Rice cultivation management device, rice cultivation management method, and rice cultivation management system

Abstract

To estimate a greenhouse effect gas emission amount from rice paddies for the purpose of creating carbon credits.SOLUTION: A rice cultivation management device, a rice cultivation management method, and a rice cultivation management system can acquire one or more types of satellite data of rice paddies, calculate a daily water index for each of a plurality of water management sections included in the rice paddies based on the satellite data, classify the daily status of each of the plurality of water management sections based on the water index, store the daily status data in a calendar (time series database), detect water management techniques being implemented using the data, and confirm contents of a farmer's logbook. Furthermore, by using this data in conjunction with data from water level sensors, the logbook can be replaced with the calendar in the future. In addition, a daily greenhouse effect gas emission amount from each rice paddy can be more accurately estimated for the purpose of creating carbon credits.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present disclosure relates to a rice cultivation management device, a rice cultivation management method, and a rice cultivation management system. [Background technology]

[0002] Carbon credits have been gaining popularity in recent years. According to the carbon credit system, businesses (credit creators) create credits by implementing projects that reduce greenhouse gas emissions or increase absorption. Meanwhile, companies, local governments, and individuals (credit purchasers) purchase the created credits and use them for carbon offsetting, complying with laws and regulations, etc.

[0003] Carbon credits are the main economic tool to promote incentives for the transition from rice cultivation without AWD to rice cultivation with AWD. In order to effectively trade carbon credits, they need to be highly reliable and have a market value. To issue carbon credits with market value (such as J-credits), it is essential to accurately calculate greenhouse gas emissions.

[0004] On the other hand, a method is known for determining the start time of water withdrawal from paddy fields using satellite data (Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Masato Fukumoto, "Understanding the start time of water withdrawal from paddy fields using Sentinel-2 satellite data", [online], March 5, 2019, Systems Agriculture (J.JASS), 35(2): 15-23, 2019, [Retrieved September 21, 2023], Internet〈URL:https: / / www.jstage.jst.go.jp / article / jass / 35 / 2 / 35_15 / _pdf / -char / ja〉 Summary of the Invention [Problem to be solved by the invention]

[0006] According to Non-Patent Document 1, satellite data observed on clear days from April to June is used to grasp the start time of water intake from paddy fields at intervals of about one week (Figure 6), and the actual usage of agricultural water is grasped. However, since Non-Patent Document 1 uses satellite data on clear days, it does not teach grasping the water intake status in more specific and shorter units such as one day (page 22, "5. Conclusion"). In addition, the purpose is to grasp the actual usage of agricultural water (page 15, "1. Introduction"). Since it is not possible to grasp the water intake status in more specific and shorter units such as one day, it is not possible to estimate greenhouse gas emissions from paddy fields, for example, on a daily basis.

[0007] In view of the above circumstances, the objective of the present disclosure is to estimate greenhouse gas emissions from rice paddies for the purpose of creating carbon credits. [Means for solving the problem]

[0008] A rice cultivation management device, a rice cultivation management method, and a rice cultivation management system according to one embodiment of the present disclosure include: Obtain one or more types of satellite data for rice paddies, Calculating a daily water index for each of a plurality of water management sections included in the paddy field based on the satellite data; A daily status of each of the plurality of water management compartments is classified based on the water index. Effect of the Invention

[0009] According to the present disclosure, it is possible to estimate greenhouse gas emissions from rice paddies for the purpose of creating carbon credits.

[0010] Note that the effects described herein are not necessarily limited to those described herein, and may be any of the effects described in this disclosure. [Brief description of the drawings]

[0011] [Figure 1]1 shows a configuration of a rice cultivation management system according to an embodiment of the present disclosure. [Diagram 2] The operational flow of the rice cultivation management system is shown below. [Diagram 3] Show the machine learning model. [Figure 4] The algorithm of the status classification unit is shown below. [Diagram 5] A calendar for each of multiple water management divisions is shown. [Figure 6] An example of the drainage period (intermediate drainage) analyzed by the analysis unit being superimposed on a calendar is shown below. [Figure 7] 4 shows an example of a drainage period (intermittent irrigation) analyzed by the analysis unit. [Figure 8] We show that it is possible to more accurately classify daily status based on satellite data from multiple types of satellites. [Figure 9] Field water tube is shown. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0013] 1. Background of the Present Invention

[0014] Rice is the staple food for more than half of the world's population and is also a major source of greenhouse gas (GHG) emissions. Rice cultivation is said to be responsible for 12% of the world's anthropogenic methane emissions (https: / / www.bloomberg.com / news / articles / 2019-06-03 / your-bowl-of-rice-is-hurting-the-climate-too#xj4y7vzkg). In light of the current state of global warming, it is urgent to widely establish sustainable farming methods in rice-producing areas around the world as a strategy to reduce global greenhouse gas emissions.

[0015] A method of rice cultivation called AWD (alternate wetting and drying) is known. AWD is in contrast to rice cultivation methods in which rice paddies remain flooded throughout the rice growing season. AWD is a water management and adjustment technique in which water is temporarily drained from rice paddies and the soil is exposed to air. Two typical AWD techniques are known: intermediate drainage, in which rice paddies are drained once during the growing season (so-called mid-drying), and intermittent irrigation, in which rice paddies are drained and re-flooded alternately in multiple cycles throughout the growing season. Of these, mid-drying (i.e., by draining the rice paddy once before ear emergence during the rice growing season and drying the rice field surface) can prevent excessive tillering (branching from the base of the rice plant) and control rice growth.

[0016] In both AWD methods, exposing the soil to oxygen stops the anaerobic decomposition of soil organic matter, reducing methane emissions. Therefore, extending the drainage period (for example, the period of mid-season drainage) beyond the conventional period reduces methane emissions from the soil. In this way, adjusting water management can reduce greenhouse gas emissions from rice cultivation.

[0017] Incidentally, carbon credits have been gaining popularity in recent years. According to the carbon credit system, businesses (credit creators) create credits by implementing projects that reduce greenhouse gas emissions or increase absorption. Meanwhile, companies, local governments, and individuals (credit purchasers) purchase the created credits and use them for carbon offsetting and other purposes.

[0018] Carbon credits are the main economic tool to promote incentives for the transition from rice cultivation without AWD to rice cultivation with AWD. In order to effectively trade carbon credits, they need to be highly reliable and have a market value. To issue carbon credits with market value (such as J-credits), it is essential to accurately calculate greenhouse gas emissions.

[0019] In Japan, there is a system called "J-Credit" where the government certifies credits. In the J-Credit system, if the extension of the mid-drainage period in rice cultivation meets certain conditions, the business owner (e.g., rice farmer) is certified for credit (https: / / japancredit.go.jp / pdf / methodology / AG-005_v1.0.pdf). Specifically, if the mid-drainage period in rice cultivation is extended by 7 days or more from the average number of days implemented in the most recent 2 years or more before the project was implemented in the project-implemented paddy field, the credit application can be certified. If the credit application is certified, the value of the credit is calculated based on the difference between the baseline emissions (emissions expected if the drainage period is not extended) and the emissions after the project is implemented (emissions expected if the drainage period is extended).

[0020] To prove that greenhouse gas emissions have been reduced or that the reduction amount has increased compared to the baseline emissions, first, it is necessary to prove that the drainage period extension was not implemented (or that a drainage period was not set) before the start of the project (baseline period). Second, it is necessary to prove that the drainage period extension was implemented during the project period. In other words, in order to be certified for credits, it is necessary to accurately prove the drainage period over several years.

[0021] In addition, accurate data on the drainage period (the period when water is not present in the paddy field) allows for accurate calculation of greenhouse gas emissions. For example, when a farmer drains a paddy field by artificially draining and re-flooding, if rain falls during the drainage period and water accumulates in the paddy field, greenhouse gas emissions will decrease. Since the value of the credit depends on the amount of greenhouse gas emissions, in order to accurately calculate the value of the credit, it is necessary to accurately grasp not only the artificial drainage period caused by artificial drainage and re-flooding, but also the presence or absence and amount of water in the paddy field due to factors such as the presence or absence of rainfall, the drainage properties of the paddy field, and the water management of the paddy field.

[0022] Generally, farmers keep logbooks (daily journals) to record the dates when they flooded or drained their paddy fields, as a means of proving the drainage period. However, there are problems with relying solely on logbooks to receive carbon credits. First, when a new project begins, there is a possibility that there is no logbook for the baseline period (several years ago). Second, there is a possibility that the accuracy and authenticity of the logbook may be questioned due to financial incentives. Third, even if farmers keep accurate logbooks, while the farmers' own actions (e.g., the dates when flooding and drainage gates were opened and closed) are generally recorded, for example, the presence of water due to rainfall may not be recorded in the logbook because it is not based on the farmers' own actions. However, in order to accurately calculate greenhouse gas emissions, it is necessary to objectively clarify the actual state of the paddy field at a specific time (the presence or absence of water, the amount of water).

[0023] The accuracy of greenhouse gas emission calculations is a particularly important issue. For example, doubts have been raised about the accuracy of the rice cultivation methodology of the Clean Development Mechanism (CDM) of the United Nations Framework Convention on Climate Change (UNFCCC) (https: / / cdm.unfccc.int / methodologies / DB / D14KAKRJEW4OTHEA4YJICOHM26M6BM) (https: / / verra.org / verra-inactivates-unfccc-cdm-rice-cultivation-methodology / ).

[0024] Methods for estimating greenhouse gas emissions from rice paddies include process-based computer models such as the DeNitrification-DeComposition (DNDC) model developed at the University of New Hampshire. This model is commonly used to simulate complex biogeochemical interactions between crops, soil, irrigation, fertilizer, etc. This model has already been adopted in rice paddy methane reduction methods for carbon credits, but it is generally based on rough estimates and seasonal averages, limiting its accuracy.

[0025] In consideration of the above circumstances, according to an embodiment of the present disclosure, the waterless periods of paddy fields that are not affected by artificial drainage or rainfall are analyzed on a daily basis over a period of several years or more, thereby aiming to reliably prove the drainage periods that were carried out over a period of several years or more, so that farmers can properly receive carbon credit certification.

[0026] 2. Structure of the rice cultivation management system

[0027] FIG. 1 shows the configuration of a rice cultivation management system according to an embodiment of the present disclosure.

[0028] The rice cultivation management system 1 acquires satellite data of paddy fields acquired by multiple types of artificial satellites 210 from the satellite database 200 via a network such as the Internet. The rice cultivation management system 1 performs calculation processing based on the satellite data. The rice cultivation management system 1 outputs the calculation results via a network such as the Internet.

[0029] The rice cultivation management system 1 has a satellite data acquisition unit 110, a water index calculation unit 120, a data processing unit 140, a calendar generation unit 150, an analysis unit 160, an output unit 170, and a discharge amount estimation unit 180. In one information processing device (rice cultivation management device 100) functioning as a server, a processor may load an information processing program recorded in a ROM into a RAM and execute the program, thereby functioning as the satellite data acquisition unit 110, the water index calculation unit 120, the status classification unit 140, the calendar generation unit 150, the analysis unit 160, and the output unit 170. Alternatively, a plurality of distributed information processing devices may cooperate to function as the satellite data acquisition unit 110, the water index calculation unit 120, the data processing unit 140, the calendar generation unit 150, the analysis unit 160, the output unit 170, and the discharge amount estimation unit 180.

[0030] The multiple types of satellites 210 have different detection characteristics and acquire satellite data with different characteristics. The multiple types of satellites 210 may be, for example, Sentinel-2 (referred to as S2), LandSat-9 (referred to as LS9), Sentinel-1 (referred to as S1), and PlanetScope (referred to as PS).

[0031] The wavelength bands that S2 can detect are visible light, near infrared (NIR), and shortwave infrared (SWIR). S2 returns to Earth every 5 days on the equator, and its resolution is 10m to 60m.

[0032] LS9 can detect visible light, near infrared (NIR), and shortwave infrared (SWIR) wavelengths. It returns every 16 days and has a resolution of 30m.

[0033] S1 has a synthetic aperture radar (SAR) that irradiates microwaves (electromagnetic waves) on the target and receives the reflected (backscattered) signal. S1's satellite data (SAR backscattered) includes VV polarization (vertical output, vertical reception) and VH polarization (vertical output, horizontal reception). S1's recurrence period is 12 days and its resolution is 10m.

[0034] The detectable wavelength band of the PS is visible light and near infrared (NIR), but does not include shortwave infrared (SWIR). The multiple types of artificial satellites 210 are not limited to the above, and for example, other artificial satellites 210 may be further used. Also, some of the above may not be used (for example, the PS may not be used). In that case, the third water index calculation unit 123 described later may not be provided.

[0035] 3. Operation of the rice cultivation management system

[0036] Figure 2 shows the operational flow of the rice cultivation management system.

[0037] The satellite data acquisition unit 110 acquires satellite data on paddy fields acquired by a plurality of types of artificial satellites 210 (S2, S1, LS9) from the satellite database 200 via a network such as the Internet (step S101).

[0038] The water index calculation unit 120 calculates a daily water index for each of a plurality of water management sections included in a paddy field based on satellite data (optical band data) from a plurality of types of artificial satellites 210 (S2, S1, LS9). In this specification, a water management section means a section in which appropriate water management is possible. A water management section may be, for example, a field section (the largest section in which appropriate water management is possible). The water index calculation unit 120 has a first water index calculation unit 121, a second water index calculation unit 122, and a third water index calculation unit 123.

[0039] The first water index calculation unit 121 calculates two types of water indices for each of the multiple water management sections included in the paddy field based on the reflected light light band data (visible light, near infrared (NIR), and shortwave infrared (SWIR)) that are satellite data of S2 (step S102). The two types of water indices are the Modified Normalized Difference Water Index (MNDWI) and the Surface Water Index (LSWI). The MNDWI is calculated by MNDWI=(Green-SWIR) / (Green+SWIR) (Green is the green band). The LSWI is calculated by LSWI=(nir-swir) / (nir+swir). The MNDWI is effective for detecting surface water, but it is difficult to detect surface water when rice grows above the water level. On the other hand, the LSWI can detect the water index more appropriately (by quantifying the presence of water in the vegetation) during the period when the rice is above the water level, such as the latter half of the rice growing season.

[0040] The first water index calculation unit 121 calculates the water index (MNDWI and LSWI) for each of the multiple water management sections contained in the rice paddy based on the reflected light optical band data (visible light, near infrared (NIR), and shortwave infrared (SWIR)) of LS9 satellite data in a similar manner as described above.

[0041] The third water index calculation unit 123 calculates the water index of each of the water management sections included in the paddy field based on the light band data of reflected light (visible light, near infrared (NIR)) which is the satellite data of the PS. The detectable wavelength band of the PS is visible light and near infrared (NIR), and does not include shortwave infrared (SWIR). For this reason, it is not possible to calculate the MNDWI and LSWI based only on the satellite data of the PS. In this case, the third water index calculation unit 123 may use machine learning to identify the correlation between the visible light and near infrared (NIR) data and the water index (MNDWI and LSWI). For example, the third water index calculation unit 123 may use a neural network trained using, for example, a logbook as training data to calculate the correlation between the visible light band and the predicted waterlogging / draining value. This makes it possible to detect the water index (MNDWI and LSWI) based on the satellite data of the PS. The training data may be data from the field water tube 300 and / or the digital water level sensor 301 described below.

[0042] The second water index calculation unit 122 calculates the water index of each of the water management sections included in the paddy field based on the synthetic aperture radar data, which is the satellite data of S1 (step S103). Specifically, the second water index calculation unit 122 includes a machine learning model 130 for calculating the LSWI from the synthetic aperture radar data of S1. The satellite data of S1 (backscattering of SAR) includes VV polarization (vertical output, vertical reception) and VH polarization (vertical output, horizontal reception). The VV polarization is used to detect the surface shape. The VH polarization is used to detect the soil moisture content. The radar does not rely on visible light and passes through clouds. Therefore, unlike the satellite data (optical band data) of S2, LS9, and PS, it is less affected by clouds. Therefore, it is possible to reinforce the MNDWI and LSWI based on the satellite data (optical band data) of S2, LS9, and PS by the satellite data (radar data) of S1.

[0043] Figure 3 shows the machine learning model.

[0044] The machine learning model 130 is a model obtained by training the VV backscattering coefficient and the VH backscattering coefficient based on the synthetic aperture radar data, the ratio VV / VH, and the date (Julian date) and the value of LSWI to obtain a representative index value of the radar data through a simple neural network. The XGBoost machine learning algorithm can be used to improve the correlation accuracy of the machine learning model 130. XGBoost is a decision tree-based supervised machine learning algorithm for regression date and classification problems.

[0045] The second water index calculation unit 122 inputs the backscattering coefficient of VV and the backscattering coefficient of VH based on the synthetic aperture radar data, the ratio VV / VH, and the date (Julian date) to the machine learning model 130. The machine learning model 130 outputs the LSWI.

[0046] FIG. 4 shows the algorithm of the status classifier.

[0047] The data processing unit 140 has a status classification unit 141 and a water level estimation unit 142. The status classification unit 141 classifies the daily status of each of the multiple water management sections based on the MNDWI and LSWI calculated by the first water index calculation unit 121 and the third water index calculation unit 123, and the LSWI calculated by the second water index calculation unit 122 (step S104).

[0048] There are five types of status for each day, for example: dry, wet, cloud-covered, snow, or no satellite observation. The algorithm for detecting snow (the third step below) does not need to be executed depending on the climate of the area where the paddy field is located.

[0049] The status classification unit 141 performs threshold processing on the MNDWI and LSWI calculated by the first water index calculation unit 121 and the third water index calculation unit 123. If MNDWI<0.0 and LSWI<0.17, the status is likely to be flooded. If MNDWI<0.0 and LSWI>0.17 or if MNDWI>0.0, the status is likely to be flooded.

[0050] The status classification unit 141 performs threshold processing on the LSWI calculated by the second water index calculation unit 122. If LSWI<0.17, the status is likely to be flooded. If LSWI>0.17, the status is likely to be flooded.

[0051] The status classification unit 141 judges whether the status is flooded or drained based on the threshold processing for MNDWI and LSWI calculated by the first water index calculation unit 121 and the third water index calculation unit 123 and the threshold processing for LSWI calculated by the second water index calculation unit 122. It is possible that the result of the threshold processing of the first water index calculation unit 121 and the third water index calculation unit 123 differs from the result of the threshold processing of the second water index calculation unit 122. Therefore, it is preferable that the status classification unit 141 performs machine learning in advance and judges whether the status is flooded or drained based on the threshold processing of the first water index calculation unit 121, the second water index calculation unit 122, and the third water index calculation unit 123. In addition, each of the multiple types of artificial satellites 210 has a recurrence period (S2 is 5 days, LS9 is 16 days). For this reason, there may be a day when only one artificial satellite 210 can obtain satellite data of paddy fields and the other satellite cannot obtain satellite data. In such a case, the status classification unit 141 may determine whether the area is flooded or has fallen water based on the obtained satellite data.

[0052] In this way, as a first step, the status classification unit 141 uses a multi-level threshold algorithm that utilizes two types of water indices (MNDWI and LSWI) to identify whether the area is flooded or flooded. The MNDWI threshold (0.0) and the LSWI threshold (0.17) are explained. A common threshold used to detect water for MNDWI is 0. MNDWI greater than 0 indicates the presence of water, and MNDWI less than 0 indicates the absence of water. On the other hand, there is no such prescribed threshold for LSWI. Therefore, when training the algorithm, the farmer's logbook data can be used as teacher data to determine the LSWI threshold that returns the most accurate results.

[0053] As training data for machine learning, not only farmers' logbooks but also digital water level sensors 301 (IoT sensors) installed in rice paddies can be used.

[0054] There is technology to digitally measure the water level in rice paddies frequently (e.g., daily or more) using digital water level sensors 301 that are more accurate than a farmer's logbook. Such sensors are typically mounted above the maximum water level of the paddy field and measure water depth using the reflection of a laser or electric eye sensor. The collected data may be transmitted wirelessly to a central database or stored locally requiring periodic data downloads.

[0055] However, it may be impractical and cost-prohibitive to deploy digital water level sensors 301 to monitor every individual rice field in a project. In contrast, in this embodiment, deploying digital water level sensors 301 in a representative sample of rice fields can generate training data for a neural network to detect water levels from satellites. This allows data to be collected in a more accurate and cost-effective manner.

[0056] FIG. 9 shows a field water tube.

[0057] A field water tube 300 may be used to monitor water depth in rice paddies. The field water tube 300 is a hollow cylindrical tube that is buried vertically in the soil without any soil inside the field water tube 300. The lower part buried in the soil is provided with a number of punched holes that allow water to flow in and out. When the water is above the soil surface, the water accumulates in the upper part of the field water tube 300 (the part without punched holes) and a positive water level can be measured, whereas when the water is below the soil surface, the water in the soil flows into the field water tube 300 through the punched holes and accumulates in the lower part of the field water tube 300 (the part with punched holes) and a negative water level can be measured. In this way, the field water tube 300 can be used to check positive and negative water levels. In AWD practices where the soil is drained to -15 cm, the field water tube 300 is important in determining when to re-flood the field. The digital water level sensor 301 in combination with the field water tube 300 allows for digital measurements of both positive values ​​(when the water is above ground) and negative values ​​(when the water is below ground).

[0058] Digital datasets of positive and negative water levels from a representative sample of rice fields can be used as training data (instead of farm logbooks) to estimate water levels in rice fields with greater accuracy. In this scenario, neural networks can be run on all bands collected from each satellite source to generate separate correlations for each set of source data, without the need for water indices such as MNDWI or LSWI, to predict water levels accurate to the centimeter.

[0059] The use of the digital water level sensor 301 provides a new and reliable source of information for training machine learning algorithms on satellite data. Additionally, the algorithm can be used to generate a calendar of accurate water levels for each rice field. Additionally, the above creates the ability to see below the soil surface and detect negative water levels. Since intermittent irrigation techniques stipulate that water should be drained 15cm below the soil surface, machine learning based on the training data of the digital sensor data can provide reinforcement that drainage periods are being performed correctly.

[0060] In the second step, the status classification unit 141, based on the satellite data of S1, S2, LS9, and PS, excludes clouds that occupy 80% or more from the median calculation process and determines the status as cloudy. The status classification unit 141 has already determined whether the water is flooded or falling in the first step, but if the clouds occupy 80% or more, it only needs to determine the status as cloudy. This eliminates the possibility of misidentifying whether the water is flooded or falling on a cloudy day.

[0061] In the third step, the status classification unit 141 determines the status as "snowfall" if snowfall accounts for more than 50%. The status classification unit 141 has already determined whether the status is flooded or falling water in the first step, but if snowfall accounts for more than 50%, it only needs to determine the status as "snowfall". This eliminates the possibility of misidentifying a day with snowfall as falling water.

[0062] In this way, the status classification unit 141 determines whether the status for each day is flooded or falling water in the first step. If the status classification unit 141 determines cloudy or snowfall in the second and third steps, it updates the flooded or falling water in the first step to cloudy or snowfall.

[0063] Furthermore, as described above, each of the multiple types of artificial satellites 210 has a recurrent period (S2 is 5 days, LS9 is 16 days, and S1 is 12 days). For this reason, there may be days when none of the artificial satellites 210 (S2, LS9, PS, S1) can observe rice paddies. In such cases, the status classification unit 141 determines the status for each day to be no observation.

[0064] Meanwhile, the water level estimation unit 142 uses the data collected from the field water tube 300 and the digital water level sensor 301 as a learning data set for the neural network and combines it with the satellite data. As a result, the water level estimation unit 142 estimates the positive and negative water levels for each day (step S109). Through machine learning, the water level estimation unit 142 becomes able to estimate the positive and negative water levels (cm) of the paddy field for each day from only the satellite data, even if the water level is below the soil surface (e.g., -15 cm below).

[0065] FIG. 5 shows a calendar for each of a number of water management compartments.

[0066] Meanwhile, the calendar generating unit 150 generates calendars C1 to C4 that distinguish and display the daily status (five types: water fall, flooding, cloudy, snowfall, or no observation) of each of the multiple water management sections 1 to 4 classified by the status classifying unit 141 (step S105). The calendar can also be said to be a daily time series database.

[0067] A calendar is a two-dimensional matrix in which multiple squares are arranged in a grid pattern. One square on the calendar represents one day. The multiple squares are consecutive from the first day to the last day without missing a single day. One axis of the two-dimensional matrix that makes up the calendar displays weeks, and the other axis displays multiple consecutive months in a continuous manner without separating them in the arrangement (placement) for a year. In this example, the vertical axis has seven squares, each corresponding to a day, and displays one week from Monday to Sunday from top to bottom on the vertical axis. The horizontal axis displays one year from January 1st to December 31st in a continuous manner from left to right on the horizontal axis, without separating multiple consecutive months (not independent or separated in the arrangement (placement)). In the calendar, the squares of consecutive months are arranged in a continuous manner without any separation, but as in this example, adjacent months may be separated by different line types (thick lines in this example) for the functionality of the calendar (making it easier to understand the dates). In addition, if the functionality as a calendar is already present (the date is clear), such as by entering the date in numbers within the squares, it is not necessary to separate adjacent months with different line types.

[0068] The calendar displays different statuses by filling in (painting) the squares in different ways (e.g., different colors, different hatching, etc.), to visually clearly distinguish and display the status for each day. There are, for example, five types of status: Dry, Wet, Cloud-covered, Snow, or No satellite observation. Depending on the climate of the region where the rice paddies are located, there may be four types of status excluding Snow.

[0069] FIG. 6 shows an example in which the drainage period (intermediate drainage) analyzed by the analysis unit is superimposed on a calendar.

[0070] The analysis unit 160 determines to be a drainage period (intermediate drying period) the longest period during a specific period (e.g., April to September, which is the rice growing season), that is a period of a predetermined number of consecutive days or more, does not include flooded days, the ratio of drained days during the period is equal to or greater than a threshold value, and both the first day and the last day are drained days (step S106). In this example, the analysis unit 160 determines to be a drainage period (intermediate drying period) the longest period during a specific growing season (e.g., April to September), that is a period of a predetermined number of consecutive days or more (e.g., 5 days), does not include flooded days, the ratio of drained days during the period (11 / 19 days) is equal to or greater than a threshold value (e.g., 50%), and both the first day (Sunday, May 28) and the last day (Thursday, June 16) are drained days.

[0071] The threshold value (e.g., 50%) may be determined based on the cycle or number of days when none of the artificial satellites 210 can observe the paddy fields due to the recurrent periods of the multiple types of artificial satellites 210 (5 days for S2, 16 days for LS9, and 12 days for S1). The reason why both the first and last days must be days of drainage is that, for example, the status of June 17th (Friday) is no observation so there is a possibility that the paddy fields will actually be drained, but the status of June 18th (Saturday) is flooded so there is a possibility that June 17th (Friday) will also be flooded. For this reason, it is highly reliable to not include June 17th (Friday) in the drainage period and to determine that the last day of the drainage period is June 16th (Thursday), which is a day of drainage.

[0072] In this manner, using a daily calendar of flooding and drainage data, the start and end dates of mid-season drainage can be determined. Mid-season drainage is usually performed in June-July. For this reason, the analysis unit 160 searches for consecutive periods of drainage between flooding periods within June-July. The analysis unit 160 determines the first observed drainage day of the mid-season drainage period as the start date of the mid-season drainage. The analysis unit 160 determines the last drainage day before the consecutive flooding days following the mid-season drainage as the end date of the mid-season drainage.

[0073] The calendar generating unit 150 displays the drainage period (interim drying period) analyzed by the analysis unit 160 by superimposing it on a two-dimensional matrix calendar. For example, the calendar generating unit 150 explicitly displays the drainage period (interim drying period) indicating the first day (Sunday, May 28) and the last day (Thursday, June 16) on the calendar. In this specific example, the calendar generating unit 150 may circle the square of the first day (Sunday, May 28) and the square of the last day (Thursday, June 16), respectively, and may surround the period including the possession value and the last day with a roughly rectangular frame drawn with thick lines.

[0074] FIG. 7 shows an example of a drainage period (intermittent irrigation) analyzed by the analysis unit.

[0075] The analysis unit 160 determines, as a drainage period (intermittent irrigation period), a period that is a predetermined number of consecutive days or more during the growing season (e.g., April to September), does not include flooded days, the ratio of drainage days during the period is equal to or greater than a threshold, and both the first and last days are drainage days. The analysis unit 160 may analyze whether continuous flooding, intermediate drainage (intermittent drainage), or intermittent irrigation is being implemented. The analysis unit 160 may analyze whether the farmer is following an intermittent irrigation technique (i.e., intermediate drainage (intermittent drainage) or intermittent irrigation). As shown in (b), intermittent irrigation involves draining and re-flooding paddy fields alternately in multiple cycles throughout the growing season, so that multiple short drainage periods are performed. The predetermined number of consecutive days and the threshold for the ratio of drainage days may be determined based on a logbook, the number of visits of multiple types of artificial satellites 210, or the like. In addition, (a) shows a calendar for a comparative example in which no drainage period (intermittent irrigation, mid-season drainage) is performed.

[0076] Thus, intermittent irrigation is a rice cultivation method that implements intermittent irrigation rather than mid-season drainage periods. In intermittent irrigation, the field is flooded to 5 cm above the soil level and then the soil is allowed to dry out until the water is 15 cm below the soil level. When intermittent irrigation is used, the analysis unit 160 may use a daily calendar of flooding and drainage data to detect alternating multi-day patterns in which the field is flooded and then drained.

[0077] The calendar generating unit 150 may display the drainage periods (multiple periods of intermittent irrigation) analyzed by the analysis unit 160 by superimposing them on a two-dimensional matrix calendar. For example, the calendar generating unit 150 may explicitly display the drainage periods (multiple periods of intermittent irrigation) with the first and last days clearly indicated on the calendar.

[0078] The analysis unit 160 analyzes the drainage period for several consecutive years (which may or may not include the current year) (step S107). For example, the analysis unit 160 analyzes the length of the drainage period for several consecutive years, the start date, the presence or absence of rainfall before and after the drainage period, etc. As described above, under the J-Credit Scheme, if the period of mid-drying is extended by seven days or more from the average number of days implemented over the past two or more years, credit application may be approved. For this reason, records from the past two years as a baseline and analysis results of the baseline and the present are required.

[0079] For example, the analysis unit 160 may analyze the recommended start date of the drainage period of this year before the drainage period is implemented. The analysis unit 160 may analyze before the drainage period is implemented how many days the drainage period of this year should be implemented to extend the drainage period by 7 days or more from the average number of implementation days over the last two or more years (recommended number of implementation days of the drainage period). The analysis unit 160 may analyze during the drainage period how many remaining days the drainage period of this year should be continued to extend the drainage period by 7 days or more from the average number of implementation days over the last two or more years (recommended remaining number of days of the drainage period being implemented). The analysis unit 160 may analyze after the end of the drainage period that the drainage period of this year has been extended by 7 days or more from the average number of implementation days over the last two or more years (whether or not the goal has been achieved after the implementation of the drainage period).

[0080] The emission amount estimation unit 180 further estimates the daily greenhouse gas emissions based on the daily positive and negative water levels by the water level estimation unit 142 (step S110). The emission amount estimation unit 180 may analyze, for example, the expected daily methane gas (CH4) emissions, the expected daily nitrous oxide (N2O) emissions, the expected daily CO2 emissions, etc. The emission amount estimation unit 180 may combine environmental data (soil type, temperature, etc.) in addition to the water level to estimate the daily greenhouse gas emissions using a process-based computer model such as DNDC. The emission amount estimation unit 180 may further estimate the emissions for the entire season by applying standard coefficients to convert CH4 and N2O to tons of CO2 equivalent (tCO2e) and summing the daily emissions over the entire growing season. The actual emissions during the project are subtracted from the baseline emissions to calculate the tCO2e reduction and issue the corresponding carbon credits.

[0081] The output unit 170 outputs and records the calendar, the analysis results, and the emission amount in a large-capacity non-volatile storage device. The output unit 170 may output the calendar, the analysis results, and the emission amount in a format that can be displayed on a display device (step S108). The output unit 170 may output the analysis results to the terminal device 300 as recommendations.

[0082] Based on the displayed calendar, you can understand the past drainage periods (before last year). This makes it easier to determine when the drainage period for the future (this year) should start and how many days it should last, from the perspective of being less susceptible to the effects of rainfall and producing credits based on the baseline.

[0083] 4. Conclusion

[0084] Figure 8 shows that daily status can be classified more accurately based on satellite data from multiple types of satellites.

[0085] It is possible to detect water in rice fields via satellite using various techniques, depending on the specific data bands collected. Satellite coverage is improving, and data providers such as PlanetLabs can provide near-daily imagery of the Earth's land mass. However, data from multiple satellite sources still needs to be combined and layered into a single calendar to create a daily dataset with minimal missing days. As more types of satellite data are integrated, the resulting calendar has fewer missing days, as shown in (d) of the same figure. (a) is S2 only, (b) is two types (S2 and S1), (c) is three types (S2, S1, and LS9), and (d) is four types (S2, S1, LS9, and PlanetLabs).

[0086] Typically, the use of remote sensing in surface water mapping of rice paddies is limited to detecting water early in the season when the rice is usually low enough to observe the water directly. Later in the season, when the rice is above the water level, optical band satellites, and even radar satellites, cannot penetrate the rice vegetation to observe the water directly.

[0087] In contrast, the present embodiment has the advantages of (1) detecting water under rice using laser data from S1, (2) enabling periodic and high-frequency detection by combining data from multiple satellites, and (3) enabling reliable detection by fusion of optical band satellites and radar satellites. According to the present embodiment, surface water can be detected at rice growth stages throughout the year with a time resolution of one day from the integration of various satellite data sources.

[0088] This embodiment allows a baseline to be established based on satellite data without the need to obtain farm logbooks for the period before the start of the extended drainage project. The approach of this embodiment can be used to construct a daily calendar from the historical satellite data for several years before the start of the project to identify when a drainage period was not in place or was in place, and to estimate the corresponding greenhouse gas emissions.

[0089] The majority of records of drainage periods for rice cultivation still rely on farmer logbook records, and considering the challenges of verifying logbooks and collecting past logbooks, this embodiment can also be used to verify and augment farmer logbooks.

[0090] According to this embodiment, a process-based computer model such as the DeNitrification-DeComposition (DNDC) model is coupled with continuous digital measurements, more specifically satellite data with daily frequency representing water depth for each rice field. This embodiment may be used to verify the contents of a farmer's logbook. A calendar may be used instead of a logbook as a reliable source of water quantity data. By incorporating daily measurements, this embodiment allows for more accurate estimates of greenhouse gas reductions compared to existing techniques.

[0091] According to this embodiment, the daily status of each of the multiple water management plots can be classified based on water indices, the daily status data can be stored in a calendar (time series database), and the data can be used to detect the water management techniques being implemented and to check the contents of the farmer's logbook. Furthermore, in the future, the logbook can be replaced by a calendar when used in conjunction with data from water level sensors. Also, daily greenhouse gas emissions from individual rice fields can be more accurately estimated for the purpose of generating carbon credits.

[0092] According to this embodiment, the waterless periods of paddy fields that are not affected by artificial drainage and rainfall are analyzed on a daily basis over a period of several years or more, so that farmers can properly receive carbon credit certification, and the drainage periods carried out over a period of several years or more can be reliably proven. This embodiment can directly contribute to the convenience of farmers who actually grow rice (recording and analysis over the past several years is possible without creating a logbook) and ultimately to their profits (creation of valuable credits based on accurate greenhouse gas reductions). As a result, according to this embodiment, for example, the following effects can be obtained.

[0093] When farmers want to start a project to extend the drainage period, they can know the baseline drainage period history for the past few years, which helps them to start the project promptly. After the project to extend the drainage period is started, farmers can easily determine the start and end dates and length of the drainage period this year based on the baseline drainage period history of the past few years. Also, the actual drainage period this year will be the appropriate start and end dates and length based on the past drainage period history. Reliable verification of multi-year drainage periods will result in more accurate calculation of multi-year greenhouse gas emissions, which in turn will result in more accurate calculation of the value of the carbon credits generated by farmers. This will directly contribute to the convenience and profits of farmers who actually grow rice.

[0094] Although the embodiments and modified examples of the present technology have been described above, the present technology is not limited to the above-described embodiments, and it goes without saying that various modifications can be made within the scope of the gist of the present technology. [Explanation of symbols]

[0095] 1. Rice Cultivation Management System 100 Rice cultivation management device 110 Satellite Data Acquisition Unit 120 Water index calculation section 121 First Water Index Calculation Section 122 Second Water Index Calculation Section 123 Third Water Index Calculation Section 130 Machine Learning Models 140 Data Processing Unit 150 Calendar Generation Unit 160 Analysis Department 170 Output section 180 Emission Estimation Department 200 satellite database 210 Satellite

Claims

1. a satellite data acquisition unit that acquires one or more types of satellite data on paddy fields; a water index calculation unit that calculates a daily water index for each of a plurality of water management sections included in the paddy field based on the satellite data; a status classification unit that classifies a daily status of each of the plurality of water management sections based on the water index; A rice cultivation management device comprising:

2. The rice cultivation management device according to claim 1, The water index calculation unit is A first water index calculation unit and / or a third water index calculation unit that calculates a Modified Normalized Difference Water Index (MNDWI) and a Surface Water Index (LSWI) based on the optical band data, which is the satellite data; A second water index calculation unit that calculates a LSWI based on the radar data, which is the satellite data; having The status classification unit classifies the status based on the MNDWI and the LSWI calculated by the first water index calculation unit and / or the third water index calculation unit and the LSWI calculated by the second water index calculation unit. Rice cultivation management equipment.

3. The rice cultivation management device according to claim 2, The first water index calculation unit calculates the MNDWI and the LSWI based on light band data of visible light, near infrared light, and short wave infrared light, The third water index calculation unit calculates the MNDWI and the LSWI using a neural network based on the visible light and near infrared light band data. Rice cultivation management equipment.

4. The rice cultivation management device according to claim 1, A calendar generating unit that generates a calendar that displays the daily status of each of the plurality of water management compartments in a distinguishable manner. The rice cultivation management device further comprises:

5. The rice cultivation management device according to claim 4, The status classification unit classifies the status into flooded, falling water, cloudy, snowfall, or no observation based on the satellite data; The calendar displays flooded, drained, cloudy, snowy or no observation days in a distinguishable manner for each day. Rice cultivation management equipment.

6. The rice cultivation management device according to claim 4, The calendar is a two-dimensional matrix in which a number of squares are arranged in a grid pattern, with each square representing one day, and the squares are continuous from the first day to the last day without missing a single day, with one axis of the two-dimensional matrix displaying a week, and the other axis displaying a number of consecutive months in a continuous manner without separating them from one another, thereby displaying a year. Rice cultivation management equipment.

7. The rice cultivation management device according to claim 6, The calendar displays different statuses by filling the spaces in different ways, thereby distinguishing between the statuses. Rice cultivation management equipment.

8. The rice cultivation management device according to claim 6, An analysis unit that determines a drainage period as the longest period that is a period of a predetermined number of consecutive days or more during a specific period, does not include flooded days, has a ratio of drainage days within the period equal to or greater than a threshold, and has both a first day and a last day as drainage days. The rice cultivation management device further comprises:

9. The rice cultivation management device according to claim 8, The calendar generating unit further displays the drainage period by superimposing it on the two-dimensional matrix calendar. Rice cultivation management equipment.

10. The rice cultivation management device according to claim 8, The analysis unit analyzes whether the current irrigation method is continuous flooding, intermediate drainage (intermediate drainage), or intermittent irrigation. Rice cultivation management equipment.

11. The rice cultivation management device according to claim 8, The analysis unit analyzes consecutive multi-year drainage periods to determine recommended start dates for future drainage periods, recommended durations for drainage periods, recommended remaining durations for ongoing drainage periods, and / or whether goals have been achieved after the drainage periods have been implemented. Rice cultivation management equipment.

12. The rice cultivation management device according to claim 1, A water level estimation unit that estimates daily positive and negative water levels based on the satellite data by machine learning by learning the satellite data using the water level measurement results by the IoT sensor and / or water tube installed in the paddy field as teacher data. The rice cultivation management device further comprises:

13. The rice cultivation management device according to claim 12, an emission amount estimation unit that estimates a daily greenhouse gas emission amount based on the daily positive and negative water levels; The rice cultivation management device further comprises:

14. Obtain one or more types of satellite data for rice paddies, Calculating a daily water index for each of a plurality of water management sections included in the paddy field based on the satellite data; Classifying a daily status of each of the plurality of water management compartments based on the water index. Rice cultivation management methods.

15. The present invention includes a plurality of information processing devices, a satellite data acquisition unit that acquires one or more types of satellite data on paddy fields; a water index calculation unit that calculates a daily water index for each of a plurality of water management sections included in the paddy field based on the satellite data; a status classification unit that classifies a daily status of each of the plurality of water management sections based on the water index; Functions as Rice Crop Management System.

Images