the logging time B is linked. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 7, the node 61b is a node in which the reference distance information P18 is combined with the reference aerial image information P01, the logging time C is linked to w15, and the logging time E is linked. The degree is w16. The node 61c is a node in which the reference distance information P19 and P21 are combined with respect to the reference aerial image information P02, and the degree of association of the logging time B is w17 and the degree of association of the logging time D is w18.

Such degree of association is what is called learned data in artificial intelligence. After creating such learned data, the above-mentioned learned data will be used when actually determining the logging time of the forest. In such a case, in addition to the above-mentioned aerial image information, the distance information of the forest for determining the logging time is newly acquired. The distance information corresponds to the reference distance information, and may be acquired from data stored in, for example, the Ministry of Land, Infrastructure, Transport and Tourism or other organizations.

Search for the logging time based on the newly acquired aerial image information and distance information in this way. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, if the newly acquired aerial image information is the same as or similar to P02 and the land classification information is P21, the node 61d is associated via the degree of association. The node 61d is associated with a logging time C of w19 and a logging time D of association w20. In such a case, the logging time C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the logging time D in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In the form shown in FIG. 7, the reference distance information is not used as it is, but the reference shape information consisting of the shape of the tree acquired based on the data measured by the distance measuring sensor is used. You may. The shape of the tree referred to here includes information on all shapes such as the thickness of the trunk of the tree, the height of the trunk, the degree of branching, the shape of the epidermis of the tree, the height of the tree, and the position where the leaves grow.

In such a case, the relationship between the distance data measured by the distance measuring sensor and the shape data of the tree is investigated in advance, and a table of the shape data of the tree with respect to the distance data measured by the distance measuring sensor is created. It is stored in the database 3. Then, when the distance data is newly measured by the distance measuring sensor, the table in which the shape data of the corresponding tree is stored in the database 3 is referred to, and the shape data of the tree corresponding to this is read out.

At this time, as shown in FIG. 8, a machine learning model trained by using the reference distance information and the shape of the tree as a data set in advance may be used. For the shape of the tree determined by humans or the shape of the tree determined from the image, a data set is created with the reference distance information based on the distance data by the distance measuring sensor, and this is learned. Then, when the distance information is newly acquired as the input information, the shape of the tree corresponding to this is solved and searched. The specific method of this solution search is omitted below by quoting the explanations of FIGS. 3 and 4.

FIG. 9 shows three or more stages of the reference shape information obtained by converting the shape of the tree acquired through the reference distance information into data or directly acquired through the distance measuring sensor, and the logging time for the combination of the reference aerial image. An example in which the degree of association is set is shown.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which is more suitable for the reference aerial image information, the reference shape information, and the logging time in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 9 is created. This analysis may be performed by artificial intelligence.

Such degree of association is what is called learned data in artificial intelligence. After creating such learned data, the above-mentioned learned data will be used when actually determining the logging time of the forest. In such a case, shape information is acquired in addition to the above-mentioned aerial image information. The shape information corresponds to the reference shape information, and the acquisition method thereof is the same as that of the reference shape information, and the above-mentioned table stored in the database 3 and the machine learning model shown in FIG. 8 are used.

Search for the logging time based on the newly acquired aerial image information and shape information in this way. In such a case, the solution is searched in the same manner as described above with reference to the degree of association shown in FIG. 9 (Table 1) acquired in advance.

In the above-described embodiment, the explanation is given by taking as an example the case where the relationship between the reference aerial image information and the logging time is learned in advance, but the present invention is not limited to this. As an alternative to the reference aerial image information, the relationship between the reference ground image information and the logging time may be learned and related via the above-mentioned degree of association, or the relationship between the reference distance information and the logging time may be determined. It may be trained and associated via the above-mentioned degree of association, or further, the relationship between the reference shape information and the logging time may be learned and associated via the above-mentioned degree of association. In such a case, when the input of the information (any of the ground image information, the distance information, and the shape information) corresponding to the learned reference information is accepted, the logging time as a search solution corresponding to the input is obtained. .. The specific method of obtaining the above will be omitted below by quoting the above-mentioned explanations of FIGS. 3 and 4.

In addition, when the reference information as the keynote is any of the reference ground image information, the reference distance information, and the reference shape information, this and other reference information (reference ground image information, reference land). Classification information, reference terrain information, reference distance information, reference shape information, reference climate information) may be combined to form three or more levels of association with the felling time. Then, as input data, information (ground image information, distance information, shape information) according to the reference information (any of the reference ground image information, the reference distance information, and the reference shape information) as the keynote, and the keynote When information corresponding to other reference information (ground image information, land classification information, topography information, distance information, shape information, climate information) combined with the reference information is input, the search solution is the same as above. Find the cutting time as. The specific method for obtaining the above is omitted below by quoting the above-mentioned explanations in FIGS. 5 to 7.

In addition to the basic information (aerial image information, ground image information, distance information, shape information), other information (ground image information, land classification information, topography information, distance information, shape information, climate information) When any two or more are acquired, the reference information (reference aerial image information, reference ground image information, reference) based on the two or more reference information corresponding to the acquired two or more information. By creating learning data consisting of three or more levels of association between the combination with the distance information and the shape information for reference) and the cutting time for the combination, it is possible to search for the solution of the credit rating in the same manner.

In the above-described embodiment, the case where the logging time is learned by associating it with the reference information to form the degree of association is described as an example, but the present invention is not limited to this. As an alternative to the logging time, the degree of growth of trees in the forest may be associated with reference information for learning. The degree of growth of this tree may be indicated by the age of the tree, or the growth stage may be defined in advance in a plurality of stages to indicate at what stage.

The degree of growth can be calculated from the time of planting if it is a forest that has been planted. If the forest is not planted, the age of the tree may be discriminated by an expert and the discriminant result may be input as the degree of growth.

As an alternative to this logging time, the detailed explanation of the case where the degree of growth of trees in the forest is used will be described by replacing the above-mentioned cutting time of FIGS. 3 to 9 with the degree of growth of trees. Omit.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-mentioned configuration, anyone can easily search for the creditworthiness of a company considering financing without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than that made by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Note that the above-mentioned input data and output data may not be completely the same in the process of learning, so that the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A dataset may be created between the data and the output data and trained.

Further, according to the present invention, there is a feature that the optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by a numerical value from 0 to 100%, for example, in addition to the above-mentioned 10 levels, but is not limited to this, and any stage can be described as long as it can be described by a numerical value of 3 or more levels. It may be configured.

In a situation where there are multiple possible candidates for a search solution by determining the creditworthiness with higher creditworthiness and lower misunderstanding based on the degree of association expressed by such numerical values of three or more levels. It is also possible to search and display in descending order of the degree of association.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the extremely low output such as the degree of association of 1%. Remind the user that even a judgment result with an extremely low degree of association is connected as a slight sign, and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is a merit that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once in tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one should be emphasized based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided, for example, via a public communication network such as the Internet. In addition to market information, when event information, external environment information, household information, real estate information, expert opinion information, and natural environment information knowledge, information, and data are acquired, the degree of association is increased or decreased accordingly. Let me.

In other words, this update is equivalent to learning in terms of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is done by the system side or the user side based on the contents of research data, treatises, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from public communication networks. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

In addition, the process of first creating a trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and training the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

The present invention is not limited to the above-described embodiment, and for example, as shown in FIG. 10, the reference information as the keynote and the degree of association between the logging time and the logging time are used in three or more stages. You may. In such a case, the solution search will be performed based on the degree of association with the logging time according to the newly acquired information in three or more stages. The reference information that is the basis is, for example, aerial image information for reference, ground image information for reference, land classification information for reference, terrain information for reference, distance information for reference, shape information for reference, climate information for reference, and reference information. Any reference information described above, such as type information, is applicable.

Similarly, in these cases, when the information corresponding to the reference information used as the learning data is input, the solution search is performed based on the above-mentioned method.

The search solution obtained through the degree of association may be further modified based on other reference information, or the weighting may be changed.

The other reference information referred to here corresponds to any reference information other than the reference information which is the keynote when any of the above-mentioned reference information is used as the keynote reference information.

For example, as one of the other reference information, it is assumed that there was a lot of circumstances in which B was previously determined as the logging time in a certain reference land classification information P14. When the land classification information corresponding to the reference land classification information P14 is newly acquired, the search solution B as the logging time is subjected to a process of increasing the weight, in other words, the search solution B as the logging time. It is set in advance to perform the process to connect to.

For example, other reference information G is an analysis result that suggests a search solution C as a logging time, and reference information F is an analysis result that suggests a search solution D as a logging time. Suppose there is. After setting with the reference information in this way, if the actually acquired information is the same as or similar to the reference information G, a process of increasing the weighting of the search solution C as the logging time is performed. On the other hand, when the actually acquired information is the same as or similar to the reference information F, a process of increasing the weighting of the search solution D as the logging time is performed. That is, the degree of association itself that leads to the logging time may be controlled based on the reference information F to H. Alternatively, after the logging time is determined only by the above-mentioned degree of association, the obtained search solution may be modified based on the reference information F to H. In the latter case, how to modify the logging time as a search solution based on the reference information F to H will reflect what was designed on the system side each time.

Further, the reference information is not limited to the case where it is composed of any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the more the case leads to the logging time suggested by the reference information, the higher the correction of the logging time as the search solution obtained through the degree of association may be made.

Similarly, as shown in FIG. 11, even in the case of forming a degree of association with the logging time for a combination having the reference information as the keynote and other reference information, the reference information as the keynote is still available. Any of the above-mentioned reference information (reference aerial image information, reference ground image information, reference land classification information, reference terrain information, reference distance information, reference shape information, reference climate information, reference type information, etc. ) Is also applicable. Other reference information includes any reference information other than the underlying reference information.

At this time, if the reference information that is the keynote is the reference distance information, any other reference information is included as the other reference information.

In such a case, the logging time can be estimated by searching for a solution in the same way. At this time, as shown in FIG. 10 described above, the logging time is corrected through further other reference information (reference information F, G, H, etc.) for the search solution obtained through the degree of association. You may do it.

As shown in FIGS. 12 and 13, the solution search may be performed by using the reference information as the keynote and the degree of association between the logging time and the logging time in three or more stages.

Determine the logging time only from the reference information. For example, as shown in FIGS. 12 and 13, reference information (reference aerial image information, reference ground image information, reference land classification information, reference terrain information, reference distance information, reference shape information) acquired in the past. , Reference climate information, reference type information, etc.) and the degree of association between the felling time actually determined in the past and the degree of association are used.

Such degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining the new logging time from now on. In such a case, reference information (reference aerial image information, reference ground image information, reference land classification information, reference terrain information, reference distance information, reference shape information, reference climate information, reference type Information, etc.) will be newly acquired.

Based on the newly acquired information in this way, the logging time is determined. In such a case, the degree of association shown in FIGS. 12 and 13 acquired in advance is referred to. Since the specific method for estimating the logging time is the same as described above, the description below will be omitted.

As an alternative to this logging time, the detailed explanation of the case where the degree of growth of trees in the forest is used will be described by replacing the above-mentioned cutting time of FIGS. 10 to 13 with the degree of growth of trees. Omit.

As an alternative to this logging time, the quality of trees in the forest may be determined. The quality of a tree is the thickness of the trunk, the amount of branching, the amount of water contained in the tree, and the height of the trunk when considering the quality of the wood. Whether it is lively or not may be evaluated by the color of the leaves, the direction and length of the branches. In addition, the actual mechanical properties (strength, toughness, elastic modulus, water content, splitting property, humidity control property) after processing the actual tree as wood, and other than that, the texture, fragrance, and processing of wood. It may be composed of ease of use, and appearance quality (for example, the amount and size of worm-eaten) in the state of wood or a tree as it is.

Alternatively, as an alternative to the logging time, the amount of trees in the forest may be determined. The amount of trees may be composed of the number of trees per unit area in the forest, the total cross-sectional area of the trunk of the tree, or a quantitative index with the number of trees and the thickness of the trunk of the tree as parameters.

Even when determining the quality and quantity of these trees, a degree of association is formed between each of the above-mentioned reference information and the quality and quantity of the tree as a search solution. Then, at the time of solution search, the quality and quantity of trees are obtained while referring to the degree of association based on the similarly input information.

As an alternative to this logging time, the type of tree in the forest may be determined. The type of tree is composed of the actual type name of the tree such as Sugi, Quercus acutissima, Japanese cypress, and pine.

That is, through the above-mentioned reference information and the tree type data set, the relationship between the various characteristics generated in the reference information and the tree type can be understood. In other words, the characteristics of the reference information and the type of tree are the data set. Therefore, by collecting the reference information and the data set of the tree type, it is possible to know what kind of features the tree type has been determined in the past.

When constructing this data set, the reference information is acquired in the same manner as above for the forest area where the tree type is known in advance, and the known tree type and data set are created.

Even when determining the type of forest tree like this, the degree of association is formed between each of the above-mentioned reference information and the type of forest tree as a search solution. Then, at the time of solution search, the type of tree is obtained while referring to the degree of association based on the similarly input information.

The present invention may be applied as a system as shown in FIGS. 14 and 15, as well as a robot or a mobile body using the system.

FIG. 14 is a block diagram showing an overall configuration of a logging time determination system 101 to which a logging time determination program to which the present invention is applied is implemented. The logging time determination system 101 includes a moving body 5 including an information acquisition unit 9, a discrimination unit 2 connected to the information acquisition unit 9, and a movement control unit 20 connected to the discrimination unit 9, and a discrimination unit for the moving body 5. It has a database 3 connected to 2.

The mobile body 5 is configured to be movable, such as a vehicle, a robot, an unmanned aerial vehicle such as a drone, or the like.

The information acquisition unit 9 has the same configuration as described above, and is composed of a camera that captures an image, a sensor for acquiring the current position, a GPS system, and the like. Further, the information acquisition unit 9 may be integrated with the determination unit 2 described later. The information acquisition unit 9 outputs the detected information to the determination unit 2. The information acquisition unit 9 acquires the position information of the moving body 5 itself. Since the information acquisition unit 9 is mounted on the mobile body 5, it is desirable that the information acquisition unit 9 is composed of a camera or the like suitable for the information acquisition unit 9.

The movement control unit 29 controls the movement of the moving body 5. If the moving body 5 is a vehicle, the rotation of the wheels and the traveling direction of the vehicle are controlled. If the moving body 5 is a robot, if the robot moves by the rotation of the wheels, the rotation of the wheels and the traveling direction of the vehicle are controlled. If the moving body 5 is an unmanned aerial vehicle, its flight direction and flight speed are controlled.

In particular, this mobile body 5 is suitable for acquiring ground image information or reference ground image information newly captured from the ground through the information acquisition unit 9.

In addition to these, the moving body 5 may further include a cutting portion (not shown) for cutting trees, a grip portion 51 for gripping wood, a storage portion 52 for stacking wood, and the like. The wood in the storage location is gripped by the gripping portion 51, and the wood is accommodated in the accommodating portion 52, or the wood is conveyed while being gripped without being accommodated in the accommodating portion 52. Any well-known gripping means can be applied to the gripping portion 51.

In the discrimination unit 2, as shown in FIG. 15, a movement control unit 29 is connected to the internal bus 21. The control unit 24 instructs the movement control unit 29 to control the movement.

Such a mobile body 5 can acquire various information through the information acquisition unit by the same method as described above. Then, based on the acquired information, the type of tree, the cutting time, the degree of growth of the tree, and the quality and quantity of the tree can be determined in the same manner as described above. At this time, it is of course possible to cut, cut, grip, accommodate, and transport the tree based on the discriminated growth degree, quality, and quantity of the tree.

Further, the present invention is not limited to the above-described embodiment. As an alternative to logging time, the boundaries of the forest may be determined. Especially when there is an owner in this forest, there are cases where it becomes a problem from where to where the forest becomes. In such a case, the efficiency of discrimination can be improved by learning the reference aerial image information and the reference aerial image information defined in advance as the boundary of the forest.

The boundary of the forest may be determined by having an expert who visually recognizes the aerial image information for reference determine where the boundary is.

Even when determining such a forest boundary, a degree of association is formed between each of the above-mentioned reference information and the forest boundary as a search solution. Then, when searching for a solution, the boundary of the forest is obtained while referring to the degree of association based on the similarly input information.

Further, the present invention is not limited to the above-described embodiment. As an alternative to logging time, the topography of the forest may be determined. The topography of a forest is information necessary for creating an area or topographic map on a forest map, and information necessary for creating contour lines indicating several meters above sea level. The terrain of this forest may be such a topographic map or contour lines themselves, or may be composed of the position information and height of the above-mentioned forest boundary necessary for this.

Even when determining the topography of such a forest, a degree of association is formed between each of the above-mentioned reference information and the topography of the forest as a search solution. Then, at the time of solution search, the topography of the forest is obtained while referring to the degree of association based on the similarly input information.

Further, the present invention is not limited to the above-described embodiment. As an alternative to the logging time, the condition of the seedlings in the forest may be determined. The sapling status is information on the position of the sapling and the growth status of the sapling. First, humans determine the position and growth status of the seedlings, and obtain the degree of association between this and the above-mentioned reference information.

Even when determining the status of such seedlings, the degree of association is formed between the above-mentioned reference information and the status of the seedlings as a search solution. Then, at the time of solution search, the condition of the seedling is obtained by referring to the degree of association based on the similarly input information.

In the form of searching for the situation of the seedlings, the seedlings may be searched for by measuring the distance information and the reference distance information using a distance measuring sensor or the like.

Further, the present invention is not limited to the above-described embodiment. As an alternative to logging time, the status of destruction in the forest may be determined. The deforestation status may indicate the degree to which the forest is burned down due to the burning of trees or logging. In general, deforestation indicates a situation in which the forest is reduced or disappears due to the cutting of trees that exceeds the resilience of nature, so this deforestation situation is indexed in relation to the resilience of nature. You may. The state of destruction in this forest may be determined by an expert. At this time, it may be displayed by ranking in a plurality of stages according to the situation of destruction.

Even when determining the state of destruction in such a forest, a degree of association is formed between each of the above-mentioned reference information and the state of destruction in the forest as a search solution. Then, at the time of solution search, the state of destruction in the forest is obtained by referring to the degree of association based on the similarly input information.

Further, the present invention is not limited to the above-described embodiment. As an alternative to logging time, the risk of fire in the forest may be determined. The risk of fire is the degree of risk that a forest will catch fire. The risk of this fire can be determined by extracting the degree of dryness of fallen leaves, the state of fallen leaves on tree branches, the degree of density of trees, the degree of overlap of branches and leaves between adjacent trees, and the like. The risk of this fire is the degree of risk that the forest will catch fire. The risk of this fire was determined by an expert from images such as the dryness of fallen leaves, the condition of fallen leaves on tree branches, the degree of density of trees, and the degree of overlap of branches and leaves between adjacent trees. You may. At this time, it may be displayed by ranking in a plurality of stages according to the situation of destruction.

From the ground image information for reference and the ground image information, the dryness of the fallen leaves, the condition of the fallen leaves on the tree branches, the degree of density of the trees, the overlap of the branches between adjacent trees, etc. are extracted by image analysis. The data set may be trained sequentially by associating it with the risk of fire judged by humans.

Even when determining the risk of fire in such a forest, a degree of association is formed between each of the above-mentioned reference information and the risk of fire in the forest as a search solution. Then, at the time of solution search, the state of destruction in the forest is obtained by referring to the degree of association based on the similarly input information.

Further, the present invention is not limited to the above-described embodiment. As an alternative to logging time, the soil in the forest may be determined. Forest soil contains all the information about the soil. Examples of information about soil include soil type, soil composition, pH, water content, temperature and the like. The results of actual sampling of soil components and analysis based on a chemical analysis method may be used, or data detected by a well-known soil sensor may be used. In addition, the result obtained by having an expert actually see the image or the actual thing and make a judgment may be used.

Even when determining the soil in such a forest, the degree of association is formed between each of the above-mentioned reference information and the soil in the forest as a search solution. Then, at the time of solution search, the soil in the forest is obtained by referring to the degree of association based on the similarly input information.

Other than this, the soil of the forest may be identified from the above-mentioned various exploratory solutions (type, quality and quantity of forest, growth status, etc.). In such a case, a template in which the forest soil is associated with each search solution is acquired in advance, and when the forest soil is obtained, the template is referred to for the obtained search solution. You may try to identify the soil of the associated forest. For example, in the case of the type of forest, the type of soil is associated with each type. In the case of forest quality, the type of soil is associated with each quality. The soil associated with each type and quality of forest obtained as a search solution will be displayed as a solution.

Further, the present invention is not limited to the above-described embodiment. As an alternative to logging time, the status of greenhouse gases in the forest may be determined.

Forests generally absorb carbon dioxide, convert it into oxygen, and release it into the atmosphere. If deforestation loses the carbon dioxide fixation function of the forest itself, it will further adversely affect climate change as a result of increasing greenhouse gas emissions. The status of greenhouse gases is the concentration of gases such as carbon dioxide, methane, nitrous oxide, and chlorofluorocarbons, which have a large effect on global warming. The concentration of such greenhouse gases can be measured by each government agency or private company, but the concentration of such gases may be learned as the situation of greenhouse gases.

Even when determining the greenhouse gas status in such a forest, a degree of association is formed between each of the above-mentioned reference information and the greenhouse gas status in the forest as a search solution. Then, when searching for a solution, the status of greenhouse gases in the forest is obtained by referring to the degree of association based on the similarly input information.

In order to obtain the greenhouse gas status, the same method as described above may be used to search for the deforestation status and identify the greenhouse gas based on the deforestation status. ..

In such a case, obtain a template in advance in which the greenhouse gas status is associated with each search solution for the deforestation status, and when obtaining the greenhouse gas status, use that template. References may be made to identify the greenhouse gas status associated with the search solution for the deforestation status sought.

As an alternative to this greenhouse gas, for example, the determination of destruction of nature may be made. Forests also have the effect of suppressing flooding during heavy rains, and also reduce the amount of water in the air and on land, such as by letting water flow out into rivers even if the drought continues for a while. It has a so-called water source recharge function to adjust. When forests are destroyed, the water circulation cycle is disrupted, causing various problems such as floods and associated landslides. Furthermore, when the water and nutrients of the soil are lost due to deforestation, soil erosion occurs, and the soil is devastated and deteriorated, causing desertification.

Therefore, based on the above-mentioned search solutions (type, destruction, wildfire risk, seedlings, soil, etc.), it is possible to determine the destruction of nature such as the water source recharge function and soil erosion.

Various species of organisms inhabit the forest to maintain the balance of the ecosystem, but deforestation may disrupt the balance of the ecosystem. Therefore, the balance of these ecosystems may be determined based on the above-mentioned search solutions (type, destruction, wildfire risk, seedlings, soil, etc.).

1 Tree discrimination system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimating unit 28 Storage unit 61 Node

Claims

In the logging time determination program that determines the logging time of trees in the forest
An information acquisition step to acquire aerial image information of a forest that newly determines the felling time of trees from the air, and
Reference aerial image information obtained by capturing a forest from the air in the past and reference aerial image information obtained in the above information acquisition step by referring to the degree of association between three or more stages of the relationship between the tree felling time in the forest and the reference aerial image information. A logging time determination program characterized by having a computer execute a determination step for determining the felling time of a tree by giving priority to the one having the higher degree of association based on the image information.
In the above information acquisition step, the ground image information of the newly imaged forest from the ground is acquired.
In the discrimination step, the combination of the reference aerial image information and the reference ground image information obtained by imaging the forest from the ground in the past and the degree of association between the logging time of the tree in the forest are referred to at three or more stages. Further, claim 1 is characterized in that, based on the reference ground image information according to the ground image information acquired in the above information acquisition step, priority is given to the one having a higher degree of association, and the cutting time of the tree is determined. The listed logging time determination program.
In a tree discrimination program that discriminates the types of trees in the forest
An information acquisition step to acquire ground image information that is a new image of a forest that determines the type of tree from the ground,
The reference ground image information obtained by imaging the forest from the ground in the past and the reference ground according to the ground image information acquired in the above information acquisition step by referring to the degree of linkage between the forest and the logging time of trees in three or more stages. A logging time determination program characterized by having a computer execute a determination step for determining the felling time of a tree by giving priority to the one having the higher degree of association based on the image information.
The logging time determination program according to any one of claims 1 to 3, wherein in the determination step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used.
In a tree discrimination program that discriminates the types of trees in the forest
An information acquisition step for acquiring aerial image information of a forest that newly determines the type of tree from the air and ground image information of the above forest captured from the ground.
Refer to the combination of the reference aerial image information obtained by capturing the forest from the air in the past, the reference ground image information obtained by capturing the forest from the ground in the past, and the degree of association of three or more levels with the type of tree in the forest. Then, based on the reference aerial image information according to the aerial image information acquired in the above information acquisition step and the reference ground image information according to the ground image information, the tree having a higher degree of association is prioritized and the tree is given priority. A tree discrimination program characterized by having a computer execute a discrimination step for discriminating the type.
In a tree discrimination program that discriminates the types of trees in the forest
An information acquisition step to acquire aerial image information of a forest that newly determines the type of tree from the air and land classification information related to the classification of the land where the forest is located, and
A combination having a reference aerial image information obtained by imaging a forest from the air in the past and a reference land classification information relating to the classification of a certain land in which the forest captured the reference aerial image information in the past, and the type of tree in the forest. Refer to the three or more levels of linkage, and use the reference aerial image information according to the aerial image information acquired in the above information acquisition step and the reference land classification information according to the land classification information acquired in the above information acquisition step. Based on this, a tree discrimination program characterized by having a computer execute a discrimination step for discriminating a tree type by giving priority to those having a higher degree of association.
In a tree discrimination program that discriminates the types of trees in the forest
An information acquisition step to acquire ground image information that is a new image of a forest that determines the type of tree from the ground,
Reference ground image according to the ground image information acquired in the above information acquisition step by referring to the three or more levels of association between the reference ground image information obtained by capturing the forest from the ground in the past and the type of tree in the forest. A tree discrimination program characterized by having a computer execute a discrimination step for discriminating a tree type based on information, giving priority to the one having a higher degree of association.
In the above information acquisition step, land classification information regarding the classification of land with a forest that newly determines the type of tree is acquired.
In the above-mentioned discrimination step, the combination having the above-mentioned reference ground image information, the reference land classification information regarding the classification of the land having the forest for which the reference ground image information was imaged in the past, and the type of trees in the forest are used. The type of tree is determined by giving priority to the one with the higher degree of association based on the reference land classification information according to the land classification information acquired in the above information acquisition step, referring to the degree of association above the stage. 7. The tree discrimination program according to claim 7.