Tree health determination system

Abstract

To provide a tree soundness determination system that can easily determine the soundness of trees.SOLUTION: A tree soundness determination system 1 includes: an illumination unit 2 that irradiates a tree T with measurement light having a wavelength in a near-infrared region; a camera 3 that receives reflection light obtained by reflecting the measurement light by the tree T and captures a near-infrared image; a calculation unit 41 that calculates the reflection intensity of the reflection light at a wavelength of about 1,450 nm that is absorbed by moisture in the tree T based on the near-infrared image; and a determination unit 42 that determines the soundness of the tree T from the reflection intensity at a wavelength of about 1,450 nm calculated by the calculation unit 41 based on a relation between the reflection intensity and the soundness of the tree T, which has been stored in advance.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a tree health determination system for determining the health of trees such as street trees.

Background Art

[0002] Conventionally, tree diagnosis has been performed in which a tree doctor performs a regular plant diagnosis on each individual tree. Tree diagnosis includes visual observation and the like in which a tree doctor visually examines each tree for abnormalities such as bark damage and decay.

[0003] However, such tree diagnosis requires a highly skilled diagnostician, so there is a risk of a shortage of personnel and a decline in diagnostic ability in the future. In addition, a great deal of cost is required to regularly diagnose a huge number of trees. Furthermore, since the decline in the health of trees is constantly progressing, it is necessary to perform tree diagnosis in a timely manner.

[0004] As a method for efficiently evaluating the health of trees in place of such tree diagnosis by a tree doctor, as shown in Patent Document 1, a health database associating the spectral characteristics of leaves with the health of trees for each tree species, soil property, and nutrient in leaves is created in advance, the spectral characteristics of the leaves of trees are read using a high-resolution satellite image, and further, with reference to the health database from the soil property and nutrient data in leaves of the tree to be evaluated, the health of individual trees is evaluated.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] However, in the evaluation method described in Patent Document 1, it is necessary to collect the soil in which the trees are growing and the leaves of the trees, and then measure in advance the nutrients in the leaves that are closely related to the characteristics and components of the soil. This may require a huge amount of time and cost for the evaluation of the tree health.

[0007] Therefore, a technical problem to be solved arises in order to simply determine the health of the trees, and an object of the present invention is to solve this problem.

Means for Solving the Problem

[0008] In order to achieve the above object, a tree health determination system according to the present invention is a tree health determination system for determining the health of a tree, comprising: an illumination unit that irradiates the tree with measurement light having a wavelength in the near-infrared region; a camera that receives the reflected light reflected by the tree from the measurement light and captures a near-infrared image; a calculation unit that calculates the reflection intensity of the reflected light at a first wavelength absorbed by the moisture of the tree based on the near-infrared image; and a determination unit that determines the health of the tree from the reflection intensity at the first wavelength calculated by the calculation unit based on a relationship between the reflection intensity stored in advance and the health of the tree.

[0009] Further, in the tree health determination system according to the present invention, it is preferable that the illumination unit irradiates the tree with measurement light having a wavelength in the visible light region, the camera receives the reflected light reflected by the tree from the measurement light and captures a visible light image, the calculation unit detects the position coordinates of the concave portions formed on the bark surface of the tree from the visible light image, and calculates the reflection intensity at the first wavelength at the position coordinates of the concave portions.

[0010] Further, in the tree health determination system according to the present invention, the calculation unit calculates the reflection intensities of the reflected light at second and third wavelengths that are less likely to be absorbed by the moisture of the tree compared to the first wavelength, respectively, detects the position coordinates of a measurement area that is an area where the reflection intensities at the second and third wavelengths in the near-infrared image are substantially equal, and calculates the reflection intensity at the first wavelength in the measurement area, which is preferable.

[0011] Further, in the tree health determination system according to the present invention, a first threshold value set based on the reflection intensity of the reflected light acquired during a period when the photosynthesis of the tree is relatively inactive is stored in advance in the determination unit, and when the reflection intensity of the reflected light in the tree to be determined, which is acquired during a period when the photosynthesis of the tree is relatively active, is less than the first threshold value, the determination unit preferably determines that the health of the tree is high.

[0012] Further, in the tree health determination system according to the present invention, a first threshold value set based on the reflection intensity of the reflected light acquired during a period when the photosynthesis of the tree is relatively inactive is stored in advance in the determination unit, and when the reflection intensity of the reflected light in the tree to be determined, which is acquired during a period when the photosynthesis of the tree is relatively active, is substantially equal to the first threshold value, the determination unit preferably determines that the health of the tree is low.

[0013] Further, in the tree health determination system according to the present invention, a second threshold value set based on the reflection intensity of the reflected light of the tree with high health acquired during a period when the photosynthesis of the tree is relatively active is stored in advance in the determination unit, and when the reflection intensity of the reflected light in the tree to be determined is substantially equal to the second threshold value, the determination unit preferably determines that the health of the tree is high

[0014] In addition, in the tree health determination system according to the present invention, the determination unit stores in advance a first threshold value set based on the reflection intensity of the reflected light obtained during a period when the photosynthesis of the tree is relatively inactive, and a second threshold value set based on the reflection intensity of the reflected light of the tree with high health obtained during a period when the photosynthesis of the tree is relatively active. The determination unit preferably determines that the closer the reflection intensity of the reflected light in the tree to be determined is to the first threshold value, the lower the health, and the closer it is to the second threshold value, the higher the health.

Advantages of the Invention

[0015] According to this invention, it is not necessary to have a diagnostician with a high level of proficiency as in the conventional tree diagnosis, and the health of the tree can be easily determined.

Brief Description of the Drawings

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Embodiments for Carrying Out the Invention

[0017] An embodiment of the present invention will be described with reference to the drawings. In the following, when referring to the number of components, numerical values, amounts, ranges, etc., unless otherwise specifically stated and unless it is clearly limited to a specific number in principle, it is not limited to that specific number, and it may be more or less than the specific number.

[0018] Also, when referring to the shape, positional relationship, etc. of components, etc., unless otherwise specifically stated and unless it is clearly considered otherwise in principle, it includes those that are substantially similar or analogous to the shape, etc.

[0019] Also, the drawings may be exaggerated, such as enlarging characteristic parts for easy understanding of the characteristics, and the dimensional ratios of components are not necessarily the same as the actual ones.

[0020] FIGS. 1(a) and (b) are schematic diagrams showing the configuration of a tree health determination system 1 according to an embodiment of the present invention. The tree health determination system 1 according to this embodiment includes an illumination unit 2 and a camera 3.

[0021] The illumination unit 2 irradiates measurement light toward a tree T such as a street tree. The measurement light has wavelengths in the visible light region (about 400 to 800 nm) and in the near-infrared region (about 1000 to 1500 nm). The illumination unit 2 is, for example, a halogen lamp or the like. Note that the illumination unit 2 may be divided into one that irradiates measurement light having wavelengths in the visible light region and one that irradiates measurement light having wavelengths in the near-infrared region. The illumination unit 2 is arranged at a position about 1 m away from the tree T. Note that the distance from the tree T to the illumination unit 2 can be arbitrarily adjusted.

[0022] The camera 3 includes, for example, a SWIR camera having sensitivity in the near-infrared wavelength range. The camera 3 receives the reflected light having wavelengths in the near-infrared region that is reflected by the tree T of the measurement light and captures a near-infrared image. Imaging by the camera 3 is preferably performed at night in order to prevent reflected light derived from sunlight from being reflected in the near-infrared image. Further, imaging by the camera 3 is preferably performed on a sunny day in order to prevent the bark surface and leaves of the tree from being wetted by rainfall.

[0023] Further, at the same time as capturing the near-infrared image, the camera 3 receives the reflected light having wavelengths in the visible light region that is reflected by the tree T of the measurement light and captures a visible light image showing the trunk surface or the like of the tree T. Note that the camera 3 may be divided into one that captures a near-infrared image and one that captures a visible light image.

[0024] The camera 3 is arranged at a position about 1 m away from the tree T. Also, the ground height of the camera 3 is set to about 0.9 m, for example. Further, the reflection angle formed by the optical path of the measurement light irradiated from the illumination unit 2 and the optical path of the reflected light incident on the camera 3 reflected by the tree T is set to about 45 degrees. Note that the distance from the tree T to the camera 3, the ground height of the camera 3, and the reflection angle of the reflected light can be arbitrarily adjusted.

[0025] The operation of the tree health determination system 1 is controlled by the controller 4. The controller 4 controls each of the components that make up the tree health determination system 1. The controller 4 is composed of, for example, a CPU, a memory, etc. Note that the function of the controller 4 may be realized by controlling using software, or may be realized by operating using hardware. The controller 4 includes a calculation unit 41 and a determination unit 42.

[0026] The calculation unit 41 calculates the reflection intensity of the reflected light at the first wavelength (for example, about 1450 nm) where the absorption by the moisture of the tree T is significant, based on the near-infrared image captured by the camera 3. As shown in FIG. 2, inside the bark of the tree T, there is moisture composed of the conduit fluid, which is the moisture absorbed from the ground and sent from the roots to the whole tree T, and the sieve tube fluid, which is the nutrient generated in the leaves. When the measurement light of the first wavelength irradiated from the lighting unit 2 is present, the amount of light absorption increases in proportion to the amount of moisture inside the bark. That is, when the amount of moisture inside the bark is large and the humidity of the tree T is high, most of the measurement light of the first wavelength is absorbed, and in the near-infrared image, it appears darker (less light quantity). On the other hand, when the amount of moisture inside the bark is small and the humidity of the tree T is low, most of the measurement light of the first wavelength is reflected, and in the near-infrared image, it appears whiter (more light quantity).

[0027] The reflection intensity of the reflected light calculated by the calculation unit 41 may be the average value of the reflection intensities of the entire tree T shown in the near-infrared image, or may be the local reflection intensity in any region of the tree T shown in the near-infrared image.

[0028] Any region of the tree T in the near-infrared image is, for example, a recess formed on the bark surface of the tree T extracted by the calculation unit 41 from the visible light image. Thus, in the case of a tree species in which convex portions that are corked and dried on the bark surface and concave portions with a large water content are formed, by excluding the convex portions from the calculation of the reflection intensity and calculating the reflection intensity in the concave portions, the actual water content of the tree can be accurately grasped.

[0029] Alternatively, the calculation unit 41 may calculate the reflection intensities of the reflected light at a second wavelength (for example, about 1000 nm) and a third wavelength (for example, about 1100 nm) at which almost no absorption due to the moisture of the tree T occurs compared to the first wavelength, for the entire tree T captured in the near-infrared image, and set a measurement area extracted as an area where the reflection intensities of the reflected light at the second and third wavelengths of the tree T are substantially equal, to an arbitrary area of the tree T in the near-infrared image. As a result, by eliminating noise caused by light reflection and scattering (diffuse reflection), a highly reliable reflection intensity at the first wavelength can be obtained.

[0030] The determination unit 42 determines the soundness of the tree T from the reflection intensity at the first wavelength calculated by the calculation unit 41, based on the relationship between the reflection intensity of the reflected light and the soundness of the tree T, which has been obtained in advance through experiments or the like and stored.

[0031] Specifically, when the tree T performs photosynthesis, moisture for melting and absorbing carbon dioxide needs to be supplied from the roots of the tree T to the leaves through the conduits. A tree T with a large amount of moisture in the bark or on the back of the leaves and a high humidity is presumed to actively perform photosynthesis and respiration and grow. Therefore, for example, the determination unit 42 determines that a tree T that appears black with a reflection intensity lower than the threshold value in the near-infrared image calculated by the calculation unit 41, based on the threshold value of the reflection intensity of the reflected light set according to the tree species and measurement conditions, is lively and has a high soundness, and determines that a tree T that appears white with a reflection intensity higher than the threshold value in the near-infrared image calculated by the calculation unit 41 has a low soundness because photosynthesis and respiration are not sufficiently performed due to the progress of root decline or the like. Alternatively, the determination unit 42 may compare the reflection intensities at the first wavelength calculated by the calculation unit 41 for a plurality of trees T, respectively, and relatively determine the superiority or inferiority of the soundness among the plurality of trees T.

[0032] Note that the illumination unit 2, the camera 3, and the controller 4 may be mounted on a vehicle or the like and perform imaging of the near-infrared image and the visible light image by the camera 3 while relatively moving with respect to the tree T. Thereby, when a plurality of trees T are planted at intervals along a road like street trees, imaging of the plurality of trees T can be efficiently performed.

[0033] In this way, the tree health determination system 1 according to the present embodiment is a tree health determination system 1 for determining the health of the tree T, including an illumination unit 2 that irradiates the tree T with measurement light having a wavelength in the near-infrared region, a camera 3 that receives the reflected light reflected by the tree T from the measurement light and captures a near-infrared image, a calculation unit 41 that calculates the reflection intensity of the reflected light at a first wavelength absorbed by the moisture of the tree T based on the near-infrared image, and a determination unit 42 that determines the health of the tree T from the reflection intensity at the first wavelength calculated by the calculation unit 41 based on the relationship between the pre-stored reflection intensity and the health of the tree T.

[0034] With such a configuration, it is not necessary to have a diagnostician with a high level of proficiency like the conventional tree diagnosis, and the physiological health of the tree can be determined in a timely, simple, and low-cost manner.

Example

[0035] <Experimental Example 1> A comparative experiment between the tree health determination using the tree health determination system 1 for cherry trees, zelkova trees, or oaks and the tree diagnosis by a conventional arborist will be described.

[0036] ·Experimental procedure First, tree diagnosis by a conventional arborist was performed on cherry trees, zelkova trees, or oaks, and trees diagnosed as "healthy trees" and trees diagnosed as "unhealthy trees" in the tree diagnosis by the arborist were selected. In this experiment, the trees diagnosed as "healthy trees" were selected as those close to the trees diagnosed as "unhealthy trees". Here, an "unhealthy tree" means a tree that received a B2 determination (significant damage is observed) in the health determination of street tree diagnosis based on the "Manual for Street Tree Diagnosis etc. (Tokyo Metropolitan Bureau of Construction)", and a "healthy tree" means a tree that received an A determination (healthy or nearly healthy) or a B1 determination (damage that requires attention is observed).

[0037] Next, on a clear night, using a halogen lamp, measurement light was irradiated toward the trees diagnosed as "healthy trees" or "unhealthy trees", and with the camera 3, the reflected light from the trees was received to capture a near-infrared image at a wavelength of about 1450 nm. The camera 3 was provided with a band-pass filter, and the camera 3 captured a near-infrared image corresponding to a wavelength of about 1450 nm that passed through the band-pass filter. Subsequently, the calculation unit 41 calculated the average value of the reflection intensity of the reflected light of the entire tree shown in each near-infrared image.

[0038] · Experimental results Fig. 3(a) is a near-infrared image showing the trunk of the cherry trees belonging to the first group (hereinafter referred to as "cherry tree group 1") diagnosed as "unhealthy trees" by tree diagnosis, and Fig. 3(b) is a near-infrared image showing the trunk of the cherry tree group 1 diagnosed as "healthy trees" by tree diagnosis. Fig. 4(a) is a near-infrared image showing the trunk of the cherry trees belonging to the second group (hereinafter referred to as "cherry tree group 2") diagnosed as "unhealthy trees" by tree diagnosis, and Figs. 4(b) and (c) are near-infrared images showing the trunk of the cherry tree group 2 diagnosed as "healthy trees" by tree diagnosis. Fig. 5(a) is a near-infrared image showing the trunk of the oaks diagnosed as "unhealthy trees" by tree diagnosis, and Fig. 5(b) is a near-infrared image showing the trunk of the oaks diagnosed as "healthy trees" by tree diagnosis. Fig. 6(a) is a near-infrared image showing the trunk of the zelkovas diagnosed as "unhealthy trees" by tree diagnosis, and Fig. 6(b) is a near-infrared image showing the trunk of the zelkovas diagnosed as "healthy trees" by tree diagnosis. Also, Fig. 7 is a graph showing the average value of the reflection intensity of the reflected light in each near-infrared image. In this experiment, the image captured by the SWIR camera was divided into 256 gradations, with black being set as the reflection intensity 0 and white being set as the reflection intensity 256.

[0039] According to FIG. 7, it can be seen that in the first group of cherry trees, the second group of cherry trees, and the zelkova trees, the reflection intensity of "healthy trees" is lower than that of "unhealthy trees". That is, in tree species such as cherry trees and zelkova trees, a threshold value of the average value of the reflection intensity of the reflected light corresponding to "unhealthy trees" obtained in advance through experiments or the like (for example, cherry tree: 75, zelkova tree: 60) is set. When the average value of the reflection intensity of the trees in the near-infrared image captured by the camera 3 exceeds the threshold value, it can be determined that the tree is unhealthy. When it is below the threshold value, it can be determined that the tree is healthy.

[0040] On the other hand, in the oaks, it can be seen that the reflection intensity of "healthy trees" is higher than that of "unhealthy trees".

[0041] This is because in tree species such as oaks, kunugi oaks, chestnut trees, or ginkgo trees, the older sieve tubes of trees with higher soundness turn into periderm, and the old periderm becomes the bark. Cracks occur with annual ring growth, and unevenness is formed on the bark surface. Among the convex parts that occupy a large area on the bark surface, they are the old periderm where water supply is cut off, corkified, and drying is progressing. On the contrary, in the concave parts, a cortex of the newly formed periderm is formed, and the water content is high. Therefore, in the average value of the reflection intensity of the reflected light of the whole tree, the actual soundness of the tree cannot be accurately grasped.

[0042] Therefore, in tree species such as oaks, kunugi oaks, chestnut trees, or ginkgo trees, the calculation unit 41 detects the position coordinates of the concave parts from the visible light image captured by the camera 3, calculates the reflection intensity of the reflected light in the concave parts in the near-infrared image, sets a threshold value of the average value of the reflection intensity of the reflected light corresponding to "unhealthy trees" obtained in advance through experiments or the like. When the average value of the reflection intensity of the concave parts of the tree in the near-infrared image captured by the camera 3 exceeds the threshold value, it can be determined that the tree is unhealthy. When it is below the threshold value, it can be determined that the tree is healthy.

[0043] <Experimental Example 2> A comparative experiment between the tree soundness determination of the zelkova tree using the tree soundness determination system 1 and the tree diagnosis by a conventional tree doctor will be described.

[0044] · Experimental procedure First, five Japanese zelkova trees close to each other were selected, and tree diagnosis was performed on them by a conventional arborist.

[0045] Next, on a clear night, using a halogen lamp, measurement light was irradiated toward each tree, and a SWIR camera as camera 3 received the reflected light from the tree and captured an infrared image at a wavelength of about 1450 nm. Subsequently, the calculation unit 41 calculated the average value of the reflection intensity of the reflected light of the entire tree shown in each infrared image.

[0046] In addition, in order to evaluate the balance between photosynthesis for obtaining tree nutrients and the size of the tree body, a balance index obtained by dividing the crown projection area by the volume and a balance ratio obtained by multiplying 100 by the reciprocal of the balance index were used. The crown projection area was obtained by the measurer looking up at the crown of the tree, determining the edge of the crown where the branches extend most in 8 directions from the tree trunk, measuring the distance from the tree trunk to the edge of the crown with an 8-direction measuring tape, and calculating the projected area of the crown using a radar chart. The volume was calculated by estimating the radius of the tree trunk from the outer circumference of the tree trunk at a ground height of 1.2 m. Trees with a large balance ratio are presumed to have insufficient replenishment of photosynthesis amount and nutrients commensurate with the tree body, and the soundness is decreasing.

[0047] ·Experimental results The results of determining the vitality of the trees using the "Tree Survey Form of the Tokyo Arborist Society" by an arborist are shown in FIG. 8. The tree diagnosed as a "dangerous tree" was diagnosed as "slightly poor" with a vitality of 1.09 and a vitality classification of 2. Also, the four trees (healthy trees 1 to 4) diagnosed as "healthy trees" were diagnosed as "good" with a vitality of 0.45 to 0.55 and a vitality classification of 1. Note that only the "dangerous tree" received a B2 determination, which is the same as the soundness determination of the previous street tree diagnosis described in Experimental Example 1.

[0048] FIG. 9 is a graph showing the average value and balance ratio of the reflection intensity of the reflected light in the near-infrared images of five trees. Note that in the reflection intensity of this experiment, similar to Experimental Example 2, black was set as the reflection intensity of 0, and white was set as the reflection intensity of 256. FIG. 10 is a table showing the balance index, balance ratio, etc. of the dangerous tree and healthy trees 1 to 4. According to FIG. 9, it can be seen that the tendencies of the reflection intensity and the balance ratio are the same for the dangerous tree and healthy trees 1 to 4. Also, focusing on the reflection intensity, it can be seen that the bark of healthy tree 1 has the highest dryness, and focusing on the balance ratio, it can be seen that the photosynthetic ability required for maintaining the tree body of healthy tree 1 is inferior to that of other trees.

[0049] According to FIGS. 8 and 9, it can be seen that healthy tree 1 has a lower health level than the dangerous tree, which does not match the results of the previous street tree diagnosis described in Experimental Example 1 and the tree diagnosis results by arborists. This is presumably because in the health level determination of the conventional street tree diagnosis, due to reasons such as decay marks occurring on the tree due to heavy pruning of the main branches, poor bark condition, confirmed material decay, damage recognized on the tree trunk, or poor entanglement state after pruning, the health level is extremely lowly diagnosed. However, since there is almost no correlation between the presence or absence of decay marks associated with heavy pruning and the health level, it is speculated that the health level determination based on the reflection intensity of the reflected light accurately evaluates the actual state of the tree by focusing on the moisture content on the back side of the tree trunk.

[0050] <Experimental Example 3> A verification experiment on a method for accurately calculating the reflection intensity of the reflected light from the near-infrared image will be described.

[0051] · Experimental procedure (Case 1) Two near-infrared 500W lights and a SWIR camera (for example, Pika NIR-640 manufactured by RESONON) were prepared, and the average reflection intensity of the entire subject was measured based on the reflected light in the wavelength band of approximately 900 to 1700 nm reflected by the subject. As the subjects, two blocks of cypress cut into plate shapes were prepared, one was dried, and the other was moistened. Also, the surface of the subject was covered with wrap to suppress the drying of the block surface and the change in humidity due to illumination heat. Then, the angle of the measurement light with respect to the perpendicular line of the irradiated surface of the subject (incident angle) was set to approximately 30 degrees, and the distance between the subject and the SWIR camera was set to approximately 1 m. Figure 11 shows a near-infrared image at a wavelength of approximately 1100 nm, and Figure 12 shows a near-infrared image at a wavelength of approximately 1450 nm. The range surrounded by the dashed line in each image indicates the area where the average reflection intensity was evaluated.

[0052] · Experimental procedure (Case 2) The same lighting, SWIR camera, and subject as in Case 1 were prepared. The incident angle of the measurement light was set to approximately 60 degrees, and the distance between the subject and the SWIR camera was set to approximately 1 m. Based on the reflected light in the wavelength band of approximately 900 to 1700 nm reflected by the subject, the average reflection intensity of the entire subject was measured. Figure 13 shows a near-infrared image at a wavelength of approximately 1100 nm, and Figure 14 shows a near-infrared image at a wavelength of approximately 1450 nm. The range surrounded by the dashed line in each image indicates the area where the average reflection intensity was evaluated.

[0053] · Experimental procedure (Case 3) The same lighting, SWIR camera, and subject as in Case 1 were prepared, and the incident angle of the measurement light and the distance between the subject and the SWIR camera were set to be the same as in Case 1. Then, the calculation unit 41 extracted the coordinates of the region (measurement area) where the reflection intensity was substantially constant in the near-infrared images at wavelengths of about 1000 nm and about 1100 nm captured by the SWIR camera. Subsequently, the SWIR camera measured the reflection intensity of the reflected light in the wavelength band of about 900 to 1700 nm reflected from the measurement area of the subject. Fig. 15 shows an image indicating the ratio (band ratio) of the reflection intensities at wavelengths of about 1000 nm and about 1100 nm for the dry and wet subjects. In the image of Fig. 15, the regions where there is no change in the reflection intensity according to the wavelength change are shown in white. The range surrounded by the solid line in each image indicates the measurement area, and the range surrounded by the dashed line in each image indicates the region where the reflection intensity fluctuates particularly greatly.

[0054] · Experimental procedure (Case 4) The same lighting, SWIR camera, and subject as in Case 2 were prepared, and the incident angle of the measurement light and the distance between the subject and the SWIR camera were set to be the same as in Case 2. Also, as in Case 3, the calculation unit 41 extracted the coordinates of the region (measurement area) where the reflection intensity was substantially constant in the near-infrared images at wavelengths of about 1000 nm and about 1100 nm captured by the SWIR camera, and the SWIR camera measured the reflection intensity of the reflected light in the wavelength band of about 900 to 1700 nm reflected from the measurement area of the subject.

[0055] · Experimental results Fig. 16 is a graph showing the average reflection intensity of the reflected light in the wavelength band of about 900 to 1700 nm over the entire surface of the subject in Cases 1 and 2. Fig. 17 is a graph showing the reflection intensity of the reflected light in the wavelength band of about 900 to 1700 nm within the measurement area in Cases 3 and 4. In this experiment, the reflection intensity was taken as the measured value of the SWIR camera (maximum value: about 65535).

[0056] According to FIG. 16, it can be seen that at a wavelength of about 1450 nm that is absorbed by moisture, changes in the reflection intensity according to the level of humidity are detected. On the other hand, at a wavelength of about 1100 nm that is not absorbed by moisture, although the reflection intensity should originally be kept substantially constant regardless of the level of humidity, it can be seen that the reflection intensity is changing.

[0057] Such a change in the reflection intensity (noise) that should not originally occur is presumably caused by light reflection and scattering (diffuse reflection) due to the incident angle of the measurement light on the subject, the incident angle of the reflected light on the SWIR camera, the distance from the subject to the SWIR camera, or the deflection of the wrap covering the subject. And it is presumed that such noise will similarly occur depending on lighting, camera-side conditions (position, incident / reflection angle, illuminance, etc.) or subject-side conditions (bark state, tree trunk shape, etc.) even when photographing street trees.

[0058] On the other hand, according to FIG. 17, in Cases 3 and 4, in the entire wavelength band of about 1100 nm or less, the reflection intensity is the same regardless of the level of humidity, and since the noise caused by the above-described light reflection and scattering (diffuse reflection) is corrected, it is presumed that the reliability of the detection result of the reflection intensity according to the level of humidity at a wavelength of 1450 nm that is absorbed by moisture is high.

[0059] Also, as shown in Fig. 18, the difference in the reflection intensities of subjects with different humidities at a wavelength of approximately 1450 nm in Case 1 (dry: 5330, wet: 1693) is 3637, and the difference in the reflection intensities of subjects with different humidities at a wavelength of approximately 1450 nm in Case 2 (dry: 7247, wet: 3359) is 3888. In contrast, the difference in the reflection intensities of subjects with different humidities at a wavelength of approximately 1450 nm in Case 3 (dry: 5644, wet: 1883) is 3760, and the difference in the reflection intensities of subjects with different humidities at a wavelength of approximately 1450 nm in Case 4 (dry: 7876, wet: 4069) is 3807. Thus, it can be seen that in Cases 3 and 4, the difference in reflection intensity is reduced even between two cases with different incident angles and distances compared to Cases 1 and 2. Thereby, it can be understood that by using the reflection intensity of the reflected light at a wavelength of approximately 1450 nm in the measurement area within the subject, the soundness can be accurately determined even for a group of trees with various bark states or trunk shapes mixed together.

[0060] <Experimental Example 4> A verification experiment on a method for determining the soundness of trees in summer using a threshold value (first threshold value) set based on the reflection intensity of the reflected light obtained in winter will be described.

[0061] ·Experimental Procedure First, in this experiment, the trees were classified into the following six groups according to the tree species. (1) Group 1 (G1): Deciduous broad-leaved trees such as zelkova and muku-noki, whose outer bark is thin and whose water retention capacity of the outer bark is low and the outer bark surface is likely to dry in a state where the outside air is dry, especially in winter. (2) Group 2 (G2): Deciduous broad-leaved trees such as akikire, enoki, and someiyoshino, whose outer bark is thick and whose water retention capacity of the outer bark is high and the outer bark surface is unlikely to dry in a state where the outside air is dry, especially in winter. (3) Group 3 (G3): Deciduous broad-leaved trees such as yurinoki, kaki-noki, konara, and ginkgo, whose outer bark thickness belongs to approximately the middle of the deciduous broad-leaved trees belonging to G1 and G2. (4) Group 4 (G4): Evergreen broad-leaved trees such as Japanese white birch and Japanese oak, with a small amount of foliage due to pruning of thick branches corresponding to the trunk thickness. The outer bark thickness of Group 4 corresponds to that of Group 3. (5) Group 5 (G5): Evergreen broad-leaved trees such as Japanese white birch and Japanese oak, with a large amount of foliage as thick branches corresponding to the trunk thickness have not been pruned. The outer bark thickness of Group 5 corresponds to that of Group 3. (6) Group 6 (G6): Evergreen coniferous trees such as Japanese red pine.

[0062] Next, two 36V45W near-infrared illuminations (for example, HySiL1500-2 manufactured by KLS) and a SWIR camera (for example, NIR640SN manufactured by Vision Sensing) were prepared, and the angle (incident angle) of the measurement light with respect to the perpendicular line of the irradiated surface of the subject was set to approximately 0 degrees, and the distance between the subject and the SWIR camera was set to approximately 1 m. Then, for Groups 1 to 6, the reflection intensity of the entire subject was measured based on the reflected light in the wavelength band of approximately 1450 nm reflected by the subject (trees) during the period when the photosynthesis of the trees was relatively inactive (winter).

[0063] · Experimental results Figure 19 is a table showing the average reflection intensity of near-infrared images at a wavelength of approximately 1450 nm by tree species measured in winter. The average reflection intensity in Figure 19 means that value when the reflection intensity is measured from one direction with respect to the tree, and means the average value thereof when the reflection intensity is measured from a plurality of directions with respect to the tree. Figure 20 is a graph showing the average reflection intensity by tree species described in Figure 19. Figures 21(a) to (c) are near-infrared images at a wavelength of approximately 1450 nm taken of the trunks of three Japanese zelkovas diagnosed as healthy trees in Experimental Example 1 during the period when the photosynthesis of the trees was relatively active (summer), and (d) is a near-infrared image taken of the trunk of a Japanese zelkova diagnosed as an unhealthy tree in Experimental Example 1 in summer. Figure 22 is a graph showing the average reflection intensity of each of the near-infrared images of the three Japanese zelkovas shown in Figure 21 and the first threshold value described later of the tree species group G1 to which the Japanese zelkovas belong. In this experiment, the reflection intensity was taken as the measured value of the SWIR camera (maximum value: approximately 65535).

[0064] According to FIGS. 19 and 20, it can be seen that the reflection intensity measured in winter shows no significant difference between unhealthy trees with low soundness and healthy trees with high soundness among the same tree species. This is presumably because in winter, the energy of sunlight is weak, and furthermore, deciduous trees shed their leaves, and regardless of the soundness of the trees, photosynthesis is inactive and the bark moisture content is low. Therefore, considering that the reflection intensity measured in winter corresponds to dormant trees, a first threshold value for use in soundness determination was set for each of Groups 1 to 6 based on the reflection intensity measured in winter. Note that the "dormant state" refers to a state in which, like during winter, due to leaf shedding or a decrease in solar energy, etc., the amount of photosynthesis and the amount of water absorption from the roots are minimized, and the activity of growth activities has decreased.

[0065] Specifically, assuming that the reflection intensity within Group 1 is about 30,000 and a difference of about ±10% is not significant, the first threshold value for Group 1 was set to 28,000. Similarly, since the reflection intensity within Group 2 is about 20,000, the first threshold value for Group 2 was set to 18,000. Since the reflection intensity within Group 3 is about 25,000, the first threshold value for Group 3 was set to 23,000. Since the reflection intensity within Group 4 is about 23,000, the first threshold value for Group 4 was set to 22,000. Since the reflection intensity within Group 5 is about 16,000, the first threshold value for Group 5 was set to 15,000. Since the reflection intensity within Group 6 is about 28,000, the first threshold value for Group 6 was set to 28,000. Note that the threshold values for Groups 1 to 6 are not limited to the above-mentioned numerical values and may vary according to conditions on the lighting and camera sides (position, incident / reflection angle, illuminance, etc.) or conditions on the subject side (bark state, tree trunk shape, etc.).

[0066] As shown in FIG. 22, the reflection intensities of the reflected light in three Japanese zelkova trees (healthy trees 1 to 3) measured in summer, corresponding to the images in FIGS. 21(a) to (c), are 15437, 17784, and 16900 in order, and it can be seen that they are far below the first threshold value of 28000 for Group 1. This is presumably because in summer, the energy of sunlight is strong, deciduous trees have leaves, and photosynthesis is active, so the bark moisture content in summer is higher than that in winter. Therefore, these trees are determined to be healthy trees with a high degree of soundness. The reflection intensity of the reflected light in the Japanese zelkova tree measured in summer, corresponding to the image in FIG. 21(d), was 29442.

[0067] In this way, when the reflection intensity of the reflected light in the tree to be determined measured in summer is less than the first threshold value of the group to which the tree belongs, it can be determined that the bark moisture content of the tree is higher and the soundness is higher than that of the dormant tree. On the other hand, when the reflection intensity of the reflected light in the tree measured in summer is approximately equal to the first threshold value of the group to which the tree belongs, it can be determined that the bark moisture content of the tree is low and the soundness is low, similar to the dormant tree. Note that being approximately equal to the first threshold value means, for example, a range (such as ±10% or less) within which it can be assumed that there is no significant difference based on the first threshold value.

[0068] Also, regarding the reflection intensities of the reflected light in the healthy trees belonging to each of Groups 1 to 6 measured in summer, considering that they correspond to trees with a high degree of soundness, a second threshold value for determining whether the soundness is high or not can be set based on these reflection intensities.

[0069] Specifically, when the second threshold value for Group 1 indicating trees in a highly healthy state is set to 17,000 based on the reflection intensity of the reflected light in the healthy trees 1 to 3 of Group 1 shown in FIG. 22, when the reflection intensity of the reflected light in the trees belonging to Group 1 is approximately equal to the second threshold value, the tree is determined to be highly healthy on the assumption that the bark moisture content is high, similar to healthy trees. Note that being approximately equal to the second threshold value means, for example, a range (±10% or less, etc.) within which it can be assumed that there is no significant difference based on the second threshold value. In the present embodiment, the numerical value of Group 1 is exemplified as the second threshold value, but the second threshold values for the other Groups 2 to 6 are set separately.

[0070] Also, the threshold values (first threshold values) set based on the respective reflection intensities of the reflected light in the dormant trees belonging to each of Groups 1 to 6 measured in winter and the threshold values (second threshold values) set based on the respective reflection intensities of the healthy trees belonging to each of Groups 1 to 6 measured in summer are stored in advance, and it may be determined that the closer the reflection intensity of the reflected light in the tree to be determined is to the first threshold value of the Groups 1 to 6 to which the tree belongs, the lower the health level, and the closer it is to the second threshold value of the Groups 1 to 6 to which the tree belongs, the higher the health level.

[0071] Note that the present invention can be variously modified without departing from the spirit of the present invention, and it is natural that the present invention extends to such modified ones.

Explanation of Signs

[0072] 1: Tree health determination system 2: Lighting unit 3: Camera 4: Controller 41: Calculation unit 42: Determination unit T: Tree

Claims

1. A tree health determination system for determining the health of a tree, comprising: an illumination unit that irradiates measurement light having a wavelength in the near-infrared region toward the trunk of the tree; a camera that receives the reflected light reflected from the bark surface of the trunk and captures a near-infrared image; a calculation unit that calculates the reflection intensity of the reflected light at a first wavelength absorbed by the moisture of the tree based on the near-infrared image; a determination unit that determines the health of the tree from the reflection intensity at the first wavelength calculated by the calculation unit based on the relationship between the pre-stored reflection intensity and the health of the tree; A tree health determination system, characterized by comprising the above.

2. The illumination unit irradiates measurement light having a wavelength in the visible light region toward the trunk, the camera receives the reflected light reflected from the bark surface and captures a visible light image, the calculation unit detects the position coordinates of the recess formed on the bark surface from the visible light image, and calculates the reflection intensity at the first wavelength at the position coordinates of the recess. The tree health determination system according to claim 1, characterized by the above.

3. The calculation unit calculates the reflection intensities of the reflected light at second and third wavelengths that are less likely to be absorbed by the moisture of the tree compared to the first wavelength, respectively, and detects the position coordinates of a measurement area that is an area where the reflection intensities at the second and third wavelengths in the near-infrared image are substantially equal, and calculates the reflection intensity at the first wavelength in the measurement area. The tree health determination system according to claim 1, characterized by the above.

4. The determination unit pre-stores a first threshold value set based on the reflection intensity of the reflected light obtained during a period when the photosynthesis of the tree is relatively inactive, The determination unit determines that the health of the tree is high when the reflection intensity of the reflected light in the tree to be determined obtained during a period when the photosynthesis of the tree is relatively active is less than the first threshold value. The tree health determination system according to claim 1, characterized by the above.

5. The determination unit pre-stores a first threshold value set based on the reflection intensity of the reflected light obtained during a period when the photosynthesis of the tree is relatively inactive, The determination unit determines that the soundness of the tree is low when the reflection intensity of the reflected light in the tree, which is the object to be determined and is acquired during a period when the photosynthesis of the tree is relatively active, is substantially equal to the first threshold value. The tree soundness determination system according to claim 1, characterized in that.

6. The determination unit stores in advance a second threshold value set based on the reflection intensity of the reflected light of the tree that is acquired during a period when the photosynthesis of the tree is relatively active and has a high soundness. The determination unit determines that the soundness of the tree is high when the reflection intensity of the reflected light in the tree, which is the object to be determined, is substantially equal to the second threshold value. The tree soundness determination system according to claim 1, characterized in that.

7. The determination unit stores in advance a first threshold value set based on the reflection intensity of the reflected light acquired during a period when the photosynthesis of the tree is relatively inactive, and a second threshold value set based on the reflection intensity of the reflected light of the tree that is acquired during a period when the photosynthesis of the tree is relatively active and has a high soundness. The determination unit determines that the closer the reflection intensity of the reflected light in the tree, which is the object to be determined, is to the first threshold value, the lower the soundness, and the closer it is to the second threshold value, the higher the soundness. The tree soundness determination system according to claim 1, characterized in that.

8. The illumination unit irradiates the measurement light at night. The camera captures the near-infrared image at night. The tree soundness determination system according to claim 1, characterized in that.

9. The illumination unit and the camera are mounted on a vehicle that can move horizontally with respect to the tree. The illumination unit irradiates the measurement light while moving horizontally. The camera captures the near-infrared image while moving horizontally. The tree soundness determination system according to claim 1, characterized in that.

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