Estimation method and estimation device

Abstract

Provided is a method for easily and highly accurately calculating the wet weight of seaweed. This estimation method is used for estimating a growth index of an object growing in water through photosynthesis, the method comprising: a step for measuring the photon density in the vicinity of the object during or after growth; and a step for estimating a growth index of the object during or after growth on the basis of the measured photon density and information indicating the relationship between the photon density and the growth index.

Description

Estimation method and estimation apparatus

[0001] This disclosure relates to a method and apparatus for estimating the wet weight of seaweed. This disclosure claims priority under Japanese Patent Application No. 2024-184186, filed in Japan on 18 October 2024, the contents of which are incorporated herein by reference.

[0002] Amid efforts toward decarbonization, blue carbon is attracting attention. Blue carbon refers to "carbon absorbed and stored in marine ecosystems such as seagrass, mangroves, and salt marshes, after CO2 from the atmosphere has been taken up by marine organisms." Compared to green carbon (carbon taken up by terrestrial organisms such as trees and grass), blue carbon has the following characteristics: (1) High capacity to absorb CO2 from the atmosphere (storing carbon up to approximately 40 times faster). (2) High sustainability of carbon sequestration (while green carbon has a storage period of several decades, blue carbon has a storage period of several hundred to several thousand years). In Japan, JBE (Japan Blue Economy Association) issues blue carbon credits (J Blue Credits).

[0003] The JBE manual states that the amount of CO2 absorbed by aquaculture facilities can be calculated using the following formula (1) for calculating the amount of CO2 absorbed by blue carbon: CO2 absorbed = Area of ​​aquaculture facility (or length of cultivation rope) × Wet weight per unit area (or length of rope) × (1 - moisture content) × Carbon content × 44 / 12 × P / B ratio × Retention rate ... (1) Sample analysis values ​​or literature values ​​should be applied to the moisture content and carbon content in formula (1), and literature values ​​should be applied to the P / B ratio and retention rate. Therefore, in order to accurately calculate the amount of CO2 absorbed, it is important to accurately determine the wet weight of the cultivated kelp and other seaweed. Currently, the only way to measure wet weight is to harvest all (or representative points) of the kelp and other seaweed and measure the wet weight using a crane or similar equipment. However, this method is time-consuming and labor-intensive. A boat is needed to harvest the kelp and other seaweed, and the amount of CO2 emitted by the boat must be subtracted from the amount of CO2 absorbed by the cultivated kelp as CO2 emissions. Once landed, they are treated as industrial waste and cannot be released into the sea (and if they cannot be released into the sea, they cannot be counted as CO2 absorption), among other issues.

[0004] In seaweed and seaweed beds, there is a method of aerial photographing the target area from an unmanned aircraft such as a so-called drone, obtaining the coverage from the image, and estimating the wet weight from the relational expression between the coverage and the wet weight described in the JBE manual. However, applying this method to the wet weight of seaweed in aquaculture facilities is not approved by JBE. Even if this method were applicable, the aquaculture kelp in the water depth direction might not be considered as coverage, lacking accuracy in wet weight calculation. There is also an issue that a person who can operate an unmanned aircraft is required.

[0005] Guidelines for J-Blue Credit (Registered Trademark) Certification Application Ver.2.4, [online], March 2024, Japan Blue Economy Technology Research Consortium, [searched on September 17, 2024], Internet <https: / / www.blueeconomy.jp / wp-content / uploads / jbc2024 / 20240312_J-BlueCredit_Guidline_v.2.4.pdf>

[0006] A method for easily and accurately calculating the wet weight of seaweed is required.

[0007] The present disclosure provides an estimation method and an estimation device capable of solving the above problems.

[0008] According to one aspect of the present disclosure, the estimation method is a method for estimating the growth index of an object growing in water by photosynthesis, including a step of measuring the photosynthetic photon density near the object during or after growth, and a step of estimating the growth index of the object during or after growth based on the measured photosynthetic photon density and information indicating the relationship between the photosynthetic photon density and the growth index.

[0009] According to one aspect of the present disclosure, the estimation device is an estimation device for estimating the growth index of an object growing in water by photosynthesis, including an acquisition unit that acquires a measured value of the photosynthetic photon density near the object during or after growth, and an estimation unit that estimates the growth index of the object during or after growth based on the measured photosynthetic photon density and information indicating the relationship between the photosynthetic photon density and the growth index.

[0010] According to the estimation method and apparatus described above, the wet weight of seaweed can be calculated easily and with high accuracy.

[0011] This is a schematic diagram of the aquaculture equipment according to each embodiment. This is a block diagram showing an example of the estimation system according to each embodiment. This is a diagram showing an example of the weight characteristics per kelp sheet in the direction of water depth according to the first embodiment. This is a diagram showing an example of the leaf number characteristics per kelp plant in the direction of water depth according to the first embodiment. This is a diagram showing an example of a graph showing the relationship between photon flux density, wet kelp weight, and leaf number according to the first embodiment. This is a flowchart showing an example of the weight estimation process according to the first embodiment. This is a second flowchart showing an example of the weight estimation process according to the second embodiment. This is a diagram showing an example of time history data of photon flux density according to the second embodiment. This is a diagram showing an example of a graph showing the relationship between the average or cumulative value of photon flux density and wet kelp weight and leaf number according to the second embodiment. This is a diagram showing an example of time history data of photon flux density according to the third embodiment. This is a diagram showing an example of a graph showing the relationship between the average or cumulative value of photon flux density and wet kelp weight and leaf number according to the third embodiment. This is a diagram showing an example of the hardware configuration of the estimation device according to each embodiment.

[0012] <First Embodiment> The method for estimating the weight of seaweed according to this disclosure will be described below with reference to the drawings. As an example of seaweed, cultivated kelp will be used to describe the method for estimating the wet weight of cultivated kelp. Figure 1 is a side view of a sea area where kelp is cultivated. The top of the paper is the sea surface, and the bottom is the seabed. A main rope 3 is stretched horizontally around the cultivation area. The main rope 3 is anchored to the seabed via a mooring rope 4, one end of which is fixed to the seabed by a weight 9. On the other hand, the main rope 3 is connected to a plurality of floats 1 floating on the ocean surface via a float rope 2 and is suspended from the sea surface side. A plurality of cultivation ropes 5, each with a weight 6 attached to its lower end, are connected to the main rope 3. The cultivation ropes 5 are also called curtain ropes and extend vertically downwards. The kelp is grown attached to the cultivation ropes 5. For example, the kelp may be cultivated for carbon sequestration. In the aquaculture farm illustrated in Figure 1, kelp is cultivated to absorb CO2, and then the kelp is released and sunk to the depths of the seabed to store the CO2. The stored CO2 is counted as a reduction in greenhouse gas emissions and certified as blue credits. In Figure 1, symbol 7 represents one kelp plant cultivated on a cultivation line 5. Multiple plants 7 are cultivated on one cultivation line 5, and each plant 7 contains multiple leaves. On one cultivation line 5, the plants 7 at the top tend to have a heavier wet weight and more leaves. This is because the amount of light is greater at the top, promoting kelp growth. Seaweed grows through photosynthesis. Photosynthesis is a reaction that occurs when a single photon is absorbed, and the amount of growth is more correlated with the amount of photons than with the irradiation intensity (W / m2). Therefore, by measuring the photon density, it is possible to estimate the weight of seaweed with accuracy. There is also a correlation between water depth and the amount of light reaching that location. Therefore, in this embodiment, in order to measure the photon quantum density according to the water depth, multiple photometers 8 are arranged vertically along the cultivation line 5, and fixed-point measurements are performed simultaneously and in parallel by the multiple photometers 8. The photometers 8 are placed near the positions on the cultivation line 5 where kelp is attached. For example, the photometers 8 may be attached to a buoy and adjusted with weights or the like to be fixed at a predetermined water depth. Alternatively, the photometers 8 may be moored to the cultivation line 5 or the seabed. The amount of photons measured by the multiple photometers 8 is input to the estimation device 10 on land.Multiple photometers 8 and estimation devices 10 may be connected to each other via wireless or wired communication means, or multiple photometers 8 may be connected to a communication device (not shown), where the measured values ​​from each photometer 8 are collected by the communication device and transmitted from this communication device to the estimation device 10. For example, a battery-powered (or solar-powered or wave-powered) LPWA (Low Power Wide Area) wireless device may be attached to a buoy to which the photometers 8 are mounted, and the photon density measured by the multiple photometers 8 may be transmitted to the estimation device 10 on the ground. For example, a timer may be used to transmit data several times during the day. Alternatively, an inspector may organize the photon density measured by each photometer 8 and input the measured values ​​for each photometer 8 into the estimation device 10. The photometers 8 measure the photon density at the same location, for example, twice a month or more, starting from the start of kelp cultivation. The estimation device 10 estimates the wet weight per kelp leaf and the number of leaves per kelp plant at each water depth based on the photon density measured by the photon meter 8, and estimates the wet weight of kelp for each cultivation line 5. In Figure 1, six photon meter 8s are arranged along the fifth cultivation line 5 from the left, but the number and placement of the photon meter 8s are not limited to this. For example, it is desirable to have four or more photon meter 8s arranged at 2m intervals in the water depth direction.

[0013] Figure 2 shows an example of an estimation system. The estimation system 100 includes a plurality of photometers 8 and an estimation device 10. The estimation device 10 includes a sensor value acquisition unit 11, a weight estimation unit 12, a CO2 absorption amount calculation unit 13, and a storage unit 14. The sensor value acquisition unit 11 acquires the measured values ​​(photon quantum density) of the plurality of photometers 8 and records the measured value for each photometer 8 in the storage unit 14. The weight estimation unit 12 estimates the wet weight of the kelp based on the photon quantum density acquired by the sensor value acquisition unit 11 and a table or function that shows the relationship between photon quantum density and growth indicators of kelp, etc. The CO2 absorption amount calculation unit 13 calculates the amount of CO2 absorbed by the kelp using the wet weight of the kelp estimated by the weight estimation unit 12 and formula (1) described in the JBF manual. The storage unit 14 stores the measured values ​​acquired by the sensor value acquisition unit 11, a table or function that shows the relationship between photon quantum density and growth indicators of kelp, etc.

[0014] Figure 3 shows an example of the weight characteristics per kelp sheet with respect to water depth. Inspectors harvest kelp near each photonometer 8 positioned along the water depth direction when the kelp is at its most mature stage and measure the wet weight per kelp leaf. Then, they organize the relationship between the measured wet weight and the photon density measured by nearby photonometers 8 for each water depth and create the graph in Figure 3. In Figure 3, the left vertical axis represents the wet weight of the kelp, the right vertical axis represents the photon density, and the horizontal axis represents the water depth. As shown in Figure 3, there is a correlation between photon density and wet weight, and as the water depth increases, the photon density decreases, and the wet weight per kelp leaf also decreases.

[0015] Figure 4 shows an example of the characteristics of the number of leaves per kelp plant in relation to water depth. When the kelp is at its most mature stage, the inspector harvests kelp near each photonometer 8 positioned in the direction of water depth and counts the number of leaves per kelp plant. Then, the relationship between the counted number of leaves and the photon density measured by nearby photonometers 8 is organized for each water depth and the graph in Figure 4 is created. In Figure 4, the left vertical axis represents the number of leaves per kelp plant, the right vertical axis represents the photon density, and the horizontal axis represents water depth. As shown in Figure 4, there is a correlation between photon density and the number of leaves, and as the water depth increases, the photon density decreases and the number of leaves per plant also decreases.

[0016] Figures 3 and 4 show that there is a positive correlation between photon flux density and the number of kelp leaves, and between photon flux density and the wet weight of kelp. Figure 5 shows these relationships. The inspector analyzes the relationship between photon flux density and the number of leaves per kelp plant and creates graphs or functions (referred to as leaf number characteristic data) that show the relationship between photon flux density and the number of leaves per kelp plant (for example, graph 51 in Figure 5). The inspector also analyzes the relationship between photon flux density and the wet weight per kelp leaf and creates graphs or functions (referred to as weight characteristic data) that show the relationship between photon flux density and the wet weight per kelp leaf (for example, graph 52 in Figure 5). These graphs and functions may also be created by the estimation device 10. For example, the weight estimation unit 12 performs regression analysis on the graphs in Figures 3 and 4 to create leaf number characteristic data showing the relationship between photon flux density and the number of leaves per kelp plant, and weight characteristic data showing the relationship between photon flux density and the wet weight per kelp leaf. The created functions and other data are recorded in the memory unit 14. The weight estimation unit 12 uses the leaf number characteristic data, weight characteristic data, and the photon density measured by the photon meter 8 to estimate the wet weight of the seaweed at the time of release.

[0017] (Operation) Next, the flow of the weight estimation process will be explained with reference to Figure 6. Figure 6 is a flowchart showing an example of the weight estimation process according to the first embodiment. First, weight characteristic data and leaf number characteristic data are created (step S1). For example, the weight estimation unit 12 analyzes the relationship between the photon flux density and the weight of one kelp leaf based on the graph in Figure 3 and creates weight characteristic data (Figure 5). The weight estimation unit 12 analyzes the relationship between the photon flux density and the number of leaves per kelp plant based on the graph in Figure 4 and creates leaf number characteristic data (for example, Figure 5). The weight estimation unit 12 records the weight characteristic data and leaf number characteristic data in the storage unit 14.

[0018] Next, the sensor value acquisition unit 11 acquires the photon density measured by multiple photometers 8 during the release of seaweed (step S2). The sensor value acquisition unit 11 records the photon density for each photometer 8 in the storage unit 14.

[0019] Next, the weight estimation unit 12 estimates the wet weight (step S3). For example, when estimating the wet weight of kelp on a single cultivation line 5 in Figure 1, six kelp plants are attached to the cultivation line 5. For the bottommost plant 7, the weight estimation unit 12 estimates the weight per kelp leaf at the same water depth based on the photon density measured by the photon meter 8 near the plant 7 (photon meter 8 at the bottom of the page) and weight characteristic data (for example, graph 52 in Figure 5). The weight estimation unit 12 estimates the number of leaves contained in the kelp plant 7 at the same water depth based on the photon density measured by the photon meter 8 at the bottom of the page and leaf number characteristic data (for example, graph 51 in Figure 5). The weight estimation unit 12 multiplies the estimated weight per kelp leaf by the estimated number of leaves to estimate the wet weight of the bottommost plant 7. The weight estimation unit 12 similarly estimates the wet weight of the second to the top row of kelp plants 7 based on the photon flux density, leaf number characteristic data, and weight characteristic data measured at each water depth. The weight estimation unit 12 then sums the wet weights of all the plants 7 to estimate the wet weight of the kelp grown on one cultivation line 5. The weight estimation unit 12 similarly estimates the wet weight of the kelp grown on the cultivation lines 5 of the other rows.

[0020] Next, the CO2 absorption amount calculation unit 13 calculates the amount of CO2 absorbed (step S4). For example, the CO2 absorption amount calculation unit 13 calculates the amount of CO2 absorbed by kelp on one cultivation rope 5 by substituting the length of the cultivation rope 5 into the cultivation rope length in formula (1) of the JBE manual, and substituting the wet weight per unit length, which is obtained by dividing the wet weight estimated in step S3 by the length of the cultivation rope 5, into the wet weight per unit rope length.

[0021] (Effects) As described above, the first embodiment allows for easy and highly accurate estimation of the wet weight of seaweed. Since the wet weight can be accurately determined while the device is installed in the sea, the kelp can be released after growth, allowing the seaweed, which has absorbed CO2, to sink into the deep sea. There is no need to operate an unmanned aerial vehicle or navigate a ship, and there is no need to consider the CO2 emissions from ships, etc., that occur during the measurement work.

[0022] <Second Embodiment> In the flowchart of Figure 6, a method for estimating the wet weight of kelp from the photon energy density measured at the stage when the kelp has grown was described. However, time history data of the photon energy density may be accumulated, and the wet weight of the kelp may be estimated based on the accumulated data. This process will be explained with reference to Figures 7 to 9. Figure 7 is a second flowchart showing an example of the weight estimation process according to the second embodiment. Time history data of the photon energy density is acquired (step S11). The sensor value acquisition unit 11 acquires the photon energy density measured by multiple photon energy meters 8 periodically or irregularly over the kelp cultivation period, and records the acquired photon energy density in the storage unit 14 for each photon energy meter 8, corresponding to the time of acquisition. Figure 8 shows an example of time history data of the photon energy density. In Figure 8, the vertical axis is the photon energy density, and the horizontal axis is the cultivation period. For example, graph 71 is time history data of the photon energy density measured by a photon energy meter 8 deployed at a water depth of 4 m. For example, Graph 72 shows the time history data of photon density measured by photonometer 8 deployed at a depth of 8m. For example, Graph 73 shows the time history data of photon density measured by photonometer 8 deployed at a depth of 12m.

[0023] Next, weight characteristic data and leaf number characteristic data are created (step S12). For example, the weight estimation unit 12 calculates the average value and integrated value of the photon flux density for each water depth from the time history data of step S11. Then, the weight estimation unit 12 analyzes the relationship between the number of leaves per plant at each water depth measured from kelp harvested during its most vigorous growth period and the average value of the photon flux density at the same water depth, and creates leaf number characteristic data (for example, graph 81 in Figure 9). The weight estimation unit 12 also analyzes the relationship between the wet weight per leaf at each water depth measured from kelp harvested during its most vigorous growth period and the average value of the photon flux density at the same water depth, and creates weight characteristic data (for example, graph 82 in Figure 9). Similarly, the weight estimation unit 12 may analyze the relationship between the number of leaves per plant, the wet weight per leaf, and the integrated value of the photon flux density, and create weight characteristic data and leaf number characteristic data. The weight estimation unit 12 records the weight characteristic data and leaf number characteristic data in the storage unit 14.

[0024] Next, the weight estimation unit 12 estimates the wet weight (step S13). For example, the weight estimation unit 12 calculates the average value of the photon flux density during the cultivation period from the time history data 71 at a water depth of 4 m, calculates the wet weight per kelp leaf at a water depth of 4 m from the calculated average value and weight characteristic data (graph 82 in Figure 9), and calculates the number of leaves per kelp plant at a water depth of 4 m based on the average value of the photon flux density during the cultivation period and the leaf number characteristic data (graph 81 in Figure 9). Then, the weight estimation unit 12 multiplies these values ​​to estimate the wet weight of the kelp cultivated at a water depth of 4 m. The same procedure is followed for kelp at other water depths. Alternatively, the weight estimation unit 12 may calculate the cumulative value of the photon flux density for each water depth from the time history data 71 to 73, and estimate the wet weight of the kelp from the cumulative value, weight characteristic data and leaf number characteristic data.

[0025] Next, the CO2 absorption amount calculation unit 13 calculates the amount of CO2 absorbed (step S14). The CO2 absorption amount calculation unit 13 calculates the amount of CO2 absorbed from formula (1) in the JBE manual and the wet weight of the kelp estimated in step S13.

[0026] (Effects) According to the second embodiment, the same effects as those of the first embodiment can be obtained. Since the wet weight of kelp is estimated based on the average value or cumulative value of the photon flux density measured over the cultivation period, it becomes possible to make an estimate with less error (not affected by noise).

[0027] <Third Embodiment> The relationship between photon flux density and the number of leaves per plant or the weight per leaf is not uniform. These relationships also fluctuate depending on the cultivation area. Therefore, a photon flux density meter 8 is installed at each cultivation site to acquire time-history data of photon flux density during the cultivation period, and leaf number characteristic data and weight characteristic data are created for each region. The graph in Figure 10 shows the time-history data of the photon flux density for each region. For example, the weight estimation unit 12 calculates the average value and cumulative value of the photon flux density for each region from the time-history data in Figure 10. The weight estimation unit 12 then analyzes the relationship between the number of leaves per plant and the weight per leaf, measured from kelp harvested during the most growth period in each region, and the average value and cumulative value of the photon flux density for the same region, and creates leaf number characteristic data and weight characteristic data (Figure 11). The weight estimation unit 12 records the weight characteristic data and leaf number characteristic data in the storage unit 14. By preparing weight characteristic data and leaf number characteristic data for each region, the wet weight of kelp for each region can be estimated by the same process as the flowchart in Figure 7 described in the second embodiment.

[0028] (Effects) According to the third embodiment, in addition to the same effects as the second embodiment, it is possible to estimate the wet weight of kelp for each region.

[0029] Figure 12 shows an example of the hardware configuration of the estimation device according to each embodiment. The computer 900 includes a CPU 901, main memory 902, auxiliary storage 903, input / output interface 904, and communication interface 905. The estimation device 10 described above is implemented in the computer 900. The functions described above are stored in the auxiliary storage 903 in the form of a program. The CPU 901 reads the program from the auxiliary storage 903, expands it in the main memory 902, and executes the above processing according to the program. The CPU 901 allocates a storage area in the main memory 902 according to the program. The CPU 901 allocates a storage area in the auxiliary storage 903 to store the data being processed according to the program.

[0030] A program for realizing all or part of the functions of the estimation device 10 may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be loaded into a computer system and executed to perform processing by each functional unit. Here, "computer system" includes hardware such as the OS and peripheral devices. If a WWW system is used, "computer system" also includes the homepage provisioning environment (or display environment). "Computer-readable recording medium" refers to portable media such as CDs, DVDs, USBs, and storage devices such as hard disks built into the computer system. If this program is distributed to the computer 900 via a communication line, the computer 900 that receives the distribution may load the program into the main memory 902 and execute the above processing. The above program may be for realizing part of the functions described above, and may also be for realizing the above functions in combination with a program already recorded in the computer system.

[0031] As described above, several embodiments relating to this disclosure have been explained, but all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be carried out in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents.

[0032] <Note> The estimation method and estimation apparatus described in each embodiment can be understood, for example, as follows.

[0033] (1) The estimation method according to the first embodiment is a method for estimating the growth index of an object that grows in water by photosynthesis, comprising the steps of: measuring the photon density near the object during or after growth; and estimating the growth index of the object during or after growth based on the measured photon density and information showing the relationship between the photon density and the growth index. This makes it possible to calculate the wet weight of seaweed easily and with high accuracy.

[0034] (2) The estimation method according to the second embodiment is the estimation method of (1), further comprising the step of analyzing the relationship between the photon quantum density and the growth index during or after the growth of the object, wherein in the step of estimating the growth index, information showing the relationship between the photon quantum density and the growth index is generated. This makes it possible to estimate the growth index from the photon quantum density.

[0035] (3) The estimation method relating to the third embodiment is the estimation method of (1) to (2), wherein in the step of measuring the photon quantum density, the photon quantum density near the object is measured at multiple timings in which the object is growing, and in the step of estimating the growth index, the growth index of the object is estimated based on the average value or cumulative value of the photon quantum density near the object at the multiple timings. By estimating the growth index based on the time-series photon quantum density, the growth index can be estimated with higher accuracy.

[0036] (4) The estimation method according to the fourth embodiment is the estimation method of (1) to (3), wherein in the step of measuring the photon quantum density, the photon quantum density near the object is measured at multiple water depth locations, and in the step of estimating the growth index, the growth index of the object is estimated based on the photon quantum density near the object at the multiple water depth locations. This makes it possible to estimate a growth index corresponding to the photon quantum density at each water depth.

[0037] (5) The estimation method relating to the fifth aspect is the estimation method of (1) to (4), wherein in the step of measuring the photon quantum density, the photon quantum density near the object is measured in multiple regions, and in the step of estimating the growth index, the growth index of the object is estimated for each region based on the photon quantum density near the object in the multiple regions. By estimating the growth index of the object for each region, the growth index of the object can be estimated with high accuracy.

[0038] (6) The estimation method relating to the sixth aspect is the estimation method of (1) to (5), wherein the object is seaweed and the growth indicator is the weight of the seaweed. This makes it possible to estimate the wet weight of the seaweed.

[0039] (7) The estimation method relating to the seventh aspect is the estimation method of (1) to (6), wherein the object is kelp, and the growth indicators are the weight per leaf of the kelp and the number of leaves per plant of the kelp. This makes it possible to estimate the wet weight of the kelp.

[0040] (8) The estimation method according to the eighth aspect is the estimation method according to (1) to (7), further comprising the step of estimating the amount of CO2 absorbed by the object based on the growth index of the object obtained in the step of estimating the growth index. This makes it possible to calculate the amount of CO2 absorbed by the object.

[0041] (9) An estimation device according to the ninth embodiment is an estimation device for estimating a growth index of an object that grows in water by photosynthesis, comprising: an acquisition unit that acquires measured values ​​of the photon quantum density near the object during or after growth; and an estimation unit that estimates the growth index of the object during or after growth based on the measured photon quantum density and information showing the relationship between the photon quantum density and the growth index.

[0042] According to the estimation method and apparatus described above, the wet weight of seaweed can be calculated easily and with high accuracy.

[0043] 1... Float ball 2... Float ball rope 3... Main rope 4... Mooring rope 5... Cultivation rope 6, 9... Weight 7... Stock 8... Quantum light meter 10... Estimation device 11... Sensor value acquisition unit 12... Weight estimation unit 13... CO2 absorption amount calculation unit 14... Memory unit 100... Estimation system 900... Computer 901... CPU 902... Main memory 903... Auxiliary memory 904... Input / output interface 905... Communication interface

Claims

1. A method for estimating a growth indicator for an object that grows in water by photosynthesis, comprising: a step of measuring the photon density near the object during or after growth; and a step of estimating the growth indicator for the object during or after growth based on the measured photon density and information showing the relationship between the photon density and the growth indicator.

2. The estimation method according to claim 1, further comprising the step of analyzing the relationship between the photon quantum density and the growth index during or after the growth of the object, wherein the step of estimating the growth index generates information showing the relationship between the photon quantum density and the growth index.

3. The estimation method according to claim 1, wherein in the step of measuring the photon quantum density, the photon quantum density near the object is measured at multiple timings in which the object is growing, and in the step of estimating the growth index, the growth index of the object is estimated based on the average value or cumulative value of the photon quantum density near the object at the multiple timings.

4. The estimation method according to claim 1, wherein in the step of measuring the photon quantum density, the photon quantum density near the object is measured at multiple water depth locations, and in the step of estimating the growth index, the growth index of the object is estimated based on the photon quantum density near the object at the multiple water depth locations.

5. The estimation method according to claim 1, wherein in the step of measuring the photon quantum density, the photon quantum density near the object is measured in multiple regions, and in the step of estimating the growth index, the growth index of the object is estimated for each region based on the photon quantum density near the object in the multiple regions.

6. The estimation method according to claim 1, wherein the object is seaweed and the growth indicator is the weight of the seaweed.

7. The estimation method according to claim 1, wherein the object is kelp, and the growth indicators are the weight per leaf of the kelp and the number of leaves per plant of the kelp.

8. The estimation method according to any one of claims 1 to 7, further comprising the step of estimating the amount of CO2 absorbed by the object based on the growth index of the object obtained in the step of estimating the growth index.

9. An estimation device for estimating a growth indicator of an object growing in water by photosynthesis, comprising: an acquisition unit that acquires measured values ​​of the photon flux density near the object during or after growth; and an estimation unit that estimates the growth indicator of the object during or after growth based on the acquired photon flux density and information showing the relationship between the photon flux density and the growth indicator.

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