Estimation method and estimation apparatus

Abstract

This invention provides a method for easily and accurately calculating the wet weight of seaweed. [Solution] The estimation method 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.

Description

Technical Field

[0001] The present disclosure relates to a method and an apparatus for estimating the wet weight of seaweed.

Background Art

[0002] Among the efforts towards decarbonization, blue carbon has attracted attention. Blue carbon refers to "carbon that is taken up by marine organisms from the atmosphere and absorbed and stored in marine ecosystems such as seaweeds, mangroves, and saline wetlands". Blue carbon has the following characteristics compared to green carbon (carbon taken up by terrestrial organisms such as trees and grass). (1) High CO2 absorption capacity in the atmosphere (storing carbon at a rate up to about 40 times faster). (2) High persistence of carbon storage (while the storage period of green carbon is several decades, blue carbon is several hundred to several thousand years). In Japan, JBE (Japan Blue Economy Association) issues blue carbon credits (J Blue Credits).

[0003] In the JBE guidebook, regarding the method for calculating the CO2 absorption amount by blue carbon, it is described that the CO2 absorption amount of the aquaculture facility can be calculated by the following formula (1). CO2 absorption amount = aquaculture facility area (or aquaculture rope length) × wet weight per unit area (or rope length) × (1 - moisture content) × carbon content × 44 / 12 × P / B ratio × survival rate... (1) In equation (1), sample analysis values ​​or literature values ​​are applied to the moisture content and carbon content, and literature values ​​are applied to the P / B ratio and residual rate. Therefore, in order to accurately calculate the amount of CO2 absorbed, it is important to accurately determine the wet weight of cultivated kelp and other seaweed. Currently, the only way to measure the wet weight is to harvest all (or representative) 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. Harvesting kelp and other seaweed requires a boat, and the amount of CO2 emitted by the boat must be deducted from the amount of CO2 absorbed by the cultivated kelp as CO2 emissions. Once harvested, the seaweed is treated as industrial waste and cannot be released into the sea (if it cannot be released into the sea, it cannot be counted as CO2 absorbed), among other issues.

[0004] In the case of seagrass and seaweed beds, one method involves taking aerial photographs of the target area using unmanned aerial vehicles (UAVs), determining the cover from the images, and then estimating the wet weight using the relationship between cover and wet weight described in the JBE manual. However, the JBE does not approve of applying this method to the wet weight of seaweed in aquaculture facilities. Furthermore, even if this method were applicable, it might not account for the cover of cultivated kelp in the depth direction, leading to inaccuracies in wet weight calculation. There are also challenges, such as the need for someone who can operate the UAV. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] J-Blue Credit (Registered Trademark) Certification Application Guide Ver. 2.4, [online], March 2024, Japan Blue Economy Technology Research Association, [Accessed September 17, 2024], Internet <https: / / www.blueeconomy.jp / wp-content / uploads / jbc2024 / 20240312_J-BlueCredit_Guidline_v.2.4.pdf> [Overview of the project] [Problems that the invention aims to solve]

[0006] There is a need for an easy and highly accurate method for calculating the wet weight of seaweed.

[0007] This disclosure provides an estimation method and an estimation apparatus that can solve the above-mentioned problems. [Means for solving the problem]

[0008] The estimation method of this disclosure is a method for estimating a 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.

[0009] The estimation device of this disclosure is an estimation device for estimating a growth index of an object that grows in water by photosynthesis, and comprises: 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. [Effects of the Invention]

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

[0011] [Figure 1] These are schematic diagrams of the aquaculture facilities according to each embodiment. [Figure 2] Block diagrams showing examples of estimation systems according to each embodiment. [Figure 3] This figure shows an example of the weight characteristics per kelp sheet in the direction of water depth according to the first embodiment. [Figure 4] This figure shows an example of the characteristics of the number of leaves per kelp plant in the direction of water depth according to the first embodiment. [Figure 5]This figure shows an example of a graph illustrating the relationship between photon quantum density, wet weight of kelp, and number of leaves according to the first embodiment. [Figure 6] This is a flowchart showing an example of the weight estimation process according to the first embodiment. [Figure 7] This is a second flowchart showing an example of the weight estimation process according to the second embodiment. [Figure 8] This figure shows an example of time-history data of the photon quantum density according to the second embodiment. [Figure 9] This figure shows an example of a graph illustrating the relationship between the average or cumulative value of the photon flux density, the wet weight of the kelp, and the number of leaves according to the second embodiment. [Figure 10] This figure shows an example of time-history data of the photon quantum density according to the third embodiment. [Figure 11] This figure shows an example of a graph illustrating the relationship between the average or cumulative value of the photon flux density, the wet weight of the kelp, and the number of leaves according to the third embodiment. [Figure 12] This figure shows an example of the hardware configuration of the estimation device according to each embodiment. [Modes for carrying out the invention]

[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, and the method for estimating the wet weight of cultivated kelp will be described below. 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 throughout 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 several buoys 1 floating on the ocean surface via a buoy rope 2, and is suspended from the sea surface side. Multiple cultivation ropes 5, each with a weight 6 attached to its lower end, are connected to the main rope 3. The cultivation ropes 5, also called curtain ropes, extend vertically downwards. Kelp is grown attached to the cultivation ropes 5. For example, the kelp may be cultivated for carbon sequestration. In the cultivation area exemplified in Figure 1, kelp is grown to absorb CO2, and then the kelp is released and sunk deep into the seabed to store CO2. The stored CO2 is counted as a reduction in greenhouse gas emissions and certified as a blue credit. 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 quantum 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 with the multiple photometers 8. The photon meter 8 is positioned near the location where kelp is attached to the cultivation net 5. For example, the photon meter 8 may be attached to a buoy and adjusted with weights or the like to fix it at a predetermined water depth. Alternatively, the photon meter 8 may be moored to the cultivation net 5 or the seabed. The amount of photons measured by multiple photon meters 8 is input to the estimation device 10 on land.The plurality of optical quantum meters 8 and the estimation device 10 may be communicably connected by wireless or wired communication means. Or, a communication machine (not shown) may be connected to the plurality of optical quantum meters 8, and the measured values of each optical quantum meter 8 may be collected by the communication machine and transmitted from this communication machine to the estimation device 10. For example, a wireless device such as LPWA (Low Power Wide Area) driven by a battery (which may be driven by a solar cell or a wave power generation device) is attached to a buoy on which the optical quantum meter 8 is attached, and the optical quantum density measured by the plurality of optical quantum meters 8 is transferred to the ground estimation device 10. For example, data may be transferred several times during the day using a timer. Alternatively, an inspector may organize the optical quantum density measured by each optical quantum meter 8 for each optical quantum meter 8, and the inspector may input the measured value for each optical quantum meter 8 into the estimation device 10. The optical quantum meter 8 measures the optical quantum density at the same location at least twice a month, for example, starting from the start of kelp cultivation. The estimation device 10 estimates the wet weight per kelp leaf for each water depth and the number of leaves per strain based on the optical quantum density measured by the optical quantum meter 8, and estimates the wet weight of kelp for each cultivation rope 5. In FIG. 1, six optical quantum meters 8 are arranged along the fifth cultivation rope 5 from the left, but the number and arrangement positions of the optical quantum meters 8 are not limited to this. For example, it is desirable that the optical quantum meters 8 be arranged at four or more points at intervals of 2 m in the water depth direction.

[0013] FIG. 2 shows an example of an estimation system. The estimation system 100 includes a plurality of optical quantum meters 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 (optical quantum density) of the plurality of optical quantum meters 8 and records the measured values in the storage unit 14 for each optical quantum meter 8. The weight estimation unit 12 estimates the wet weight of kelp based on the optical quantum density acquired by the sensor value acquisition unit 11 and a table, function, etc. showing the relationship between the optical quantum density and the growth index of kelp such as kelp. The CO2 absorption amount calculation unit 13 calculates the CO2 absorption amount by kelp according to the formula (1) described in the JBF manual based on the wet weight of kelp estimated by the weight estimation unit 12. The memory unit 14 stores the measured values acquired by the sensor value acquisition unit 11, tables, functions, etc. that indicate the relationship between the light quantum density and the growth index of kelp.

[0014] Figure 3 is a diagram showing an example of the weight characteristics per kelp leaf in the water depth direction. The inspector harvests the kelp near each quantum light meter 8 deployed in the water depth direction at the stage when the kelp has grown the most, and measures the wet weight per leaf of the kelp. Then, the relationship between the measured wet weight and the light quantum density measured by the nearby quantum light meter 8 is organized for each water depth, and the graph in Figure 3 is created. The left vertical axis in Figure 3 represents the wet weight of the kelp, the right vertical axis represents the light quantum density, and the horizontal axis represents the water depth. As shown in Figure 3, there is a correlation between the light quantum density and the wet weight. As the water depth increases, the light quantum density decreases, and the wet weight per leaf of the kelp also decreases.

[0015] Figure 4 is a diagram showing an example of the number of leaves per kelp plant in the water depth direction. The inspector harvests the kelp near each quantum light meter 8 deployed in the water depth direction at the stage when the kelp has grown the most, and counts the number of leaves per kelp plant. Then, the relationship between the counted number of leaves and the light quantum density measured by the nearby quantum light meter 8 is organized for each water depth, and the graph in Figure 4 is created. The left vertical axis in Figure 4 represents the number of leaves per kelp plant, the right vertical axis represents the light quantum density, and the horizontal axis represents the water depth. As shown in Figure 4, there is a correlation between the light quantum density and the number of leaves. As the water depth increases, the light quantum density decreases, and the number of leaves per leaf 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, we will explain the flow of the weight estimation process 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 photon quantum density and the weight per kelp leaf based on the graph in Figure 3 and creates weight characteristic data (Figure 5). The weight estimation unit 12 also analyzes the relationship between photon quantum 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, there are 6 kelp clumps attached to the cultivation line 5. For the bottom clump 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 clump 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 also estimates the number of leaves contained in the kelp clump 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 bottom clump 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] (effect) 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. Furthermore, 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> The flowchart in Figure 6 illustrates a method for estimating the wet weight of kelp from the photon flux density measured at the growth stage. However, it is also possible to accumulate time-history data of the photon flux density and estimate the wet weight of the kelp based on the accumulated data. Refer to Figures 7 to 9 to explain this process. Figure 7 is a second flowchart showing an example of the weight estimation process according to the second embodiment. Time history data of 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 for each photon energy meter 8 in the storage unit 14, corresponding to the time of acquisition. Figure 8 shows an example of time history data of photon energy density. In Figure 8, the vertical axis is photon energy density, and the horizontal axis is the cultivation period. For example, graph 71 is time history data of photon energy density measured by a photon energy meter 8 deployed at a water depth of 4 m. For example, graph 72 is time history data of photon energy density measured by a photon energy meter 8 deployed at a water depth of 8 m. For example, graph 73 is time history data of photon energy density measured by a photon energy meter 8 deployed at a water depth of 12 m.

[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 equation (1) in the JBE manual and the wet weight of the kelp estimated in step S13.

[0026] (effect) 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 estimates 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 vary 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. Then, the weight estimation unit 12 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] (effect) 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 memory 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 memory 903 in the form of a program. The CPU 901 reads the program from the auxiliary memory 903, expands it in the main memory 902, and executes the above processing according to the program. The CPU 901 also allocates a storage area in the main memory 902 according to the program. The CPU 901 also allocates a storage area in the auxiliary memory 903 to store the data being processed according to the program.

[0030] A program to implement 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. Furthermore, if a WWW system is used, "computer system" also includes the homepage provisioning environment (or display environment). Furthermore, "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. Furthermore, if this program is distributed to computer 900 via a communication line, computer 900 that receives the program may load it into main memory 902 and execute the above processing. Furthermore, the above program may be for implementing only a part of the functions described above, and may also be able to implement the above functions in combination with programs 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 a 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 easily and accurately calculate the wet weight of seaweed.

[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 will make it possible to estimate growth indicators from the photon quantum density.

[0035] (3) The estimation method relating to the third aspect 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 integrated value of the photon quantum density near the object at the multiple timings. By estimating growth indicators based on time-series photon density, it is possible to estimate growth indicators with higher accuracy.

[0036] (4) The estimation method relating to the fourth aspect is the estimation method of (1) to (3), wherein in the step of measuring the photon density, the photon 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 density near the object at the multiple water depth locations. This makes it possible to estimate growth indicators based on the photon quantum density at different water depths.

[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 indicators of the target object by region, it is possible to estimate the growth indicators of the target object 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 allows us to estimate the wet weight of 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 allows us to estimate the wet weight of the kelp.

[0040] (8) The estimation method relating to the eighth aspect is the estimation method of (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 allows us to calculate the amount of CO2 absorbed by the object in question.

[0041] (9) An estimation device according to the ninth aspect 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. [Explanation of Symbols]

[0042] 1... floating ball 2...Floating ball rope 3. Main rope 4...Mooring line 5... Training rope 6, 9... weights 7... shares 8. Quantum light meter 10... Estimation device 11. Sensor value acquisition unit 12...Weight estimation section 13. CO2 absorption calculation unit 14...Storage section 100... Estimation System 900... Computer 901···CPU 902...Main memory 903...Auxiliary storage device 904... Input / Output Interface 905...Communication Interface

Claims

1. A method for estimating growth indicators for objects that grow in water through photosynthesis, A step of measuring the photon density near the object during or after growth, A step of estimating 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, An estimation method having

2. The method further comprises the step of analyzing the relationship between the photon quantum density and the growth indicator during or after the growth of the object, In the step of estimating the growth indicator, information showing the relationship between the photon quantum density and the growth indicator is generated. The estimation method according to claim 1.

3. In the step of measuring the photon quantum density, the photon quantum density near the object is measured at multiple timings during the growth of the object. In the step of estimating the growth indicator, the growth indicator of the object is estimated based on the average or integrated value of the photon quantum density near the object at the multiple timings. The estimation method according to claim 1.

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

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

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

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

8. A step to estimate 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, It further possesses, The estimation method according to any one of claims 1 to 7.

9. An estimation device for estimating growth indicators of objects that grow in water through photosynthesis, An acquisition unit that acquires measured values ​​of the photon quantum density near the object during or after growth, An estimation unit that estimates the growth index of the object during or after growth based on the acquired photon quantum density and information showing the relationship between the photon quantum density and the growth index, An estimation device having the following features.

Images