Tree diagnostic system and tree diagnostic method

Abstract

To provide an economical and practically useful tree diagnosis system that improves efficiency to enable diagnosis of many trees in a short time.SOLUTION: Provided is a tree diagnosis system characterized by comprising: a multispectral camera 100 that is mounted on a drone 10 and receives reflected waves of sunlight from trees to generate multispectral image information; a first determination part 200 and a tree identification part 300 that identify a specific tree from this multispectral image information; an appearance diagnosis implementation part 400 and a second determination part 500 that perform appearance diagnosis on specific trees and determine unhealthy tree candidates; and a stress wave velocity measurement part 600 and a third determination part 700 that perform stress wave velocity measurements and determine whether the tree is unhealthy.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a tree diagnosis system and a tree diagnosis method for improving a method for diagnosing whether a tree is a healthy tree or an unhealthy tree, and particularly to a tree diagnosis system and a tree diagnosis method that achieve efficiency by using a multi-spectral image.

Background Art

[0002] Conventionally, trees in roadside trees or urban parks may cause serious accidents such as falling or branch dropping as they grow larger in diameter and age. In addition, due to the adverse effects of disasters or pests, there is also a risk of serious accidents such as falling or branch dropping.

[0003] Therefore, it is recommended to appropriately and reliably continuously conduct inspections and diagnoses of trees. This inspection and diagnosis is usually performed by visual inspections by people with specialized knowledge and experience through patrols and visits. As a result of the visual inspection, for trees with abnormalities or irregularities found, a diagnosis of the health status of the tree, so-called tree diagnosis, is performed.

[0004] Patent Document 1 discloses a tree diagnosis system using a computer and a tree diagnosis chart used in the system. In this system, it is possible to comprehensively and accurately manage the diagnosis, treatment, tree vigor recovery, growth, etc. of all registered trees without losing time.

[0005] Patent Document 2 discloses a tree diagnosis method and apparatus for diagnosing the health state of a tree by irradiating a tree with radio waves, detecting the transmission loss of the radio waves by the tree, and calculating the dehydration rate of the moisture contained in the tree. In this method, the transmission loss is calculated from the intensity ratio of the radiated radio wave for irradiating the tree and the transmitted radio wave after the radiated radio wave passes through the tree, and the dehydration rate of the tree is calculated from the standard deviation value of the transmission loss to diagnose the tree.

[0006] Patent Document 3 discloses a tree diagnostic device and a tree diagnostic method that can non-destructively and quickly diagnose the internal condition of a tree, such as the presence or absence of decay and heartwood. In this device, the internal condition of the tree is determined by scanning the reception position of the emitted radio waves.

[0007] Patent documents 4 and 5 disclose methods and systems for automatic object detection from aerial images for rapidly and precisely detecting target objects from aerial images of a region of interest. These methods involve obtaining a numerical surface model of a certain region and a numerical surface model image of a target object, detecting target objects within the region based on the numerical surface model images of the region and target object, obtaining the positions of the detected target objects by recognizing them using artificial intelligence, and calculating the number of recognized target objects. [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] Patent No. 3741174 [Patent Document 2] Japanese Patent Publication No. 2005-292099 [Patent Document 3] Patent No. 7000174 [Patent Document 4] U.S. Patent Publication No. 10546195 [Patent Document 5] U.S. Patent Publication No. 10699119 [Overview of the project] [Problems that the invention aims to solve]

[0009] The methods disclosed in Patent Documents 1 to 3 are inspection and diagnostic methods that target individual trees, and require human intervention to inspect and diagnose each individual tree. In other words, human work is required for each individual tree.

[0010] The inventions disclosed in Patent Documents 4 and 5 merely enable the recognition of the number of target objects in a specific area.

[0011] Considering the risk of serious accidents caused by trees falling or branches dropping due to unhealthy trees, it is desirable that not only street trees or trees in urban parks be subject to inspection, but also trees planted on hills or mountains, as well as naturally growing trees, be subject to regular inspections.

[0012] However, in reality, tree diagnosis still relies on manual labor, examining each tree individually. Furthermore, because the diagnosis requires specialized skills, manpower is limited, making it difficult to conduct regular tree diagnoses. Moreover, the time, cost, and workload / burden required for diagnosis and management are considerable.

[0013] This invention has been made in consideration of the above circumstances, and aims to provide a tree diagnostic system and tree diagnostic method that are efficient and can diagnose many trees in a short amount of time. Furthermore, the present invention aims to provide a tree diagnostic system and method that are advantageous in terms of the time, cost, and management required for diagnosis, and are economical and practically useful. Furthermore, the present invention aims to provide a tree diagnosis system and a tree diagnosis method that enable tree diagnosis without requiring deep specialized knowledge or experience. [Means for solving the problem]

[0014] The present invention is characterized by having the following configuration in order to achieve the above objective.

[0015] (1) The tree diagnostic system of the present invention is mounted on a remotely operated drone and includes a multispectral image information generation means that acquires specific wavelengths of sunlight reflected from trees from above and generates multispectral image information; a first determination means that refers to a first set of diagnostic information with respect to the multispectral image information of the trees generated by the multispectral image information generation means and determines whether the multispectral image information is within a first set of thresholds; a tree identification means that identifies trees corresponding to the multispectral image information that the first determination means has determined to be outside the first threshold; and a visual diagnosis is performed on the trees identified by the tree identification means and visual diagnosis information is obtained. The system is characterized by comprising: an external diagnostic means for acquiring information; a second determination means for determining whether the external diagnostic information acquired by the external diagnostic means is within a predetermined second threshold by referring to a predetermined second set of diagnostic information; a stress wave velocity measurement means for performing stress wave velocity measurements on trees corresponding to the external diagnostic information determined by the second determination means to be outside the second threshold, and acquiring stress wave velocity measurement information of those trees; and a third determination means for determining whether the stress wave velocity measurement information acquired by the stress wave velocity measurement means is within a predetermined third threshold by referring to a predetermined third set of diagnostic information.

[0016] (2) The configuration of (1) above is characterized by the addition of a flight path memory management means for storing and managing the flight path of the drone on which the multispectral image information generation means is mounted.

[0017] (3) A tree diagnosis method for diagnosing the health of multiple trees using a remotely controlled drone capable of memorizing flight paths, The multi-spectral camera equipped with a thermal infrared sensor function on the drone acquires specific wavelengths of sunlight reflected from the trees from above the trees and generates them as multi-spectral image information. Referring to the preset first diagnostic information for this multi-spectral image information, it is determined whether the multi-spectral image information is within the preset first threshold. The tree corresponding to the multi-spectral image information determined to be outside the first threshold is identified, and an appearance diagnosis is performed on the identified tree to obtain appearance diagnosis information. Referring to the preset second diagnostic information for this appearance diagnosis information, it is determined whether the appearance diagnosis information is within the preset second threshold. For the tree corresponding to the appearance diagnosis information determined to be outside the second threshold in this determination, stress wave velocity measurement is performed to obtain stress wave velocity measurement information of the tree. Referring to the preset third diagnostic information for this stress wave velocity measurement information, it is determined whether the stress wave velocity measurement information is within the preset third threshold. It is characterized in that the tree corresponding to the stress wave velocity measurement information determined to be outside the third threshold in this determination is determined to be an unhealthy tree.

[0018] According to the above configuration, many trees can be made diagnostic targets in a short time, and the efficiency of tree diagnosis can be improved.

[0019] Also, according to the above configuration, great improvements can be achieved in terms of the people, time, cost, and management required for tree diagnosis, making it superior and economically and practically useful.

[0020] Furthermore, according to the above configuration, tree diagnosis can be performed without requiring deep specialized knowledge or a lot of experience, so it is possible to expand the diagnostic target range and reduce the risk of major accidents such as falling and branch shedding at an early stage, contributing to safety and security.

Effects of the Invention

[0021] According to the present invention, many trees can be made diagnostic targets in a short time, and an efficient tree diagnosis can be provided. In addition, according to the present invention, advantages can be ensured in terms of the time, cost, and management required for tree diagnosis, so that excellent economic and practical effects can be achieved. Furthermore, according to the present invention, accurate and appropriate tree diagnosis can be performed without being affected by the depth of expertise or practical experience.

Brief Description of the Drawings

[0022] [Figure 1] It is a diagram schematically showing the overall configuration of a tree diagnosis system according to an embodiment of the present invention. [Figure 2] Regarding the same embodiment, it is a diagram schematically showing the functional configuration of a multispectral camera. [Figure 3] Regarding the same embodiment, it is a diagram schematically showing the functional configuration of a PC内设 having a first determination unit, a tree identification unit, an appearance diagnosis execution unit, a second determination unit, a stress wave velocity measurement execution unit, a third determination unit, and others. [Figure 4] Regarding the same embodiment, it is a diagram schematically showing the flow of the entire tree diagnosis process. [Figure 5] Regarding the same embodiment, it is a diagram (flowchart) showing the flow of appearance diagnosis processing. [Figure 6] Regarding the same embodiment, it is a diagram (flowchart) showing the flow of precise diagnosis processing.

Modes for Carrying Out the Invention

[0023] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0024] It should be noted that in the translation of "内设" in ID=16, it is a bit unclear in the original text. The translation "内设" is used as it is in the original, but it might need to be further clarified in the context of the original Japanese text for a more accurate translation. If it is a specific term with a known English equivalent, it should be replaced accordingly.The tree diagnosis system according to this embodiment comprises a remotely operated drone 10, a multispectral camera (hereinafter referred to as "camera") 100 mounted on the drone 10 which has a thermal infrared sensor function and a function to receive reflected sunlight waves from trees and generate multispectral image information, a first judgment unit 200 which has the function of making a first judgment by referring to this multispectral image information, a tree identification unit 300 which has the function of identifying and specifying a specific tree by referring to the judgment information of the first judgment unit 200, an appearance diagnosis implementation unit 400 which has the function of performing an appearance diagnosis on the identified tree, a second judgment unit 500 which has the function of making a second judgment by referring to this appearance diagnosis information, a stress wave velocity measurement implementation unit 600 which has the function of performing stress wave velocity measurement by referring to the judgment information of the second judgment unit 500, and a third judgment unit 700 which has the function of determining whether or not a tree is unhealthy by referring to this stress wave velocity measurement information.

[0025] The drone 10 is capable of receiving control signals transmitted from the ground via radio waves and flying in airspace less than 150m above the ground. The drone 10 is also equipped with a transmitting unit (not shown) that transmits various signal information and is functionally configured to send actual flight path information to the flight path memory management unit 800.

[0026] Camera 100 is a multispectral camera that integrates a thermal infrared sensor (thermal infrared sensor unit 140) with five high-resolution multiband sensors (multiband sensor unit 120) for red, green, blue, red edge, and near infrared, and has the function of acquiring specific wavelengths and generating multispectral images and thermal infrared images (collectively referred to as "multispectral image information") (generated by the image generation unit 160). In other words, it has the function of acquiring specific wavelengths of sunlight reflected from trees from above and generating multispectral image information. The generated multispectral image information is sent to the outside via interface 180. In this embodiment, both the multispectral image and the thermal infrared image were combined to form the multispectral image information. However, it is also possible to use only the images acquired and generated by the multiband sensor unit 120 as the multispectral image information.

[0027] The first decision unit 200 is installed inside the personal computer (PC) 20. PC20 has a central control processing unit 22 that has the function of managing the overall control processing by referring to predetermined program information stored in memory 28, an input unit 24 that has the function of receiving various information sent from the outside and storing it in a predetermined storage area of ​​memory 28, an output unit 26 that has output and transmission functions for outputting various information and sending it to the outside, and a read / write memory 28 that has a work area for performing various tasks, a program information storage area which is a storage area for storing program information that the central control processing unit 22 refers to for various control processing and management, and a storage area for storing various information, and each is connected via signal lines 29. The multispectral image information is sent to the PC 20, for example, via the interface 180 of the camera 100, and stored in a predetermined storage area of ​​the memory 28 via the input unit 24 under the management of the central control processing unit 22 of the PC 20, in a read / write manner.

[0028] The first determination unit 200 has the function of determining whether or not the multispectral image information is within a preset first threshold by referring to a preset first diagnostic information. Specifically, the first determination unit 200 has the function of referring to the first diagnostic information storage area 28a of the memory 28 to determine whether the multispectral image information is within the first threshold, and if it is outside the first threshold, to what extent. This allows it to determine whether the leaf is a "dead leaf" (more than σ outside the threshold), a "stressed leaf" (within σ outside the threshold), or a "healthy leaf" (within the threshold). The first threshold may be stored in the first diagnostic information storage area 28a as part of the first diagnostic information.

[0029] The tree identification unit 300 is installed within the PC 20 and has the function of identifying the location of trees that have been determined by the first determination unit 200 to be outside the first threshold, specifically those with "dead leaves" or "stressed leaves," from the multispectral image information under the management of the central control processing unit 22. This identified tree information is stored in a predetermined storage area of ​​the memory 28 in a read / write manner under the management of the central control processing unit 22.

[0030] The tree identification unit 300 may be located outside the PC 20 or inside the camera 100 (not shown). In this case, the multispectral image information is sent to the tree identification unit 300 via the output unit 26 of the PC 20. The tree identification unit 300 receives the sent multispectral image information via an internally installed input / output unit. The tree identification unit 300 then identifies the location of trees with "dead leaves" or "stressed leaves" from the received multispectral image information and sends the information of these identified trees to the PC 20 via the input / output unit. The PC 20 receives the information of the identified trees sent to it via the input unit 24 under the management of the central control processing unit 22 and stores it in a predetermined storage area of ​​the memory 28 in a read / write manner. In other words, the identification by the first judgment unit 200 and the tree identification unit 300 is an aerial diagnosis that identifies unhealthy trees on site A by analyzing images taken of the entire site A where trees are located using a camera 100 mounted on the drone 10.

[0031] Here, the first threshold mentioned above can be set based on, for example, the NDVI (Normalized Difference Vegetation Index). In this case, the first determination unit 200 calculates the NDVI from the multispectral image information generated by the camera 100 and makes a judgment on the calculation result according to the following criteria (first threshold). The information for calculating the NDVI from the multispectral image information is stored in the first diagnostic information storage area 28a as part of the first diagnostic information. <When the first threshold is set to NDVI = greater than 0.1 and less than or equal to 1.0> Within the first threshold (NDVI value greater than 0.1 and less than or equal to 1.0): Healthy leaves Leaf stressed if its value falls between 0.9 and 1.1 above the upper limit of the first threshold (NDVI value greater than -0.1 and less than or equal to 0.1): A value between 1.1 and 2 from the upper limit of the first threshold (NDVI value between -1.0 and -0.1): Dead leaves The first threshold mentioned above can be changed as appropriate.

[0032] In this case, the tree identification unit 300 identifies the location of a tree with "dead leaves" or "stressed leaves" from the multispectral image information, based on the judgment result of the first judgment unit 200. The information of the identified trees can then be displayed on a display unit (not shown) located within the PC20, combined with multispectral image information, according to the following criteria. Healthy leaves: red Stressed leaves: yellow Dead leaves: Blue Furthermore, the information combining the identified tree information and multispectral image information is stored in a predetermined storage area of ​​the memory 28 in a read / write manner under the management of the central control processing unit 22. The method of displaying information about identified trees in the above display section (e.g., color coding) can be changed as appropriate.

[0033] The visual inspection unit 400 is installed within the PC 20 and has the function of acquiring visual inspection information for trees identified by the tree identification unit 300 through predetermined visual inspections under the management of the central control processing unit 22. The visual inspections include tapping tests, confirmation of the presence and depth of cavities, confirmation of the presence and absence of pests and diseases, confirmation of the presence or absence of fruiting bodies (mushrooms), and the condition of the bark. This visual inspection information is stored separately for the roots, trunk, and major branches as tree inspection sheet information in predetermined storage areas of the memory 28, and is readable and writable.

[0034] The visual inspection unit 400 may be located outside the PC20 (not shown). In this case, the information of the tree identified by the tree identification unit 300 is sent to the visual inspection unit 400 via the output unit 26 of the PC 20. The visual inspection unit 400 receives the identified tree information sent via an internal input / output unit. The visual inspection unit 400 then sends the acquired visual inspection information to the PC 20 via the input / output unit. The PC 20 receives the incoming visual inspection information via the input unit 24 under the management of the central control processing unit 22 and stores it in a predetermined storage area of ​​the memory 28 in a read / write manner.

[0035] The second determination unit 500 is installed inside the PC 20 and has the function of determining whether the tree diagnosis report information acquired by the visual diagnosis implementation unit 400 is within a preset second threshold by referring to a preset second diagnostic information under the management of the central control processing unit 22.

[0036] In other words, the second determination unit 500 has the function of referring to the second diagnostic information storage area 28b of the memory 28 and determining from the tree diagnosis report information whether there is a "risk of an unhealthy tree," or in other words, whether a detailed diagnosis is required. The second threshold value may be stored in the second diagnostic information storage area 28b as part of the second diagnostic information.

[0037] The stress wave velocity measurement unit 600 is installed inside the PC 20 and has the function of performing stress wave velocity measurements, which are a precise diagnosis, under the management of the central control processing unit 22 for trees that have been determined by the second judgment unit 500 to be "potentially unhealthy trees". This precise diagnosis involves setting driving sensors at equal intervals on the target tree and measuring the time it takes for the energy (stress wave) generated by lightly tapping each driving sensor in sequence in the circumferential direction of the tree to reach a receiving sensor. For example, a receiving sensor is provided in the stress wave velocity measurement unit 600, and the measured result information (stress wave velocity measurement information) is stored in a predetermined storage area of ​​the memory 28 in a read / write manner.

[0038] The stress wave velocity measurement unit 600 may be located outside the PC 20 (not shown). In this case, the stress wave velocity measurement unit 600 sends stress wave velocity measurement information to the PC 20 via an internally installed input / output unit. The PC 20 receives the sent stress wave velocity measurement information via the input unit 24 under the management of the central control processing unit 22 and stores it in a predetermined storage area of ​​the memory 28 in a read / write manner.

[0039] The third determination unit 700 is installed in the PC 20 and, under the management of the central control processing unit 22, has the function of determining whether the stress wave velocity measurement information is within a preset third threshold by referring to the third diagnostic information storage area 28c of the memory 28. Trees that the third determination unit 700 recognizes as being outside the third threshold are determined to be unhealthy trees. The third threshold mentioned above may be stored in the third diagnostic information storage area 28c as part of the third diagnostic information.

[0040] The flight path memory management unit 800 has the function of receiving actual flight path information transmitted from the drone 10's transmitter via the input unit 24 and storing it in the flight path information storage area 28d of the memory 28 in a read / write manner under the management of the central control processing unit 22. Of course, the flight path memory management unit 800 may be installed standalone on the drone 10 without being built into the PC 20, or it may be installed independently.

[0041] Furthermore, PC20 has an externally connected database (DB) capable of reading and writing large amounts of data.

[0042] The tree diagnosis process, based on the above configuration, is explained below.

[0043] When conducting a tree diagnosis on site A, first, an aerial diagnostic process is performed from above site A using drone 10 (see steps S100 to S300 in Figure 4).

[0044] Specifically, the camera 100 mounted on the drone 10 receives and acquires specific wavelengths of reflected sunlight from trees from above, and generates multispectral image information based on this (step S100).

[0045] Regarding the generated multispectral image information, the first determination unit 200 refers to the first diagnostic information in the first diagnostic information storage area 28a of the memory 28 to determine whether the multispectral image information is within the first threshold (step S200). This determines whether the condition of the tree leaves corresponds to "dead leaves," "stressed leaves," or "healthy leaves."

[0046] The tree identification unit 300 then identifies trees corresponding to the multispectral image information that was determined to be outside the first threshold ("dead leaves" or "stressed leaves") based on this judgment (step S300). This identifies trees that require visual inspection, i.e., candidates for unhealthy trees.

[0047] Next, a visual inspection is performed on the identified unhealthy tree candidates (step S400).

[0048] This involves the visual inspection unit 400 performing a visual inspection on a candidate tree and obtaining the results as tree diagnosis information (see step S420 in Figure 5). The second judgment unit 500 then uses this obtained tree diagnosis information to refer to the second diagnostic information storage area 28b of the memory 28 and determines whether there is a "risk of being an unhealthy tree" (whether it is within the second threshold) (step S440). This determines whether the candidate tree that has undergone a visual inspection requires a detailed diagnosis (steps S460, S480).

[0049] Finally, a detailed diagnosis is performed on trees that are deemed to require further examination (Step S500).

[0050] This involves the stress wave velocity measurement unit 600 performing stress wave velocity measurements on trees that require a detailed diagnosis and obtaining stress wave velocity measurement information for those trees (see step S520 in Figure 6). The third judgment unit 700 then refers to the third diagnostic information storage area 28c of the memory 28 to determine whether the obtained stress wave velocity measurement information is within the third threshold (step S540). If the result of this determination is outside the third threshold (Y in step S540), it is determined to be an unhealthy tree (step S560).

[0051] If, after such a tree diagnosis is performed, tree diagnoses are to be conducted again in the same area on a regular or irregular basis at a later date, the flight path used is managed by the flight path memory management unit 800, so aerial diagnoses can be performed using the same flight path.

[0052] According to the above embodiment, since target trees can be identified by aerial photography, it is no longer necessary to manually check each tree one by one, as in the conventional method, to select trees to be diagnosed. This allows for the selection and diagnosis of many trees in a short time, thereby improving the efficiency of tree diagnosis. Consequently, significant improvements can be made in terms of the manpower, time, cost, and management required for tree diagnosis, making it economically and practically useful.

[0053] Furthermore, according to the above embodiment, since the trees to be diagnosed can be identified without requiring deep specialized knowledge or extensive experience, areas that were previously impossible to diagnose due to a lack of manpower can be easily included in the diagnosis target. This allows for the early reduction of the risk of serious accidents caused by tree toppling or falling branches, contributing to safety and security.

[0054] Furthermore, according to the above embodiment, aerial imaging by the drone 10 makes it easier to identify trees to be diagnosed even in dangerous areas that are difficult for people to enter, compared to conventional methods, thus contributing to proactive conservation of the natural environment.

[0055] In the above embodiment, the first judgment unit 200, tree identification unit 300, visual diagnosis unit 400, second judgment unit 500, stress wave velocity measurement unit 600, third judgment unit 700, and flight path memory management unit 800 are installed inside the PC 20, but the configuration is not limited to this. For example, each unit may be installed independently and connected via a network.

[0056] Furthermore, in aerial imaging diagnostics for identifying potential unhealthy trees, it is certainly possible to overlay aerial photographs onto multispectral image information, for example, at the same scale. This would allow anyone to easily and accurately identify potential unhealthy trees.

[0057] The present invention is not limited to the embodiments described above, and various modifications are possible without departing from the spirit of the invention. [Explanation of Symbols]

[0058] 10… Drone 100 ... Multispectral camera (multispectral image information generation unit) 200 ...First judgment unit 300...Tree identification department 400 ... Visual Inspection Department 500 ... Second judgment unit 600 ... Stress wave velocity measurement section 700 ... Third judgment unit 800... Flight path memory management unit

Claims

1. A multispectral image information generation means mounted on a remotely controlled drone, which acquires specific wavelengths of sunlight reflected from trees from above and generates multispectral image information, A first determination means that determines whether the multispectral image information generated by this multispectral image information generation means is within a predetermined first threshold by referring to a predetermined first diagnostic information, A tree identification means that identifies a tree corresponding to the multispectral image information that has been determined to be outside the first threshold by this first determination means, This tree identification means performs a visual inspection on the trees identified by this tree identification means and obtains visual inspection information, A second determination means determines whether the visual inspection information obtained by this visual inspection means falls within a predetermined second threshold by referring to a predetermined second set of diagnostic information, A stress wave velocity measurement means for performing stress wave velocity measurements on trees corresponding to the visual diagnostic information that has been determined to be outside the second threshold by this second determination means, and obtaining stress wave velocity measurement information for said trees, A tree diagnostic system characterized by comprising a third determination means that, with respect to the stress wave velocity measurement information obtained by the stress wave velocity measurement means, refers to a third set diagnostic information and determines whether or not the stress wave velocity measurement information is within a third set threshold.

2. The tree diagnosis system according to claim 1, further comprising a flight path memory management means for storing and managing the flight path of a drone equipped with the above-mentioned multispectral image information generation means.

3. A tree diagnosis method that uses a remotely controlled drone capable of memorizing flight paths to diagnose the health of multiple trees, The drone is equipped with a multispectral camera that has a thermal infrared sensor function to acquire specific wavelengths of sunlight reflected from the trees from above the trees and generate multispectral image information. With respect to this multispectral image information, a predetermined first diagnostic information is referenced to determine whether the multispectral image information is within a predetermined first threshold. Based on this judgment, the trees corresponding to the multispectral image information that was determined to be outside the first threshold are identified. A visual inspection is performed on these identified trees to obtain visual inspection information. With respect to this visual diagnostic information, a pre-set second set of diagnostic information is referenced to determine whether the visual diagnostic information falls within a pre-set second threshold. Based on this judgment, stress wave velocity measurements are performed on trees corresponding to the visual diagnostic information that was determined to be outside the second threshold, and stress wave velocity measurement information for those trees is obtained. With respect to this stress wave velocity measurement information, a third set diagnostic piece of information is used to determine whether the stress wave velocity measurement information is within a third set threshold. A tree diagnostic method characterized by determining that trees corresponding to the stress wave velocity measurement information that is judged to be outside the third threshold based on this judgment are unhealthy trees.

Images