Method for estimating soil moisture characteristics, apparatus for estimating soil moisture characteristics, and irrigation support system

Abstract

To provide a technique for easily and precisely estimating soil moisture characteristics of a farmland converted from paddy fields.SOLUTION: A soil moisture characteristic estimation device (10) includes an estimation unit (14) which inputs geographic information of a target region where target soil of a farmland converted from paddy fields is located, to an estimation model where geographic information of a region where soil of the farmland converted from paddy fields is located is defined as an independent variable and soil moisture characteristics of the soil is defined as a dependent variable, and acquires an estimation result of the soil moisture characteristics to be output.SELECTED DRAWING: Figure 1

Description

Technical Field

[0005]

[0001] The present invention relates to a method for estimating soil water characteristics, a device for estimating soil water characteristics, and an irrigation support system.

Background Art

[0002] Field water capacity and permanent wilting point are representative soil characteristic values that determine the drainage and water-holding capacity of soil. In order to estimate the water characteristics of various soils including paddy field conversion fields, the values of field water capacity and permanent wilting point, which are specific to each soil, are particularly important among various values representing the characteristics of the soil. For example, currently, in the "soybean irrigation support system" that is being implemented in society, the measured values of field water capacity and permanent wilting point are essential input items.

[0003] Currently, the mainstream method for directly measuring the values of field water capacity and permanent wilting point still exists. In addition, on the website of the Ministry of Agriculture, Forestry and Fisheries, there is a database storing the values of field water capacity and permanent wilting point for each soil in the "Japanese Soil Inventory" (https: / / soil-inventory.rad.naro.go.jp / ). On the other hand, regarding soil water content, which is a soil characteristic value similar to field water capacity and permanent wilting point, as described in Patent Documents 1 to 3, techniques for estimation are known.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0005] Directly measuring field water capacity and permanent wilting point requires expensive measuring equipment and complicated measurement procedures, limiting the number of institutions capable of performing these measurements. Furthermore, the data stored in the Japan Soil Inventory database represents post-harvest values ​​for paddy fields, and it is known that these values ​​differ significantly from those in tilled conditions for converted paddy fields. Therefore, the values ​​stored in such databases cannot be directly referenced for various soil types, including converted paddy fields. Moreover, the technologies described in Patent Documents 1-3 are for estimating soil moisture content and do not correspond to the estimation of field water capacity and permanent wilting point.

[0006] If the field water capacity and permanent wilting point values ​​in converted paddy fields can be easily estimated, it will be easier to estimate the moisture environment in these fields, which can contribute to stable high-level production and improved self-sufficiency of crops grown in converted paddy fields.

[0007] One aspect of the present invention has been made to solve the above-mentioned problems, and its objective is to realize a technology for easily and accurately estimating the soil moisture characteristics of paddy fields converted to upland fields. [Means for solving the problem]

[0008] To solve the above problems, in one aspect of the present invention, a soil moisture characteristic estimation method is performed by an information processing device, in which geographical information of the area where the soil converted from paddy field to upland field is located is input to an estimation model in which geographical information of the area where the soil converted from paddy field to upland field is located is used as an independent variable and the soil moisture characteristic of the said soil is used as a dependent variable, and an estimation step is performed to obtain an estimated result of soil moisture characteristic that is output.

[0009] A soil moisture characteristic estimation device according to one aspect of the present invention includes an estimation unit that inputs geographical information of the target area where the soil converted from paddy field to upland field is located into an estimation model in which geographical information of the area where the soil is located is an independent variable and the soil moisture characteristics of the said soil are a dependent variable, and obtains the output soil moisture characteristic estimation result.

[0010] In one aspect of the present invention, an irrigation support method is performed by an information processing device in which a determination step is performed to determine the irrigation conditions for the soil using the soil moisture characteristics estimated by the soil moisture characteristics estimation method according to one aspect of the present invention.

[0011] An irrigation support system according to one aspect of the present invention comprises a soil moisture characteristic estimation device according to one aspect of the present invention, and an irrigation support device equipped with a determination unit that determines soil irrigation conditions using the soil moisture characteristic estimation device.

[0012] Each aspect of the present invention may be implemented by a computer. In this case, a control program for the soil moisture characteristic estimation device, which enables the computer to implement the soil moisture characteristic estimation device by operating the computer as each component (software element) of the soil moisture characteristic estimation device, and a computer-readable recording medium on which the program is recorded, also fall within the scope of the present invention. [Effects of the Invention]

[0013] According to one aspect of the present invention, the soil moisture characteristics of paddy fields converted to upland fields can be easily and accurately estimated. [Brief explanation of the drawing]

[0014] [Figure 1] This is a block diagram showing an example of the main components of an irrigation support system equipped with a soil moisture characteristic estimation device according to one aspect of the present invention. [Figure 2] This flowchart shows an example of the estimation process performed by a soil moisture characteristic estimation device according to one aspect of the present invention. [Figure 3] This figure shows an example of a histogram published in the Japan Soil Inventory. [Figure 4] This graph shows the estimated value obtained by this invention, the estimated value using only data obtained from the Japan Soil Inventory, and the measured value for the permanent wilting point. [Figure 5]This graph shows the estimated field water capacity obtained by the present invention, the estimated value using only data obtained from the Japan Soil Inventory, and the measured value. [Modes for carrying out the invention]

[0015] [Irrigation support system 100] Hereinafter, a soil moisture characteristic estimation device 10 and an irrigation support system 100 according to one aspect of the present invention will be described with reference to Figure 1. Figure 1 is a block diagram showing an example of the main components of an irrigation support system 100 equipped with a soil moisture characteristic estimation device 10 according to one aspect of the present invention. The irrigation support system 100 comprises a soil moisture characteristic estimation device 10 and an irrigation support device 20. The irrigation support system 100 supports soil irrigation work by using data on soil moisture characteristics estimated by the soil moisture characteristic estimation device 10 to determine soil irrigation conditions in the irrigation support device 20.

[0016] Furthermore, the irrigation support system 100 includes an input device 30, an output device 40, and a storage device 50. In the irrigation support system 100, the soil moisture characteristic estimation device 10, the irrigation support device 20, the input device 30, the output device 40, and the storage device 50 are each connected to each other so as to be able to communicate with one another. In addition, the soil moisture characteristic estimation device 10, the irrigation support device 20, the input device 30, the output device 40, and the storage device 50 may each be independent devices, or at least two of them may be integrated into a single device.

[0017] The input device 30 receives input operations from the user for the irrigation support system 100. As an example, the input device 30 receives the input of data used to estimate the soil moisture characteristics in the soil moisture characteristic estimation device 10. The input device 30 receives the input of data used to determine the irrigation conditions of the soil in the irrigation support device 20. Also, the input device 30 may receive input from the user via a Web API. The input device 30 may output the received data to the soil moisture characteristic estimation device 10 or the irrigation support device 20, and may also store it in the storage device 50.

[0018] The output device 40 outputs, as an example, the result estimated by the soil moisture characteristic estimation device 10 or the irrigation conditions determined by the irrigation support device 20. The mode of output by the output device 40 is not particularly limited. The output device 40 may be, for example, a display device that displays the estimated result or irrigation conditions as an image, a printing device that prints the estimated result or irrigation conditions, or an alarm device that outputs the estimated result or irrigation conditions as sound. Also, the output device 40 may be a display of a mobile device such as a smartphone that displays the estimated result or irrigation conditions. Furthermore, the output device 40 may output the estimated result or irrigation conditions to the user via a Web API.

[0019] The storage device 50 stores programs and data used in the irrigation support system 100. As an example, the storage device 50 stores various data input via the input device 30. Also, as an example, the storage device 50 stores the irrigation conditions determined in the irrigation support device 20. Furthermore, the storage device 50 may have a database for storing various data on the cloud or a server.

[0020] (Soil Moisture Characteristic Estimation Device 10) The soil moisture characteristics estimation device 10 estimates the moisture characteristics of the target soil, which is a converted paddy field. The moisture characteristics of the target soil estimated by the soil moisture characteristics estimation device 10 can be used for crop cultivation in the target soil, management of the moisture environment in the target soil, etc. In addition, the moisture characteristics of the target soil estimated by the soil moisture characteristics estimation device 10 can be used in the irrigation support device 20 to determine the irrigation conditions for the target soil.

[0021] The soil moisture characteristics estimated by the soil moisture characteristics estimation device 10 are data representing the moisture-related characteristics of the soil. The soil moisture characteristics may include at least one of the following: maximum water capacity, field water capacity, initial wilting point, permanent wilting point, and soil water content. Preferably, the soil moisture characteristics are at least one of the field water capacity and the permanent wilting point. The soil moisture characteristics estimation device 10 can also easily and accurately estimate the field water capacity and permanent wilting point of soil, which are conventionally measured directly.

[0022] The soil moisture characteristic estimation device 10 includes a control unit (information processing device) 11. The control unit 11 controls all parts of the soil moisture characteristic estimation device 10, and is implemented, for example, by a processor and memory. In this example, the processor accesses storage (not shown), loads a program (not shown) stored in storage into memory, and executes a series of instructions contained in the program. This constitutes the various parts of the control unit 11. These parts of the control unit 11 include a reception unit 12, a model acquisition unit 13, and an estimation unit 14.

[0023] <Reception area 12> The reception unit 12 receives input data from the user. The input data may be data used for estimating soil moisture characteristics by the soil moisture characteristic estimation device 10. The input data may include geographic information of the target area where the target soil, which is converted paddy field, is located. Geographic information may include, for example, the latitude and longitude of the target area. The reception unit 12 sends the received input data to the estimation unit 14.

[0024] <Model acquisition section 13> The model acquisition unit 13 acquires an estimation model to be used for estimating soil moisture characteristics. The model acquisition unit 13 may acquire an estimation model stored in the storage device 50, or it may acquire an estimation model stored on an external cloud or server. The model acquisition unit 13 sends the acquired estimation model to the estimation unit 14.

[0025] The estimation model acquired by the model acquisition unit 13 is an estimation model in which the geographical information of the region where the soil converted from paddy field is located is the independent variable, and the soil moisture characteristics of the said soil are the dependent variable. For example, such an estimation model may be a regression model that represents the relationship between the independent and dependent variables, or a trained model that has learned the relationship between the independent and dependent variables. If the estimation model is a trained model, for example, it may be a trained model generated using known learning methods such as neural networks, decision trees, random forests, or support vector machines. The data used to generate the estimation model may be the soil moisture characteristics of converted paddy field acquired for each geographical information. The estimation model can be generated by selecting a regression model using such data, or by performing machine learning using such data as training data.

[0026] Furthermore, the estimation model may also include values ​​obtained from a database containing soil moisture characteristics of paddy fields within the region where the converted paddy field soil is located as independent variables. The estimation model may be a multiple regression model that includes geographic information along with soil moisture characteristics of paddy fields within the region where the converted paddy field soil is located as independent variables, or a trained model that has learned the relationship between these independent and dependent variables.

[0027] One example of a database containing soil moisture characteristics for paddy fields is the database created by the Ministry of Agriculture, Forestry and Fisheries. The values ​​for soil moisture characteristics in paddy fields stored in this database can be obtained through the "Japan Soil Inventory" or "e-Soil Map II" published by the Ministry of Agriculture, Forestry and Fisheries. The values ​​for soil moisture characteristics in paddy fields may be the mean, median, or mode of the obtained values ​​within the region.

[0028] Furthermore, the estimation model may also include brightness data representing the brightness of the soil in converted paddy fields as an independent variable. The estimation model may be a multiple regression model that includes geographic information and brightness data of the soil in converted paddy fields as independent variables, or a trained model that has learned the relationship between these independent and dependent variables. The estimation model may be a multiple regression model that includes geographic information and soil moisture characteristics in paddy fields along with brightness data of the soil in converted paddy fields as independent variables, or a trained model that has learned the relationship between these independent and dependent variables.

[0029] Soil brightness data for converted paddy fields is data that represents soil brightness and is related to the carbon content in the soil. Soil brightness data may be data divided into brightness categories according to the carbon content contained in the soil.

[0030] Furthermore, the estimation model may also include soil type classification data as an independent variable, representing soil type classification based on clay and silt content in soil converted from paddy fields. The estimation model may be a multiple regression model that includes geographic information along with clay and silt content in soil converted from paddy fields as independent variables, or a trained model that has learned the relationship between these independent and dependent variables. The estimation model may be a multiple regression model that includes geographic information and soil moisture characteristics in paddy fields along with clay and silt content in soil converted from paddy fields as independent variables, or a trained model that has learned the relationship between these independent and dependent variables. The estimation model may be a multiple regression model that includes geographic information, soil moisture characteristics in paddy fields, and soil brightness data along with clay and silt content in soil converted from paddy fields as independent variables, or a trained model that has learned the relationship between these independent and dependent variables.

[0031] The clay and silt content in the soil may be values ​​set for each soil type category, and as an example, they are the average values ​​of the measured clay and silt content for each soil type category.

[0032] Furthermore, the estimation model may also include other soil moisture characteristic data estimated using the estimation model as independent variables.

[0033] <Estimation part 14> The estimation unit 14 obtains the estimated soil moisture characteristics by inputting the input data received by the reception unit 12 into the estimation model acquired by the model acquisition unit 13. The estimation unit 14 inputs the geographical information of the target area where the target soil (a converted paddy field) is located into an estimation model in which the geographical information of the area where the converted paddy field soil is located is the independent variable and the soil moisture characteristics of the said soil are the dependent variable, and obtains the estimated soil moisture characteristics that are output.

[0034] The estimation unit 14 outputs the acquired estimated soil moisture characteristics to the irrigation support device 20 or output device. The estimation unit 14 may also store the acquired estimated soil moisture characteristics in the storage device 50.

[0035] (Soil moisture characteristics estimation process) A soil moisture characteristic estimation method according to one aspect of the present invention is performed by a control unit 11 of an information processing device such as a soil moisture characteristic estimation device 10. The flow of the soil moisture characteristic estimation process by the soil moisture characteristic estimation device 10 will be explained with reference to Figure 2. Figure 2 is a flowchart showing an example of the estimation process performed by the soil moisture characteristic estimation device 10. As shown in Figure 2, first, the reception unit 12 acquires geographic information of the target area where the target soil is located (step S11). Next, the model acquisition unit 13 acquires an estimation model (step S12).

[0036] The estimation unit 14 inputs the geographic information of the target area acquired by the reception unit 12 into the estimation model acquired by the model acquisition unit 13, and obtains the estimated result of the soil moisture characteristics of the target soil (step S13, estimation process). The estimation unit 14 outputs the acquired estimated value as the estimation result to the output device 40 (step S14), and ends the estimation process.

[0037] The soil moisture characteristics estimation device 10 can easily estimate the soil moisture characteristics of converted paddy fields based on geographical information of the area where the soil is located. Furthermore, the soil moisture characteristics estimation device 10 can easily estimate the field water capacity and permanent wilting point of converted paddy fields. The information on soil moisture characteristics estimated by the soil moisture characteristics estimation device 10 can be used to determine soil irrigation conditions, thereby supporting the implementation of appropriate soil irrigation.

[0038] (Irrigation support device 20) The irrigation support device 20 determines the irrigation conditions for the soil of a converted paddy field. An irrigation support method according to one aspect of the present invention is executed by a control unit 21 of an information processing device such as the irrigation support device 20. The irrigation support device 20 includes a control unit (information processing device) 21. The control unit 21 controls all parts of the irrigation support device 20 and is implemented, for example, by a processor and memory. In this example, the processor accesses storage (not shown), loads a program (not shown) stored in storage into memory, and executes a series of instructions contained in the program. This constitutes the various parts of the control unit 21. As such parts, the control unit 21 includes a reception unit 22 and a determination unit 23.

[0039] <Reception Desk 22> The reception unit 22 receives input data entered by the user. The input data may be used to determine the soil irrigation conditions by the irrigation support device 20. The input data may include information about the target soil for which the irrigation conditions are determined. The reception unit 22 receives data representing the soil moisture characteristics of the target soil estimated by the soil moisture characteristic estimation device 10. The reception unit 22 sends the received data to the determination unit 23.

[0040] <Decision Section 23> The determination unit 23 determines the soil irrigation conditions using the data received by the reception unit 22. That is, the determination unit 23 determines the soil irrigation conditions using the soil moisture characteristics estimated by the soil moisture characteristics estimation device 10 (determination step). The determination of the soil irrigation conditions by the determination unit 23 may be based on criteria predetermined by the user. Alternatively, the determination unit 23 may determine the soil irrigation conditions based on irrigation conditions determined for each region. The determination unit 23 outputs the determined soil irrigation conditions to the output device 40. The determination unit 23 may also store the determined soil irrigation conditions in the storage device 50.

[0041] Soil irrigation conditions vary depending on the soil's moisture characteristics, and information on soil moisture characteristics is necessary to determine appropriate irrigation conditions. In particular, in converted paddy fields, the moisture characteristics differ from those of soil used solely for paddy fields, making it difficult to refer to the moisture characteristics of other soils. The irrigation support device 20 determines soil irrigation conditions using the soil moisture characteristics estimated by the soil moisture characteristic estimation device 10, thus enabling the determination of appropriate irrigation conditions.

[0042] This configuration will help in responding to extreme weather events and in maintaining and developing agriculture. Such effects will also contribute to achieving goals such as Goal 13, "Take urgent action to combat climate change," and Goal 15, "Protect and restore life on land," as advocated by the United Nations as Sustainable Development Goals (SDGs).

[0043] [Examples of implementation using software] The function of the soil moisture characteristic estimation device 10 (hereinafter referred to as "the device") is a program that causes a computer to function as the device, and can be realized by a program that causes a computer to function as each control block of the device (particularly each part included in the control unit 11).

[0044] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., memory) as hardware for executing the program. By executing the program using this control device and storage device, the functions described in each of the embodiments are realized.

[0045] The above program may be recorded on one or more computer-readable recording media, not temporary ones. These recording media may or may not be provided by the above device. In the latter case, the program may be supplied to the above device via any wired or wireless transmission medium.

[0046] Furthermore, some or all of the functions of each of the above control blocks can also be realized by logic circuits. For example, an integrated circuit in which logic circuits functioning as each of the above control blocks are formed is also included in the scope of the present invention. In addition, it is also possible to realize the functions of each of the above control blocks by, for example, a quantum computer.

[0047] In other words, the present invention includes a program for estimating soil moisture characteristics to enable a computer to function as a soil moisture characteristic estimation device 10, and a program for enabling a computer to function as an estimation unit 14.

[0048] Furthermore, each process described in the above embodiments may be performed by AI (Artificial Intelligence). In this case, the AI ​​may operate on the control device described above, or it may operate on other devices (for example, an edge computer or a cloud server).

[0049] The present invention is not limited to the embodiments described above, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Examples]

[0050] An embodiment of the present invention is described below. 523 soil samples from paddy fields converted to upland fields throughout Japan were analyzed, and latitude and longitude, permanent wilting point, field water capacity, clay content, silt content, total carbon content, lightness, and soil properties were analyzed. First, approximately 500g of soil from the converted paddy fields was collected and analyzed to prepare the primary data set shown in Table 1 (n=523).

[0051] [Table 1]

[0052] Based on these analytical values, a multiple regression model was created with latitude and longitude, soil type, lightness, and data from the Japan Soil Inventory as independent variables, and field water capacity and permanent wilting point as dependent variables. This model was used to estimate field water capacity and permanent wilting point.

[0053] To estimate the permanent wilting point and field water capacity from more easily obtainable data, data (Table 2) relating soil type, clay content, and silt content was acquired based on the dataset in Table 1. Table 2 shows the soil type classification and the average values ​​of clay content and silt content (n=523) for paddy-converted upland fields. The data shown in Table 2 was created by calculating the average values ​​of clay content and silt content corresponding to the soil type.

[0054] [Table 2]

[0055] Furthermore, based on the dataset in Table 1, data relating soil brightness and carbon content (Table 3) was obtained. Table 3 shows the average values ​​(n=489) of soil brightness and corresponding carbon content for converted paddy fields. The data shown in Table 3 was created by calculating the average values ​​of soil brightness and corresponding carbon content. Although the relationship between soil brightness classifications and carbon content is known, calculating the average values ​​of carbon content for each brightness classification in converted paddy fields is a new attempt according to the present invention.

[0056] [Table 3]

[0057] By referring to the Japan Soil Inventory database, we obtained publicly available histograms for each region based on the latitude and longitude of the area where each soil sample was collected. An example of the obtained histogram is shown in Figure 3. Figure 3 is a diagram showing an example of a histogram published in the Japan Soil Inventory. Histograms like the one shown in Figure 3 can be accessed from the Japan Soil Inventory website for each soil type in each prefecture. Note that the data published in the Japan Soil Inventory is post-harvest data for paddy fields.

[0058] From the acquired histograms, we obtained values ​​for permanent wilting points and field water capacity, and calculated the mean, mode, and median within the target area.

[0059] From these data, the datasets shown in Tables 4 and 5 were obtained. Tables 4 and 5 show the parameters used to estimate the permanent wilting point and field water capacity.

[0060] [Table 4]

[0061] [Table 5]

[0062] Based on the data shown in Tables 4 and 5, a multiple regression equation was derived that minimizes the mean squared error (RMSE). This process also considered cases where there were missing data points in the soil type data, lightness data, and data obtained from the Japan Soil Inventory. First, the permanent wilting point was estimated, and then the estimated permanent wilting point was included as an independent variable to estimate the field water capacity.

[0063] Table 6 shows the datasets used for estimation and the RMSE for each multiple regression equation. In Table 6, "○" indicates that the data was used for estimation, and "×" indicates that the data was not used for estimation.

[0064] [Table 6]

[0065] Figures 4 and 5 show a comparison of the estimated values ​​obtained using the multiple regression equation and the estimated values ​​using only data from the Japan Soil Inventory. Figure 4 is a graph showing the estimated value obtained by the present invention, the estimated value using only data from the Japan Soil Inventory, and the measured value for the permanent wilting point. Figure 5 is a graph showing the estimated value obtained by the present invention, the estimated value using only data from the Japan Soil Inventory, and the measured value for the field water capacity. In Figures 4 and 5, the data obtained from the Japan Soil Inventory are medians, and the multiple regression equation obtained by the present invention is the result using equation 2 in Table 6.

[0066] As shown in Table 6, Figures 4 and 5, compared to using only data obtained from the Japan Soil Inventory, an improvement in estimation accuracy (RMSE) was observed in all regression equations according to the present invention, enabling highly accurate estimation.

[0067] Furthermore, it is believed that the data in the Japan Soil Inventory overestimates actual values ​​for both field water capacity and permanent wilting point. This is thought to be due to the fact that the database consists of measurements taken in paddy field soil after cultivation, rather than in tilled field conditions. [Explanation of Symbols]

[0068] 10. Soil moisture characteristic estimation device 14 Estimation part 20 Irrigation support equipment 100 Irrigation Support System

Claims

1. The estimation process involves inputting geographical information of the area where the converted paddy field soil is located into an estimation model, where geographical information of the area is located is the independent variable, and the soil moisture characteristics of the said soil are the dependent variable, and obtaining the estimated results of the soil moisture characteristics that are output. A method for estimating soil moisture characteristics, executed by an information processing device.

2. The soil moisture characteristic estimation method according to claim 1, wherein the estimation model further includes as independent variables values ​​obtained from a database storing soil moisture characteristics in paddy fields within the region.

3. The soil moisture characteristics estimation method according to claim 1 or 2, wherein the estimation model further includes soil type determination data representing soil type classifications based on clay content and silt content in soil converted from paddy field to upland field as independent variables.

4. The soil moisture characteristic estimation method according to claim 1 or 2, wherein the estimation model further includes lightness data representing the lightness of soil that has been converted from paddy field to dryland as an independent variable.

5. The soil moisture characteristic estimation method according to claim 1 or 2, wherein the estimation model is a regression model representing the relationship between the independent variable and the dependent variable, or a trained model that has learned the relationship between the independent variable and the dependent variable.

6. The soil moisture characteristic estimation method according to claim 1 or 2, wherein the soil moisture characteristic is at least one of the field water capacity and the permanent wilting point.

7. The estimation unit takes geographical information of the area where the converted paddy field soil is located as the independent variable and the soil moisture characteristics of the said soil as the dependent variable, and inputs the geographical information of the target area where the converted paddy field soil is located, and obtains the estimated result of the soil moisture characteristics that is output. A soil moisture characteristic estimation device equipped with the following features.

8. A program for estimating soil moisture characteristics for causing a computer to function as a soil moisture characteristic estimation device according to claim 7, wherein the program causes the computer to function as the estimation unit.

9. A determination step of determining soil irrigation conditions using the soil moisture characteristics estimated by the soil moisture characteristics estimation method described in claim 1 or 2. A method for supporting irrigation, executed by an information processing device.

10. A soil moisture characteristic estimation device as described in claim 7, An irrigation support device comprising a determination unit that determines soil irrigation conditions using the soil moisture characteristics estimated in the soil moisture characteristics estimation device, Equipped with an irrigation support system.

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