Method and program for estimating the weight of algae or aquatic plants

Abstract

This invention provides a method and program for estimating the weight of algae or aquatic plants. [Solution] The method for estimating the weight of algae or aquatic plants includes a measurement step (S1) of measuring a measurement value indicating a predetermined state of the substrate 10, for example, acceleration, and an estimation step (S2) of estimating the weight of the algae P based on the measurement value. In the estimation step (S2), the computer 30 outputs the weight of the algae P attached to the substrate 10 by inputting the measurement value into an estimation model 34. The estimation model 34 is data generated by machine learning using a combination of measurement values ​​indicating a predetermined state of the substrate 10 and the weight of the algae P as training data.

Description

[Technical Field]

[0001] The present disclosure relates to an algal weight estimation method and a weight estimation program for estimating the weight of algae or aquatic plants attached to substrates suspended in water. [Background Art]

[0002] In recent years, utilization of blue carbon (BC: Blue Carbon) has been expected to achieve carbon neutrality, which makes greenhouse gas emissions substantially zero. Blue carbon is carbon derived from CO2 (carbon dioxide) captured from the atmosphere into the sea by marine organisms, and refers to carbon absorbed or stored in marine ecosystems such as seaweeds, coral reefs and mangroves.

[0003] Blue carbon can be quantified and handled as tradable credits. Measurement of blue carbon is performed, for example, by having divers dive at each measurement point to visually investigate the condition of algal beds and measure the biomass of algae such as seaweed. Dive surveys conducted by divers may be carried out, for example, in sea areas ranging from several tens to thousands of hectares (ha).

[0004] However, dive surveys conducted by divers have a problem that the working time and workload required for blue carbon measurement increase. For example, when measuring blue carbon, dive surveys by divers require enormous working time and workload, leading to increased costs.

[0005] Japanese Patent Publication No. 2003-128199 (Patent Document 1) discloses an information processing method that can reduce the variability in the accuracy of estimating the length and weight of aquatic plants. This information processing method includes the steps of: acquiring an image of aquatic plants living in water, taken from above; distance information indicating the distance from the position where the image was taken to the bottom of the water; and flow velocity information indicating the water flow velocity when the image was taken; estimating the length of the aquatic plants based on the shape of the aquatic plants included in the image, the distance information, and the flow velocity information; estimating the weight of the aquatic plants based on the estimated length of the aquatic plants and the density of the aquatic plants included in the image; and outputting the estimated length of the aquatic plants and the estimated weight of the aquatic plants. This reduces the variability in the accuracy of estimating the length and weight of aquatic plants such as seagrass and seaweed that act as CO2 absorption sources.

[0006] Japanese Patent Publication No. 2024-71116 (Patent Document 2) discloses a CO2 absorption amount evaluation system capable of accurately evaluating the amount of CO2 absorbed by seaweed in a seaweed bed under evaluation. The CO2 absorption amount evaluation system comprises a camera, a LiDAR, a mobile body to which the camera and LiDAR are attached and which is configured to move underwater, and a CO2 absorption amount evaluation device. The CO2 absorption amount evaluation device has a measurement unit that measures the frontal area of ​​each seaweed plant in the seaweed bed from images taken underwater by the camera, and measures the depth of each seaweed plant from the results of scanning the underwater with the LiDAR, and a calculation unit that calculates the weight of each seaweed plant based on the measurement results from the measurement unit, and calculates the amount of CO2 absorbed by seaweed in the seaweed bed based on the calculated weight of each seaweed plant and the number of seaweed plants in the seaweed bed. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2003-128199 [Patent Document 2] Japanese Patent Publication No. 2024-71116 [Overview of the project] [Problems that the invention aims to solve]

[0008] The object of this disclosure is to provide a method and program for estimating the weight of algae or aquatic plants in water, which can easily estimate the weight of algae or aquatic plants in water. [Means for solving the problem]

[0009] The present disclosure is a method for estimating the weight of algae or aquatic plants, comprising: a measurement step of measuring a measurement value indicating a predetermined state of a substrate floating in water with algae or aquatic plants attached to it; and an estimation step of estimating the weight of the algae or aquatic plants based on the measurement value.

[0010] The algae or aquatic plant weight estimation program is a program for estimating the weight of algae or aquatic plants, which causes a computer to perform a measurement step of measuring a value that indicates a predetermined state of a substrate floating in water with algae or aquatic plants attached to it, and an estimation step of estimating the weight of the algae or aquatic plants based on the measurement value. [Effects of the Invention]

[0011] According to this disclosure, the weight of algae or aquatic plants in water can be easily estimated. [Brief explanation of the drawing]

[0012] [Figure 1] Figure 1 shows the configuration of the weight estimation system for algae or aquatic plants in the first embodiment. [Figure 2] Figure 2 is a table showing the correspondence between the acceleration of the substrate and the weight of algae or aquatic plants. [Figure 3] Figure 3 is a flowchart of the method for estimating the weight of algae or aquatic plants in this embodiment. [Figure 4]FIG. 4 is a diagram showing the configuration of a weight estimation system for algae or aquatic plants according to a second embodiment. [Figure 5] FIG. 5 is a diagram showing the state of a substrate according to Modification 1. [Figure 6] FIG. 6 is a diagram showing the state of a substrate according to Modification 2. [Figure 7] FIG. 7 is a diagram showing the state of a substrate according to Modification 3. [Figure 8] FIG. 8 is a diagram showing the state of a substrate according to Modification 4. [Figure 9] FIG. 9 is a diagram showing the state of a substrate according to Modification 5. [Figure 10] FIG. 10 is a diagram showing the state of a substrate according to Modification 5. [Figure 11] FIG. 11 is a diagram showing the state of a substrate according to Modification 5. MODE FOR CARRYING OUT THE INVENTION

[0013] Blue carbon can normally be sufficiently estimated by measuring the weight of algae or aquatic plants. In the present disclosure, "algae or aquatic plants" may be collectively referred to as "algae, etc."

[0014] However, the information processing method of Patent Document 1 estimates the length of an aquatic plant based on the shape and distance information of the aquatic plant included in a captured image and flow velocity information, and further estimates the weight of the aquatic plant based on the estimated length of the aquatic plant and the density of the aquatic plant included in the captured image. That is, in the information processing method of Patent Document 1, it takes time and effort such as imaging the aquatic plant in order to estimate the weight of the aquatic plant.

[0015] Further, the CO₂ absorption amount evaluation system of Patent Document 2 measures the front area of seaweed per individual in a seaweed bed from an image of underwater captured by a camera, measures the depth of seaweed per individual from the result of underwater scanning by LiDAR, and calculates the weight of seaweed per individual based on the measurement results. Similar to the information processing method of Patent Document 1, the CO₂ absorption amount evaluation system of Patent Document 2 also requires time and effort to calculate the weight of seaweed.

[0016] The inventors of the present invention have intensively studied to easily estimate the weight of algae in water, and as a result, completed the weight estimation method and weight estimation program for algae and the like of the present disclosure.

[0017] (Configuration 1) A method for estimating the weight of algae or aquatic plants according to an embodiment of the present disclosure, which estimates the weight of algae or aquatic plants, comprises: a measurement step of measuring a measurement value indicating a predetermined state of a substrate floating in water with algae or aquatic plants attached thereto; and an estimation step of estimating the weight of the algae or aquatic plants based on the measurement value.

[0018] This enables easy estimation of the weight of algae and the like in water.

[0019] (Configuration 2) In the weight estimation method for algae or aquatic plants according to Configuration 1, the measurement value indicating the predetermined state of the substrate measured in the measurement step may be at least one of acceleration of the substrate, a movement amount of the substrate per unit time, and a water level from the substrate to a water surface. This enables easy estimation of the weight of algae and the like in water.

[0020] (Configuration 3) In the weight estimation method for algae or aquatic plants according to Configuration 1 or 2, in the estimation step, the weight of the algae or aquatic plants may be estimated from a weight of the algae or aquatic plants that is predetermined in correspondence with the measurement value indicating the predetermined state of the substrate. This enables easy estimation of the weight of algae and the like in water.

[0021] (Configuration 4) A method for estimating the weight of algae or aquatic plants according to any one of configurations 1 to 3, the estimation step may include: a step in which a computer generates an estimation model by machine learning using combinations of measured values ​​indicating a predetermined state of the substrate and the weight of the algae as training data; and a step in which the weight of algae or aquatic plants attached to the substrate is output by inputting the measured values ​​into the estimation model. This makes it possible to estimate the weight of algae and the like in water more easily.

[0022] (Composition 5) The algae or aquatic plant weight estimation program in the embodiments of this disclosure is an algae or aquatic plant weight estimation program that estimates the weight of algae or aquatic plants, and causes a computer to perform a measurement step of measuring a value indicating a predetermined state of a substrate floating in water with algae or aquatic plants attached to it, and an estimation step of estimating the weight of algae or aquatic plants based on the measurement value. This makes it possible to easily estimate the weight of algae and the like in water.

[0023] (Composition 6) The program for estimating the weight of algae or aquatic organisms according to configuration 5 may include the following steps: generating an estimation model by machine learning using a combination of measured values ​​indicating a predetermined state of the substrate and the weight of algae or aquatic plants as training data, and outputting the weight of algae or aquatic plants attached to the substrate by inputting the measured values ​​into the estimation model. This makes it possible to estimate the weight of algae and the like in water more easily.

[0024] [Embodiment] The embodiments will be described below with reference to the drawings. In the drawings, identical and corresponding components are denoted by the same reference numerals, and the same explanation will not be repeated. In order to make the explanation easier to understand, the components in the drawings referred to below are shown in a simplified or schematic form, and some components are omitted.

[0025] [First Embodiment] The method for estimating the weight of algae and the like according to the first embodiment will be specifically explained with reference to Figures 1 to 3. In the method for estimating the weight of algae and the like according to the first embodiment, the acceleration of the substrate 10 (change in velocity per unit time (m / s)) is used as an example of a measurement value that indicates a predetermined state of the substrate 10, which will be described later. 2 We will explain using )).

[0026] (Example of a system configuration for estimating the weight of algae, etc.) As shown in Figure 1, the algae and the like weight estimation system 100 includes a substrate 10 floating in water, an acceleration sensor 20 provided on the substrate 10, and a computer 30. The algae and the like, as described above, include algae or aquatic plants. Algae include, for example, seaweed such as wakame and kelp, and freshwater algae. Aquatic organisms include seagrass or aquatic plants. In this embodiment, as an example of algae and the like attached to the substrate 10, seaweed P such as wakame and kelp will be used for explanation.

[0027] The substrate 10 is not particularly limited as long as it has a shape that allows algae and other organisms to attach to it and float in water. Algae P are attached to the outer surface of the substrate 10. The substrate 10 is fixed to a float or the bottom of a body of water with a rope or the like, i.e., moored, and floats in the water. In this disclosure, the substrate 10 in water includes not only cases where the entire substrate 10 is submerged in water such as a river, pond, swamp, lake, or sea, but also cases where a part of the substrate 10 is exposed above the water surface and the rest of the substrate is submerged in water. In addition, the algae P attached to the substrate 10 usually grow larger and heavier over time.

[0028] The acceleration sensor 20 is installed on the substrate 10. The acceleration sensor 20 is attached, for example, to the bottom of the substrate 10. The acceleration sensor 20 is, for example, a 3-axis acceleration sensor. Normally, the substrate 10 oscillates up and down, back and forth, or left and right due to ocean currents, etc. The acceleration sensor 20 measures the acceleration of the substrate 10 when it oscillates. The acceleration of the substrate 10 changes depending on the weight of the algae P attached to the substrate 10. For example, if the size and weight of the algae P attached to the substrate 10 are small, the acceleration of the substrate 10 will be large. As the size and weight of the algae P attached to the substrate 10 increase, the acceleration of the substrate 10 gradually decreases. Thus, there is a correlation between the acceleration of the substrate 10 and the weight of the algae P.

[0029] The computer 30 includes a storage unit 31, a measurement value acquisition unit 32, and a weight estimation unit 33. The computer 30 has a processor and memory. There may be two or more computers 30. The algae estimation process described later can be realized by the processor executing a predetermined program. A program that causes the computer 30 to execute each process and a non-transitory recording medium on which the program is recorded are also included in the embodiments of this disclosure. The computer 30 may be communicably connected to the acceleration sensor 20 via a network, or it may be connected to the acceleration sensor 20 via wiring.

[0030] The memory unit 31 is, for example, a memory device built into the computer 30. As shown in Figure 2, the memory unit 31 stores a table T1 which associates, for example, measured values ​​indicating a predetermined state of the substrate 10 (in this embodiment, the acceleration of the substrate 10 (A1~An)) with the weight of the algae, etc. (W1~Wn).

[0031] The measurement value acquisition unit 32 acquires the acceleration of the substrate 10 measured by the acceleration sensor 20, for example, via a network, from the acceleration sensor 20.

[0032] The weight estimation unit 33 estimates the weight of the algae P attached to the substrate 10 based on the measured values ​​in table T1. For example, if the acceleration of the substrate 10 measured by the acceleration sensor 20 is A2, the weight of the algae P attached to the substrate 10 is estimated to be W2. Alternatively, the weight of the algae P may be estimated by calculating the weight of the algae P attached to the substrate 10 from measured values ​​of the substrate 10 in a predetermined state using a predetermined calculation formula.

[0033] The computer 30 outputs, for example, to a display unit (not shown) that the weight of the algae P attached to the substrate 10 is W2.

[0034] (Method for estimating the weight of algae) Next, the method for estimating the weight of algae or aquatic plants according to the first embodiment of this disclosure will be described in detail with reference to Figure 3.

[0035] (Measurement process S1) First, the acceleration of the substrate 10 is measured by an acceleration sensor 20 provided on the substrate 10. The measured value of the acceleration of the substrate 10 measured by the acceleration sensor 20 is transmitted to the measurement value acquisition unit 32 of the computer 30.

[0036] (Estimation process S2) Next, the weight estimation unit 33 determines the weight of the algae P based on the measured acceleration of the substrate 10 in the table T1 (see Figure 2) stored in the memory unit 31.

[0037] In this way, the weight of algae P can be easily estimated based on measured values ​​(acceleration) indicating a predetermined state of substrate 10. As described above, blue carbon can usually be determined based on the weight of algae P.

[0038] In this embodiment, the acceleration (m / s²) measured by the acceleration sensor is 2The weight of algae P was estimated using the above method, but the weight of algae P may also be estimated using other measurements measured by a (3-axis) acceleration sensor. For example, other measurements may include the amount of movement of substrate 10 per unit time or a 3-dimensional vector quantity.

[0039] For example, when using the amount of movement of the substrate 10 per unit time as the measurement value, the amount of movement of the substrate 10 can be defined as the distance the substrate 10 moves in the up, down, left, right, and diagonal directions per unit time (the unit time is not particularly limited, but may be, for example, 1 second, 10 seconds, or 1 minute). More specifically, the moored substrate 10 moves randomly in the up, down, left, right, and diagonal directions due to water currents, waves, etc. For example, if the unit time is 10 seconds, the substrate 10 moves upward, to the left, or downward during these 10 seconds due to water currents, waves, etc. The amount of movement of the substrate 10 per unit time can be defined as the total distance the substrate 10 moves during these 10 seconds. If the amount of movement of the substrate 10 per unit time is small, it can be estimated that the weight of the algae P is large, and if the amount of movement is large, it can be estimated that the weight of the algae P is small.

[0040] For example, when using a three-dimensional vector quantity as a measurement, the three-dimensional vector quantity can be expressed as displacement or acceleration, etc. For example, if the displacement of the substrate 10 in a predetermined direction is large, it can be considered that the algae P moved a large distance due to ocean currents, etc., because its weight is small, and the weight of the algae P can be estimated to be small. On the other hand, if the displacement of the substrate 10 in a predetermined direction is small, it can be considered that the algae P did not move a large distance due to ocean currents, etc., because its weight is large, and the weight of the algae P can be estimated to be small.

[0041] [Second Embodiment] Next, the method for estimating the weight of algae according to the second embodiment will be specifically explained using Figure 4. Note that, in principle, only the configurations that differ from the first embodiment will be described.

[0042] The weight estimation unit 33 can estimate the weight of algae P using the estimation model 34.

[0043] The estimation model 34 can be data generated by machine learning using a combination of measured values ​​indicating a predetermined state of the substrate 10 (in this embodiment, the acceleration of the substrate 10) and the measured weight of the algae P as training data. The generated estimation model 34 may be stored in the storage unit 31.

[0044] Machine learning may, for example, be deep learning using a neural network. In this case, the estimation model 34 may be, for example, a dataset that takes the acceleration of the substrate 10 as input and outputs the weight of the algae P attached to the substrate 10. The dataset may include, for example, parameters that represent the weights between layers of the neural network, and whose values ​​have been adjusted by machine learning. Note that machine learning is not limited to that using a neural network. For example, a trained model may be generated by machine learning using regression analysis or decision trees. Examples of such machine learning methods include linear regression, support vector machines, support vector regression, Elastic Net, logistic regression, and random forests.

[0045] (Estimation process S2) Referring to Figure 3, in estimation step S2, the computer 30 can generate an estimation model 34 by machine learning using a combination of measured values ​​indicating a predetermined state of the substrate 10 (in this embodiment, the acceleration of the substrate 10) and the measured weight of the algae P as training data. Estimation step S2 can output the weight of the algae P attached to the substrate 10 by inputting the acceleration of the substrate 10 into the estimation model 34. The estimation model 34 may also undergo reinforcement learning through feedback based on the estimated weight of the algae P.

[0046] In this way, according to the algae weight measurement method of the second embodiment, the weight of the algae P can be easily estimated based on a measurement value (acceleration) indicating a predetermined state of the substrate 10.

[0047] [Differentiation] In the first and second embodiments described above, the acceleration of the substrate 10 was used as a measurement of the substrate 10 in a predetermined state, but the measurement of the substrate 10 in a predetermined state is not limited to this. Various examples of measuring the substrate 10 in a predetermined state (modifications 1 to 5) will be described below. In addition, in the second embodiment, at least one of the measurement values ​​of the following various modifications may be used as training data. This makes it possible to generate an estimation model 34 more appropriately and output the weight of algae P attached to the substrate 10 with greater accuracy.

[0048] (Variation 1) As shown in Figure 5, the measured value of the substrate 10 in a predetermined state can be the water level WL from the substrate 10 to the water surface. The water level WL may be the distance from the top of the substrate 10 to the water surface. The water level WL changes depending on the weight of the algae P attached to the substrate 10. For example, if the size and weight of the algae P attached to the substrate 10 are small, the water level WL will be small. As the size and weight of the algae P attached to the substrate 10 increase, the water level WL will also gradually increase. Thus, there is a correlation between the water level WL of the substrate 10 and the weight of the algae P. However, the water level WL can change not only due to the weight of the algae P attached to the substrate 10, but also due to the flow velocity, waves, etc. Therefore, when using the water level WL as a measured value, machine learning may be used to remove noise such as flow velocity and waves using data such as the acceleration of the substrate 10.

[0049] The water level WL of the substrate 10 can be measured, for example, by a pressure-type water level gauge. Therefore, the reference value indicating the state of the substrate 10 may be the water pressure applied to the substrate 10. However, the method for measuring the water level WL of the substrate 10 is not limited to this. Electromagnetic waves may be emitted toward the substrate 10, and the water level WL may be calculated based on the time it takes for the electromagnetic waves to reflect and return.

[0050] (Modification 2) The measurement value for a predetermined state of the substrate 10 can be the tension generated between the float 40 floating on the water surface and the substrate 10, as shown in Figure 6. That is, a tension measuring device 41 is installed between the float 40 and the substrate 10. The float 40 and the tension measuring device 41, and the substrate 10 and the tension measuring device 41 are connected by a rope 42. This allows the tension generated between the float 40 floating on the water surface and the substrate 10 to be measured. For example, if the size and weight of the algae P are small, the tension will also be small. As the weight of the algae P increases, the water level WL (see Figure 5) increases, and the tension also increases. Thus, there is a correlation between tension and the weight of the algae P.

[0051] (Variation 3) As shown in Figure 7, the measurement of a predetermined state of the substrate 10 may be the time it takes for the ultrasonic waves emitted from the ultrasonic sensor 50 to return from the substrate 10. For example, if the size and weight of the algae P attached to the substrate 10 are small, the water level WL will be small and the ultrasonic reflection time will be short. As the size and weight of the algae P attached to the substrate 10 increase, the water level WL will also gradually increase. That is, the ultrasonic reflection time will be longer. Thus, there is a correlation between the ultrasonic reflection time and the weight of the algae P.

[0052] Furthermore, the measurement value for a predetermined state of the substrate 10 may also be the amount of ultrasonic waves reflected from the ultrasonic sensor 50. For example, as the weight of the algae P attached to the substrate 10 increases, that is, as the size of the algae P increases, or as the number of algae P increases, the amount of ultrasonic waves reflected increases. Thus, the measurement value for a predetermined state of the substrate 10 may also be the ultrasonic wave reflection time or the amount of reflection.

[0053] (Modification 4) The measured value of the substrate 10 in a predetermined state may be a value measured by a floating gauge 60, as shown in Figure 8. The floating gauge 60 is connected to the substrate 10 by a rope or the like. The floating gauge 60 has a scale section 61 and a body section 62. A pressure sensor 63 is provided on the scale section 61 of the floating gauge 60. The pressure sensor 63 senses the difference between the water pressure in the water and the atmospheric pressure above the water surface, and detects the boundary between the water and the water surface, i.e., the position of the water surface. When the weight of the algae P increases and the water level WL of the substrate 10 increases, or when the weight of the algae P decreases and the water level WL of the substrate 10 decreases, the value on the scale section 61 of the floating gauge 60 corresponding to the water surface moves up or down. This value on the scale section 61 may be used as the measured value.

[0054] (Variation 5) The measured value of the substrate 10 in a predetermined state may be the illuminance measured by a light meter, as shown in Figures 9 to 11. For example, as shown in Figure 9, if a light meter 70 is placed on top of the substrate 10, as the weight of the algae P increases, the substrate 10 sinks and less light from the water surface reaches it. Therefore, the measured illuminance decreases. As the weight of the algae P decreases, the substrate 10 gets closer to the water surface, and the measured illuminance increases.

[0055] Furthermore, as shown in Figure 10, when the illuminometer 70 is placed below the substrate 10, the illuminance decreases as the weight of the algae P attached to the substrate 10 increases, that is, as the size of the algae P increases or the number of algae P increases, the light from the water surface is blocked. On the other hand, the illuminance increases as the weight of the algae P attached to the substrate 10 decreases.

[0056] Furthermore, as shown in Figure 11, when the illuminometer 70 is placed above the water surface, as the weight of the algae P attached to the substrate 10 increases, that is, as the size of the algae P increases, or as the number of algae P increases, the amount of light reflected increases, and the illuminance measured by the illuminometer 70 increases. On the other hand, as the weight of the algae P attached to the substrate 10 decreases, the amount of light reflected decreases, and the illuminance also decreases.

[0057] The measured values ​​of the water pressure and electromagnetic wave reflection time in Modification 1, the tension in Modification 2, the ultrasonic wave reflection time in Modification 3, the scale value of the hydrometer 60 in Modification 4, and the illuminance in Modification 5 can all depend on the water level WL from the substrate 10 to the water surface in Modification 1 (see Figure 5). Therefore, in this disclosure, "water level WL from the substrate 10 to the water surface" as a "measured value" includes not only the distance from the substrate 10 to the water surface, but also measured values ​​such as the water pressure and electromagnetic wave reflection time in Modification 1, the tension in Modification 2, the ultrasonic wave reflection time in Modification 3, the scale value of the hydrometer 60 in Modification 4, and the illuminance in Modification 5.

[0058] Although embodiments of the present invention have been described above, the present invention is not limited to the above embodiments. Furthermore, the measured values ​​indicating a predetermined state of the substrate 10 or the weight data of the algae P may be corrected according to tides, current velocity, waves, etc. Alternatively, a substrate 10 without algae P attached may be prepared separately from the substrate 10 with algae P attached, and the measured values ​​of the substrate 10 with algae P attached may be corrected based on the measured values ​​of the substrate 10 without algae P attached. For example, the acceleration of the substrate 10 with algae P attached changes according to the current velocity, etc. Therefore, the weight of the algae P may be estimated by taking the difference between the measured values ​​of the substrate 10 without algae P attached and the measured values ​​of the substrate 10 without algae P attached as the "measured value". This can improve the accuracy of measuring the weight of algae, etc. Note that the "measured value" may be the average value of multiple values ​​measured at predetermined timings or time intervals in each of the above embodiments and modifications. [Explanation of symbols]

[0059] 10: Substrate, 20: Accelerometer, 30: Computer, 31: Memory unit, 32: Measurement acquisition unit, 33: Weight estimation unit, 34: Estimation model

Claims

1. A method for estimating the weight of algae or aquatic plants, A measurement step of measuring a measurement value that indicates a predetermined state of a substrate floating in water with the aforementioned algae or aquatic plants attached to it, A method for estimating the weight of an aquatic plant, comprising an estimation step of estimating the weight of the algae or aquatic plant based on the measured values.

2. A method for estimating the weight of algae or aquatic plants according to claim 1, A method for estimating the weight of algae or aquatic plants, wherein the measured value indicating a predetermined state of the substrate measured in the measurement step is at least one of the acceleration of the substrate, the amount of movement of the substrate per unit time, and the water level from the substrate to the water surface.

3. A method for estimating the weight of algae or aquatic plants according to claim 1 or 2, A method for estimating the weight of algae or aquatic plants, wherein in the estimation step, the weight of the algae or aquatic plants is estimated by the weight of the algae or aquatic plants predetermined in accordance with a measurement value indicating a predetermined state of the substrate.

4. A method for estimating the weight of algae or aquatic plants according to claim 1 or 2, In the estimation process described above, The process involves a computer generating an estimation model through machine learning using a combination of measured values ​​indicating a predetermined state of the substrate and the weight of the algae or aquatic plant as training data, A method for estimating the weight of algae or aquatic plants, comprising the step of inputting the measured values ​​into the estimation model to output the weight of the algae or aquatic plants attached to the substrate.

5. A program for estimating the weight of algae or aquatic plants, A measurement step of measuring a measurement value that indicates a predetermined state of a substrate floating in water with the aforementioned algae or aquatic plants attached to it, A program for estimating the weight of algae or aquatic plants, which causes a computer to perform an estimation step of estimating the weight of the algae or aquatic plants based on the aforementioned measurement values.

6. A weight estimation program for algae or aquatic plants according to claim 5, In the estimation process described above, A step of generating an estimation model by machine learning using a combination of measured values ​​indicating a predetermined state of the substrate and the weight of the algae or aquatic plant as training data, A program for estimating the weight of algae or aquatic plants, which causes a computer to perform the steps of inputting the measured values ​​into the estimation model to output the weight of the algae or aquatic plants attached to the substrate.

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