JP2016111939A - Cultivation abandoned site determination method, cultivation abandoned site determination program and cultivation abandoned site determination device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a cultivation abandoned site determination method, a cultivation abandoned site determination program and a cultivation abandoned site determination device which can grasp the situation of cultivation abandoned sites.SOLUTION: A cultivation abandoned site determination device 10 inputs time-series data on the intensity of one or more wavelength region. Depending on whether input time-series data includes a section having its intensity equal to or higher than a predetermined rate of change, the cultivation abandoned site determination device 10 determines whether a field associated with the time-series data is a cultivation abandoned site.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a farm abandoned land determination method, a farm abandoned land determination program, and a farm abandoned land determination apparatus.

  Conventionally, there is a technique for performing field management based on images in a field. For example, there is a technique for managing the growth state of a plant based on a temporal change in a spectral spectrum of an image obtained by photographing a plant cultivated in a field. In addition, there is a technique for managing the sugar content of a fruit based on the characteristics of a reflection spectrum when the fruit of a plant cultivated in a field is irradiated with light. In addition, there is a technique for managing the soil characteristics of the field based on the reflection spectrum when the field soil is irradiated for each position.

JP 2010-220869 A JP 2006-055744 A JP 2008-032450 A

  By the way, in recent years, farmland where farming is abandoned is increasing due to factors such as the aging of farmers, the deterioration of management conditions, lack of successors, and an increase in non-farmers with land ownership. In the future, there are concerns that the number of abandoned farmland will increase not only in the hilly and mountainous areas where the cultivation conditions are bad, but also in relatively flat agricultural areas. The increase in abandoned cultivated land leads to multifaceted functional decline in agriculture, such as national land conservation and water source recharge. In addition, the increase in abandoned cultivated land is a cause of weed overgrowth, generation of pests, damage to birds and beasts, etc., as well as adversely affecting agricultural production activities in neighboring cultivated land, and also hindering the accumulation of farmland. Become. Therefore, elimination of abandoned farmland is desired.

  Therefore, there is a request to accurately grasp the current abandoned farmland. However, because it does not know whether there is actual planting in the field, but responds by surveys such as declarations and interviews, it is inaccurate to grasp the abandoned farmland. In addition, if a visual field survey is performed for each field, a great amount of man-hours are generated. In addition, if the surveyed time point is a field before planting, it may be erroneously determined as an abandoned farmland. For this reason, it is desired to evaluate whether or not the farm field is an abandoned cultivation area through a certain period of time, but a great number of man-hours are generated.

  And the above-mentioned technique only solves the microscopic problems of growth management of individual crops in the field, sugar content control of individual fruits, and soil property management of individual fields, and abandoned farmland cannot be grasped.

  In one side, it aims at providing the cultivation abandonment land determination method which can grasp cultivation abandonment land, a cultivation abandonment land determination program, and a cultivation abandonment land determination device.

  In one aspect of the abandoned cultivating land determination method, the computer inputs time-series data regarding the intensity of one or more wavelength regions. Then, the computer is associated with the time-series data depending on whether or not there is a section whose intensity is greater than or equal to a predetermined change amount or a predetermined change rate before and after in the input time-series data. It is determined whether or not the field is abandoned farmland.

  Abandoned farmland can be grasped.

FIG. 1 is a block diagram illustrating an example of a functional configuration of the abandoned farmland determination device according to the first embodiment. FIG. 2A is a diagram illustrating an example of time-series data of reflection spectrum intensity according to the first embodiment. FIG. 2B is a diagram illustrating an example of time-series data of the spectral intensity change rate according to the first embodiment. FIG. 2C is a diagram illustrating an example of farm field information. FIG. 3 is a flowchart illustrating an example of a procedure of the abandoned farmland determination process according to the first embodiment. FIG. 4 is a diagram illustrating an example of an output mode of the abandoned farmland determination processing result according to the first embodiment. FIG. 5A is a diagram illustrating an example of an intensity distribution of a reflection spectrum of soil. FIG. 5B is a diagram illustrating an example of an intensity distribution of a reflection spectrum of a wheat field. FIG. 5C is a diagram illustrating an example of an intensity distribution of a reflection spectrum of a potato field. FIG. 5D is a diagram illustrating an example of an intensity distribution of a reflection spectrum of a sugar beet field. FIG. 5E is a diagram illustrating an example of an intensity distribution of a reflection spectrum of grassland. FIG. 6 is a diagram illustrating an example of time-series data of the spectral intensity change rate according to the second embodiment. FIG. 7 is a flowchart illustrating an example of the procedure of the abandoned farmland determination process according to the second embodiment. FIG. 8 is a block diagram illustrating an example of a functional configuration of the abandoned farmland determination device according to the third embodiment. FIG. 9A is a diagram illustrating an example of the field state determination reference data according to the third embodiment. FIG. 9B is a diagram illustrating an example of the field state determination reference data according to the third embodiment. FIG. 10 is a flowchart illustrating an example of a procedure of the abandoned farmland determination process according to the third embodiment. FIG. 11 is a diagram illustrating an example of an output mode of the abandoned farmland determination processing result according to the third embodiment. FIG. 12 is a diagram illustrating an example of a configuration of a farm abandoned land determination system including a farm abandoned land determination device. FIG. 13: is a figure which shows an example of the hardware constitutions of the computer which executes the cultivation abandonment land determination program which concerns on Examples 1-3.

  Hereinafter, an abandoned farmland determination method, an abandoned farmland determination program, and an abandoned farmland determination apparatus according to the present application will be described with reference to the accompanying drawings. In the following description of each embodiment, the description of the same configuration or processing that overlaps will be omitted. Further, the following embodiments do not limit the disclosed technology. Each embodiment can be appropriately combined within a range in which processing contents are not contradictory.

[Configuration of Cultivated Land Determination Device According to Example 1]
FIG. 1 is a block diagram illustrating an example of a functional configuration of the abandoned farmland determination device according to the first embodiment. The abandoned cultivated land determination device 10 according to the first embodiment is a computer that executes a cultivated abandoned land determination program. The abandoned cultivated land determination device 10 associates reflection spectrum intensities in a predetermined wavelength region for each position information corresponding to each position on the ground surface, which is obtained by remote sensing the ground surface from an artificial satellite, an aircraft, or the like at a predetermined cycle over a predetermined period. Input the time series data of the reflected spectrum intensity. The position information is, for example, latitude and longitude, coordinates of a predetermined coordinate system representing points on the ground surface, and the like. And the cultivation abandonment land determination apparatus 10 outputs the determination result whether the agricultural field corresponding to each position information is a cultivation abandonment land from the change of the time series data of the reflection spectrum intensity of the predetermined wavelength region. Provide judgment services.

  The reflection spectrum intensity in the predetermined wavelength region is the spectrum intensity of the reflected light reflected by the sunlight on the ground surface. The predetermined wavelength region is, for example, G (green component wavelength region): about 500 to 590 nm, and R (red component wavelength region): about 610 to 680 nm. The predetermined wavelength region is, for example, NIR (Near InfraRed, near infrared component wavelength region): about 780 to 890 nm, SWIR (Short Wave Infrared Radiometer, short wavelength infrared component wavelength region): 1,580-1 , About 750 nm. That is, the predetermined wavelength region is at least one of four bands of G, R, NIR, and SWIR. The spectral intensity of the reflected light of these four bands is obtained by a multispectral sensor that acquires a multispectral image of the ground surface or a hyperspectral sensor that acquires a hyperspectral image of the ground surface.

  As an example, the abandoned farmland determination device 10 may employ an information processing device such as a desktop or notebook personal computer. In addition, the abandoned cultivated land determination device 10 can employ not only stationary terminals such as the personal computer but also various portable terminal devices. For example, as an example of a mobile terminal device, a mobile communication terminal such as a smart phone, a mobile phone or a PHS (Personal Handyphone System), and a slate terminal such as a PDA (Personal Digital Assistants) are included in the category.

  In addition, in Example 1, as an example to the last, the cultivation abandonment land determination apparatus 10 provides the cultivation abandonment land determination service by the stand-alone which performs said cultivation abandonment land determination program independently without relying on an external resource. Is assumed. Although details will be described later, the farm abandoned land determination service is not limited to the implementation provided in a stand-alone manner. For example, it can also be constructed as a client server system by providing the cultivation abandoned land determination device 10 as a server device that provides a client with a cultivation abandoned land determination service by executing a cultivation abandoned land determination program.

  As illustrated in FIG. 1, the abandoned farmland determination device 10 includes a storage unit 11, a control unit 12, and an input / output I / F (Inter Face) unit 13. The cultivation abandoned land determination device 10 is connected to an input device 1 that receives input of time-series data of reflection spectrum intensity and an output device 2 that outputs a cultivation abandoned land determination result. The input device 1 is an input device such as a keyboard, a mouse, a touch panel, and an image scanner. Alternatively, the input device 1 may be a device that reads data from a storage medium or a storage device that stores data used for the abandoned farmland determination process such as the reflection spectrum intensity and inputs the data to the abandoned farmland determination device 10. The output device 2 is a device that outputs the result of the abandoned farmland determination process, such as a display device or a printer device. Alternatively, the output device 2 may be a storage medium that can store the result of the abandoned farmland determination process or a device that writes data to a storage device. The cultivation abandoned land determination device 10 is connected to the input device 1 and the output device 2 via the input / output I / F unit 13.

  The storage unit 11 is a device that stores data used in various programs such as an OS (Operating System) executed by the control unit 12 and a farm abandoned land determination program and application programs. The memory | storage part 11 is mounted as a main memory in the cultivation abandonment land determination apparatus 10, as an example. For example, the storage unit 11 can employ various semiconductor memory elements, such as a RAM (Random Access Memory) and a flash memory. The storage unit 11 can also be implemented as an auxiliary storage device. In this case, an HDD (Hard Disk Drive), an optical disk, an SSD (Solid State Drive), or the like can be employed.

  The storage unit 11 includes a spectrum time-series data storage unit 11a, a spectrum change rate time-series data storage unit 11b, and a field information storage unit 11c. The spectrum time series data storage unit 11a stores spectrum time series data 11a-1 (see FIG. 2A). The spectrum change rate time series data storage unit 11b stores spectrum change rate time series data 11b-1 (see FIG. 2B). The farm field information storage unit 11c stores farm field information 11c-1 (see FIG. 2C).

[Time Series Data of Reflection Spectrum Intensity According to Example 1]
FIG. 2A is a diagram illustrating an example of time-series data of reflection spectrum intensity according to the first embodiment. Each “time” corresponding to each data of each time series data is called “period”. The “period” is also a “section” corresponding to each data of each time series data. Hereinafter, the time series data of the reflection spectrum intensity is referred to as spectrum time series data. The spectrum time series data 11a-1 is defined by the field information 11c-1 as the reflection spectrum intensity for each position information in each “period” of the time series data of the reflection spectrum intensity for each position information obtained by remote sensing. Time series data averaged for each field. That is, the spectrum time series data 11a-1 is time series data of the average value of the reflection spectrum intensity for each field. Note that the spectral time-series data 11 a-1 represents representative values such as the mode value, median value, etc. of the spectral intensity of each “period” in each field, instead of the average value of the reflected spectrum intensity for each field. It is good also as each spectrum intensity | strength of the said "period."

  In the spectrum time series data 11a-1, for each “field ID (IDentidier)”, a plurality of “periods” and reflection spectral intensities of “G”, “R”, “NIR”, and “SWIR” are associated with each other. . The “period” indicates the timing at which the reflection spectrum intensity of the field corresponding to each “field ID” is periodically collected every 30 days (one month) from the predetermined “reference date”. In the example of FIG. 2A, each of “1 month” to “6 months after” every 30 days (1 month) after the predetermined “reference date” and “reference date” is set as one “period”. ] Is associated with the reflection spectrum intensity to form time-series data of the reflection spectrum in each “field ID”.

  In FIG. 2A, for example, when the “field ID” is “F001”, the reflection spectrum intensities of “G”, “R”, “NIR”, and “SWIR” on the “reference date” are “92” and “81”, respectively. “120” and “80”. Further, for example, when the “field ID” is “F001”, the reflection spectrum intensities of “G”, “R”, “NIR”, and “SWIR” at “6 months later” are “96” “88” “ 119 "" 85 ".

[Time Series Data of Spectral Intensity Change Rate According to Example 1]
FIG. 2B is a diagram illustrating an example of time-series data of the spectral intensity change rate according to the first embodiment. Hereinafter, the time series data of the spectrum intensity change rate is referred to as spectrum change rate time series data. The spectrum change rate time-series data 11b-1 includes, for each “field ID”, a plurality of “periods”, “change rate of NIR”, “change rate is greater than or equal to threshold value”, and “cultivated land determination result”. It is associated. The “farm field ID” and “period” in FIG. 2B correspond to the “farm field ID” and “period” in FIG. 2A. In the example of FIG. 2B, each “period” is defined as “one month” to “6 months after” each 30 days (one month) after the predetermined “base date” and “base date”. ] Is associated with the rate of change of the reflection spectrum intensity with respect to the previous period. Each “period” is associated with the rate of change of the reflection spectrum intensity relative to the previous period, thereby forming time-series data of the spectrum change rate for each “field ID”. The “change rate is greater than or equal to the threshold value” and the “cultivated land determination result” will be described later.

  In FIG. 2B, for example, when “Field ID” is “F001”, “NIR change rate” after “1 month” is “78.3%”. “78.3%” is “NIR reflection spectrum intensity” “94” (see FIG. 2A) “after 1 month” in “Field ID” “F001”, and “NIR reflection spectrum intensity” is “reference date”. The percentage divided by “120” (see FIG. 2A). For example, in the case of “Field ID” “F001”, “NIR change rate” in “6 months later” is “93.7%”. “93.7%” means “NIR reflection spectrum intensity” “119 months” (see FIG. 2A) in “Field ID” “F001” “After 6 months” “NIR reflection spectrum intensity” in “5 months later” "127" (see Fig. 2A).

  FIG. 2C is a diagram illustrating an example of farm field information. The farm field information 11c-1 is information in which "vertex 1" to "vertex 4" are associated with "farm field ID". “Vertex 1” to “Vertex 4” in FIG. 2C represent position information of the four vertices of the substantially rectangular section corresponding to the corresponding “farm field ID”. For example, “vertex 1” to “vertex 4” are each represented by position information such as latitude and longitude. In FIG. 2C, for example, “vertex 1” “w1”, “vertex 2” “x1”, “vertex 3” “y1”, “vertex 4” “z1” correspond to “field ID” “F001”. It is attached. For example, when the position information of the time-series data of the reflection spectrum intensity for each position information obtained by remote sensing is located in the field area of “vertex 1” “w1” to “vertex 4” “z1”, It is determined that the position is included in the field of “Farm field ID” “F001”. The method of defining the field of the field is not limited to the method using the four vertices of the field section, but the section such as a representative point such as the center of gravity of the section, one point of the section, and the vertical and horizontal length of the section is uniquely determined. Any method can be used as long as it can be identified.

  Returning to FIG. 1, the control unit 12 executes the abandoned farmland determination program using the spectrum time-series data 11a-1, the spectrum change rate time-series data 11b-1, and the field information 11c-1. Note that the spectral change rate time-series data 11b-1 is intermediate data generated through processing by the control unit 12.

  The control unit 12 is implemented as a central processing unit, a so-called CPU (Central Processing Unit). In addition, the control part 12 does not necessarily need to be mounted as a central processing unit, and may be mounted as a MPU (Micro Processing Unit). The control unit 12 can also be realized by a hard wired logic such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  The control unit 12 includes an input unit 12a, a generation unit 12b, a determination unit 12c, and an output unit 12d. The input unit 12a inputs the reflection spectrum intensity for each position information in each “period” of the time-series data of the reflection spectrum intensity for each position information obtained by remote sensing, which is input from the input device 1, and uses the field information 11c-1 Average for each field specified in. And the input part 12a produces | generates the time series data of the reflection spectrum intensity which calculated the average value for every agricultural field as the spectrum time series data 11a-1. Then, the input unit 12a stores the generated spectrum time series data 11a-1 in the spectrum time series data storage unit 11a.

  The generation unit 12b generates spectrum change rate time series data 11b-1 from the spectrum time series data 11a-1 stored in the spectrum time series data storage unit 11a. For example, in the spectrum time-series data 11 a-1 of the reflection spectrum intensity of each band corresponding to each “farm field ID”, the generation unit 12 b selects each “for the NIR band spectrum of the previous period immediately before each“ period ”. The period ratio of the spectrum of the NIR band of “period” is calculated. The ratio of the spectrum of the NIR band to the previous period is “NIR change rate”. In this way, the generation unit 12b generates time series data of “NIR change rate” with respect to the previous period of each “period” (spectrum change rate time series data 11b-1 (see FIG. 2B)). The generation unit 12b stores the generated spectrum change rate time series data 11b-1 in the spectrum change rate time series data storage unit 11b.

  The determination unit 12c refers to the spectrum change rate time series data 11b-1, and in the NIR spectrum change rate time series data 11b-1 for each “field ID”, the change rate of the spectrum intensity is reduced by, for example, 20% or more. It is determined whether or not “period” exists. That is, the presence of a “period” in which the rate of change in spectral intensity decreases by, for example, 20% or more indicates that the crop is harvested and the soil is exposed in the field corresponding to the “field ID”. .

  The determination unit 12c then determines that the field corresponding to the “field ID” is “cultivated land” for the “field ID” in which the “period” in which the rate of change of the spectrum intensity decreases by, for example, 20% or more. judge. For example, in the case of “Field ID” “F001” in FIG. 2B, the determination unit 12c has “NIR change rate” of “1 month later” as “78.3%”, and the change rate is, for example, 20 It is determined that the decrease is at least%. In the example of FIG. 2B, this corresponds to a portion marked with “◎” in the column “change rate is greater than or equal to threshold”. That is, “Agricultural field ID” “F001” to “F003”, “F009” corresponding to “Cultivated land” is applicable.

  On the other hand, regarding the “field ID” in which there is no “period” in which the rate of change of the spectrum intensity decreases by, for example, 20% or more, the determination unit 12c has “abandoned farmland” as the field corresponding to the “field ID”. Is determined. For example, in FIG. 2B, the determination unit 12 c does not have “period” in which the change rate of the spectrum intensity decreases by, for example, 20% or more when “farm ID” “F004” “F005”. In the example of FIG. 2B, “Field” is not added to any “period” in the column “change rate is greater than or equal to the threshold value”, and “cultivated land determination result” is “cultivated abandoned land”. “F004” and “F005” are applicable.

  And the determination part 12c adds the result of cultivation abandonment land determination processing to the spectrum change rate time series data 11b-1. And the determination part 12c performs each cultivation abandonment land determination process about each "farm field ID".

  The output unit 12d generates a result of the abandoned farmland determination process based on the spectrum change rate time-series data 11b-1 to which the result of the abandoned farmland determination process is added, and outputs the result to the output device 2. For example, the output unit 12d associates actual map data with “farm field ID”, which is “cultivated land” in the spectral change rate time-series data 11b-1, and the farm field corresponding to the “farm field ID” is “cultivated”. Generate and output a map indicating that it is “Ground”. Further, for example, the output unit 12d associates the actual map data with the “farm field abandoned land” in the spectrum change rate time-series data 11b-1, and displays a map indicating “abandoned farmland”. Generate and output. That is, the output unit 12d outputs a list of information indicating whether the farm field corresponding to each “farm field ID” is “cultivated land” or “cultivated abandoned land” in the map. In this way, the user can check whether each field is “cultivated land” or “cultivated abandoned land” by referring to the output map.

[Cultivation Abandoned Land Determination Processing According to Example 1]
FIG. 3 is a flowchart illustrating an example of a procedure of the abandoned farmland determination process according to the first embodiment. First, the production | generation part 12b of the control part 12 of the cultivation abandonment land determination apparatus 10 produces | generates the spectrum change rate time series data 11b-1 from the spectrum time series data 11a-1 (step S11). Next, the determination unit 12c of the control unit 12 of the abandoned farmland determination device 10 selects one “farm field ID” that has not been selected in the spectrum change rate time-series data 11b-1 (step S12).

  Next, the determination unit 12c includes in the spectrum change rate time-series data 11b-1 corresponding to the selected “field ID” whether there is data in which the change rate decreases by a predetermined threshold or more (for example, a decrease of 20%). It is determined whether or not (step S13). When the determination unit 12c determines that there is data in which the change rate decreases by a predetermined threshold value or more in the spectrum change rate time-series data 11b-1 corresponding to the selected “field ID” (Yes in step S13), the “field ID” ] Is determined to be cultivated land (step S14). On the other hand, when the determination unit 12c determines that there is no data in which the change rate decreases more than a predetermined threshold in the spectrum change rate time-series data 11b-1 corresponding to the selected field ID (No in step S13), the “field” The field “ID” is determined to be an abandoned farmland (step S15). The cultivation abandonment land determination apparatus 10 moves a process to step S16, when step S14 or step S15 is complete | finished.

  In step S <b> 16, the determination unit 12 c determines whether there is an “field ID” that is not selected in step S <b> 12. If it is determined that there is an unselected “farm field ID” in step S12 (step S16 Yes), the determination unit 12c moves the process to step S12. On the other hand, if the determination unit 12c determines in step S12 that there is no unselected “field ID” (No in step S16), the determination unit 12c moves the process to step S17. In step S <b> 17, the output unit 12 d of the control unit 12 of the abandoned farmland determination device 10 combines the determination result corresponding to each “farm field ID” with the actual map and outputs the result.

[Output Mode of Abandoned Cultivated Land Determination Processing Result According to Embodiment 1]
FIG. 4 is a diagram illustrating an example of an output mode of the abandoned farmland determination processing result according to the first embodiment. The abandoned cultivated land determination device 10 synthesizes and outputs “field ID (or information on the field name, location, etc.)” and “cultivated land” or “cultivated abandoned land” on the map display of each field in the map M1. To do. For example, in the map M1, the fields “F001” to “F003” and “F006” to “F009” are “cultivated land”, and the fields “F004” to “F005” are “abandoned farmland”. I understand. In this way, by composing and displaying the cultivated land or the abandoned cultivated land on the map, the user can easily determine which field is the cultivated land or the abandoned cultivated land while referring to the map. can do.

[Knowledge that will be the background for judging abandoned farmland]
Hereinafter, the knowledge which becomes the background of cultivation abandonment land determination is demonstrated with reference to FIG. 5A-FIG. 5E. FIG. 5A is a diagram illustrating an example of an intensity distribution of a reflection spectrum of soil. As for the spectrum of the soil, the distributions of the reflection spectrum intensities of “G”, “R”, “NIR”, and “SWIR” differ depending on the moisture content of the soil, the soil composition, and the like. For example, in FIG. 5A, the soil whose reflection spectrum intensity of “G” to “SWIR” is approximately “60” is “black soil” because of its soil composition. Further, for example, in FIG. 5A, the soil whose reflection spectrum intensities “G” to “SWIR” are approximately “90” to “100” is “moist soil” because of its water content. Further, for example, in FIG. 5A, the soil in which the reflection spectrum intensities of “G” to “SWIR” are approximately “100” to “110” is “dry soil” due to its water content. However, as can be seen from FIG. 5A, in the soil, the distributions of the reflection spectrum intensities of “G”, “R”, “NIR”, and “SWIR” are substantially constant at values according to the water content, soil composition, and the like.

  However, as shown in FIGS. 5B to 5D, the reflection spectrum intensity of the crop in the field shows a shape different from the reflection spectrum intensity of the soil. FIG. 5B is a diagram illustrating an example of an intensity distribution of a reflection spectrum of a wheat field. FIG. 5C is a diagram illustrating an example of an intensity distribution of a reflection spectrum of a potato field. FIG. 5D is a diagram illustrating an example of an intensity distribution of a reflection spectrum of a sugar beet field. The intensity distribution of each reflection spectrum varies depending on the type of crop in the field, or even in the same crop type, depending on the individual field. However, in all of the wheat field, the potato field, and the sugar bean field, the intensity of the reflection spectrum of “NIR” is the strongest, followed by the intensity of the reflection spectrum of “G”, and then the intensity of the reflection spectrum of “SWIR” is strong. The reflection spectrum intensity of “R” is the weakest.

  And, comparing the cultivated land and the abandoned cultivated land from the viewpoint of the reflected spectrum intensity of the field, the cultivated land, when viewed from the sky, is reflected spectrum intensity from after crop harvesting until seeding and seedling planting of the next crop. There is a period when the reflection spectrum intensity of the soil is obtained. Therefore, the timing at which at least one of the reflection spectrum intensities “G”, “R”, “NIR”, and “SWIR” changes to the reflection spectrum intensity of the soil in the time series change of the reflection spectrum intensity is detected. Thereby, it is determined that the crop has been harvested in the field, that is, the field is cultivated land.

  FIG. 5E is a diagram illustrating an example of an intensity distribution of a reflection spectrum of grassland. In FIG. 5E, for example, a broken line with black circles indicates the distribution of the reflection spectrum intensity of the green grassland, and a broken line with the hollow circles indicates the distribution of the reflection spectrum intensity of the withered yellow grassland. For example, if the field showing the distribution of the reflection spectrum intensity as shown in FIG. 5E is an abandoned farmland and the field is overgrown with grass all year round, the grass grows lushly in the summer and withers yellow in the winter. It is thought that it becomes. Therefore, in the field where grass grows all year round, the reflection spectrum distribution changes between summer and winter, but there is no period in which the soil reflection spectrum is obtained. That is, in the time-series change of the reflection spectrum intensity, when the timing at which at least one reflection spectrum intensity of “G”, “R”, “NIR”, and “SWIR” becomes the reflection spectrum intensity of the soil cannot be detected, the following determination is made. To do. That is, it is determined that no crop is harvested in the field, that is, the field is an abandoned farmland.

  Based on the above knowledge, it is possible to determine the abandoned cultivated land using the time-series change of the reflection spectrum intensity obtained from an artificial satellite or the like. For example, in the cultivated land, the reflection spectrum intensity of “NIR” is the strongest, and the rate of change with respect to the reflection spectrum intensity of “NIR” of the soil exposed after the crop is harvested in the field is most remarkable. Therefore, in Example 1, the abandoned farmland determination process is performed by paying attention to the time series of the reflection spectrum intensity of “NIR”. However, even with a time series of reflection spectrum intensities other than “NIR”, it is possible to perform abandoned farmland determination processing by appropriately setting a determination threshold for the rate of change with respect to the reflection spectrum intensity.

[Effects of Example 1]
According to the first embodiment, for example, it is possible to easily discriminate the cultivated land and the abandoned cultivated land on the field from the time-series data of the change rate of the reflection spectrum intensity by remote sensing with higher determination accuracy.

  In the first embodiment, it is determined whether or not the farm field corresponding to the “farm field ID” is an abandoned farmland based on “NIR change rate”. However, not only “NIR change rate” but also “NIR / R change rate” may be used to determine whether or not the field corresponding to the “field ID” is an abandoned farmland. As described above, this is based on the knowledge that the intensity of the reflection spectrum of “NIR” is the strongest and the intensity of the reflection spectrum of “R” is the weakest in any crop field. That is, by obtaining the intensity ratio of the reflection spectrum of “NIR / R”, the change in the intensity of the reflection spectrum of “NIR” can be more emphasized. Hereinafter, a second embodiment in which it is determined whether or not the farm field corresponding to the “farm field ID” is an abandoned farmland based on “NIR / R change rate” will be described.

[Configuration of Cultivated Land Determination Device According to Example 2]
As illustrated in FIG. 1, the abandoned farmland determination device 10-2 according to the second embodiment includes a storage unit 11-2 instead of the storage unit 11 as compared to the abandoned farmland determination device 10 according to the first embodiment. It has control part 12-2 instead of control part 12. Moreover, as shown in FIG. 1, the cultivation abandonment land determination apparatus 10-2 has a spectrum data change rate time series data storage unit 11b2 instead of the spectrum data change rate time series data storage unit 11b. The spectrum data change rate time series data storage unit 11b2 stores spectrum data change rate time series data 11b-2.

  Moreover, as shown in FIG. 1, the control part 12-2 of the cultivation abandonment land determination apparatus 10-2 has the production | generation part 12b-2 instead of the production | generation part 12b, and determines the determination part 12c- instead of the determination part 12c. 2 Other configurations of the abandoned farmland determination device 10-2 are the same as those of the abandoned farmland determination device 10.

[Time Series Data of Spectral Intensity Change Rate According to Example 2]
FIG. 6 is a diagram illustrating an example of time-series data of the spectral intensity change rate according to the second embodiment. In the spectrum change rate time-series data 11b-2, a plurality of “periods”, “NIR / R”, and “NIR / R change rates” are associated with each “field ID”. In the example of FIG. 6, “after one month” to “after six months” every 30 days (one month) after a predetermined “reference date” and “reference date” are set as one “period”. Then, for each “period”, an intensity ratio “NIR / R” is calculated by dividing “NIR reflection spectrum intensity” by “R reflection spectrum intensity” in each “period”. Then, “NIR / R” of each “period” is associated with the rate of change “NIR / R change rate” of “NIR reflection spectrum intensity” with respect to “NIR reflection spectrum intensity” of the previous period, It forms time-series data of the spectrum change rate in each “field ID”.

  In FIG. 6, for example, when “Field ID” is “F001”, “NIR / R” on “reference date” is “1.48”, and “NIR / R” on “after one month” is “ 1.04 ". The “NIR change rate” at “1 month later” is “70.5%”. This “70.5%” is “NIR / R” “1.04” “after 1 month” in “Field ID” “F001”, “NIR / R” “1.48” on “reference date”. The percentage divided by. Further, for example, in the case of “Agricultural field ID” “F001”, “NIR / R change rate” in “6 months later” is “91.6%”. This “91.6%” is “NIR / R” “1.35” after “6 months” in “Field ID” “F001”, and “NIR / R” “1.48” after “5 months”. "Percentage divided by".

  It returns to description of the structure of cultivation abandonment land determination apparatus 10-2. The generation unit 12b-2 generates spectrum change rate time series data 11b-2 from the spectrum time series data 11a-1 stored in the spectrum time series data storage unit 11a. For example, the generation unit 12b-2 converts the “NIR reflection spectrum intensity” of each “period” in the spectrum time-series data 11a-1 of the reflection spectrum intensity of each band corresponding to each “farm field ID” to the “R reflection spectrum. Divide by “strength” to generate “NIR / R” time-series data. “NIR / R” is an intensity ratio. Then, the generation unit 12b-2 calculates the ratio of “NIR / R” in each “period” to “NIR / R” in the previous period immediately before each “period”. In this way, the generation unit 12b-2 performs time-series data of the ratio (change rate) of “NIR / R” with respect to the previous period of each “period” (spectrum change rate time-series data 11b-2 (see FIG. 6)). ) Is generated. The generation unit 12b-2 stores the generated spectrum change rate time series data 11b-2 in the spectrum change rate time series data storage unit 11b2.

  The determination unit 12c-2 refers to the spectrum change rate time series data 11b-2 generated by the generation unit 12b-2, and among the time series data of “NIR / R change rate” for each “field ID”, It is determined whether or not there is a “period” in which the change rate decreases by, for example, 20% or more. That is, the presence of “period” in which the “rate of change of NIR / R” decreases by, for example, 20% or more has resulted in a state where the crop is harvested and the soil is exposed in the field corresponding to the “field ID”. It shows that.

  Then, the determination unit 12c-2 determines that the field corresponding to the “farm field ID” is “cultivation” for the “farm field ID” in which the “period” in which the “change rate of NIR / R” decreases by, for example, 20% or more. It is determined that it is “Earth”. For example, in FIG. 6, when “Farm ID” “F001” in FIG. 6, the “NIR / R change rate” at “1 month later” is “70.5%”, and the determination unit 12 c-2 It is determined that the rate is a decrease of, for example, 20% or more. In the example of FIG. 6, “◎” is added to the column “change rate is greater than or equal to threshold”, and “cultivated land determination result” is “field ID” “F001” to “F003”, “F009” of “cultivated land”. Is applicable.

  On the other hand, the determination unit 12c-2 determines that the field corresponding to the “field ID” is “abandoned farming” for the “field ID” in which there is no “period” in which the rate of change in the spectrum intensity decreases by, for example, 20% or more. It is determined that For example, in FIG. 6, when the “farm ID” is “F004” and “F005” in FIG. 6, the determination unit 12 c-2 reduces “NIR / R change rate” by, for example, 20% or more. ] Does not exist. In the example of FIG. 6, the “field ID” in which “◎” is not added to any “period” in the column “change rate is greater than or equal to the threshold value”, and “cultivated land determination result” is “abandoned farmland”. “F004” and “F005” are applicable.

  The determination unit 12c-2 adds the result of the abandoned farmland determination process to the spectrum change rate time-series data 11b-2. And the determination part 12c-2 performs each cultivation abandonment land determination process about each "farm field ID".

[Abandoned Cultivated Land Determination Processing in Example 2]
FIG. 7 is a flowchart illustrating an example of the procedure of the abandoned farmland determination process according to the second embodiment. The abandoned farmland determination process according to the second embodiment is different from the abandoned farmland determination process according to the first embodiment in the following points. That is, the cultivation abandonment land determination process according to the second embodiment executes step S11-2 instead of step S11. Moreover, the cultivation abandonment land determination process which concerns on Example 2 performs step S13-2 instead of step S13. Other processes in the second embodiment are the same as those in the first embodiment.

  First, the generation unit 12b-2 of the control unit 12-2 of the cultivation abandoned land determination device 10-2 uses the spectrum time-series data 11a-1 stored in the spectrum time-series data storage unit 11a for each “field ID”. The time series data of the spectrum intensity ratio of “NIR / R” for each “period” is calculated. Then, the generation unit 12b-2 calculates time series data of the rate of change of the spectrum intensity ratio of “NIR / R” of each “period” that precedes and follows in the time series data, and obtains the spectrum change rate time series data 11b-2. Generate. And the production | generation part 12b-2 stores in the produced | generated spectrum change rate time series data storage part 11b2 (step S11-2). The cultivation abandonment land determination apparatus 10-2 moves a process to step S12, after step S11-2 is complete | finished.

  Further, the determination unit 12c-2 refers to the spectrum change rate time-series data 11b-2, and the change rate of the “NIR / R change rate” for each “field ID” is 20% or more, for example. It is determined whether or not there is a “period” that decreases (step S13-2). When the determination unit 12c-2 determines that there is a “period” in which the change rate decreases by, for example, 20% or more among the “NIR / R change rate” time-series data for each “field ID” ( Step S13-2 Yes), and moves to step S14. On the other hand, if the determination unit 12c-2 determines that there is no “period” in which the change rate decreases by, for example, 20% or more (No in step S13-2), the process proceeds to step S15.

[Effects of Example 2]
According to Example 2, for example, with respect to a crop having a relatively large intensity ratio of the reflection spectrum intensity of NIR to the reflection spectrum intensity of “R”, the abandonment of cultivation from the time-series data of the change rate of the reflection spectrum intensity by remote sensing The discrimination of the ground becomes possible with higher accuracy. For example, referring to FIG. 5B to FIG. 5D, the NIR / R intensity ratio in the reflection spectrum intensity distribution of the potato field and the sugar beet field is relatively large as compared with the reflection spectrum intensity distribution of the wheat field. For this reason, Example 2 can raise determination accuracy more, for example, when performing the cultivation abandonment land determination of a potato field and a lady field.

[Modification of Embodiments 1 and 2]
In the first and second embodiments, based on “NIR change rate” and “NIR / R change rate”, it is determined whether or not the field corresponding to the “field ID” is an abandoned farmland. However, the present invention is not limited to this, and the “field ID” depends on whether there is a “period” in which any of “G change rate”, “R change rate”, and “SWIR change rate” is equal to or greater than a predetermined threshold. It may be determined whether or not the field corresponding to is “cultivated land”. In this case, the generation unit 12b obtains the change rate of “G change rate”, “NIR change rate”, “R change rate”, and “SWIR change rate” for each “field ID” from the spectrum time series data 11a-1. Spectral change rate time-series data 11b-1 having time-series data is generated.

  In the first and second embodiments, each “period” is a timing at which the reflection spectrum intensity of the ground surface is periodically collected every 30 days (one month) from a predetermined “reference date”. However, the present invention is not limited to this, and may be arbitrarily set every day according to the crop cultivation schedule or the like.

  In Examples 1 and 2, “period” of the spectrum time series data 11a-1 and the spectrum change rate time series data 11b-1 (11b-2) is “1 month later” to “6 months later” from the “reference date”. "6 periods" is defined as one determination period. However, the present invention is not limited thereto, and an arbitrary determination period may be appropriately set according to a crop cultivation schedule or the like, and an abandoned farmland may be determined based on time-series data corresponding to the determination period. Furthermore, even if the field is determined to be abandoned farmland, the field is not cultivated for the past year and is not planned to be cultivated for the next several years (up to 3 years). It may be determined whether or not it is an abandoned farmland based on time-series data for a sufficient determination period. The sufficient determination period is, for example, 1 year + 3 years = 4 years = 12 periods × 4 years = maximum 48 periods.

  In Examples 1 and 2, in the spectrum change rate time-series data 11b-1 (11b-2), whether or not there is a “period” in which the change rate of the spectrum intensity decreases by, for example, 20% or more, It is determined whether or not the field corresponding to the “field ID” is “cultivated land”. However, the present invention is not limited to this. Depending on whether or not there is a “period” in which the change rate of the spectrum intensity is increased (increased) by, for example, 20% or more in the spectral change rate time-series data 11b-1 (11b-2). It may be determined whether or not the field corresponding to the “field ID” is “cultivated land”. The “period” in which the rate of change of the spectrum intensity increases by 20% or more, for example, corresponds to “after 4 months” of “Field ID” “F001” shown in FIG. 2B. That is, in the spectrum change rate time-series data 11b-1 (11b-2), when there is a “period” in which the change rate of the spectrum intensity increases by, for example, 20% or more, it corresponds to the “field ID”. It is determined that the farm field to be “cultivated land”. On the other hand, in the spectrum change rate time-series data 11b-1 (11b-2), when there is no “period” in which the change rate of the spectrum intensity increases by, for example, 20% or more, it corresponds to the “field ID”. It may be determined that the farm field to be “abandoned farmland”. The presence of a “period” in which the rate of change in spectral intensity increases by, for example, 20% or more is a state in which the crop is seeded or seeded in the field corresponding to the “field ID” and the soil exposure decreases as the crop grows. It shows that it became.

  In Examples 1 and 2, in the spectrum change rate time-series data 11b-1 (11b-2), whether or not there is a “period” in which the change rate of the spectrum intensity decreases by, for example, 20% or more, It is determined whether or not the field corresponding to the “field ID” is “cultivated land”. However, in the spectrum change rate time-series data 11b-1 (11b-2), is there a “period” in which the change rate of the spectrum intensity is, for example, a decrease of 20% or more and an increase of, for example, 20% or more? Whether or not it is “cultivated land” may be determined depending on whether or not. That is, in the spectrum change rate time-series data 11b-1 (11b-2), there are both “period” in which the change rate of the spectrum intensity is reduced by, for example, 20% or more and “period” in which, for example, the increase is by 20% or more. In the case of performing, the field corresponding to the “field ID” is determined as “cultivated land”. On the other hand, when there is no “period” in which the rate of change of the spectrum intensity is, for example, a decrease of 20% or more or “period” in which, for example, the increase is 20% or more, there is no field corresponding to the “field ID”. It may be determined that the land is abandoned cultivation.

  In the second embodiment, it is determined whether or not the farm field corresponding to the “farm field ID” is an abandoned farmland based on “NIR / R change rate”. The third embodiment searches for the existence of time-series data of a change rate at which “change rate of NIR / R” becomes a decrease rate and an increase rate that are equal to or greater than a predetermined threshold. In the third embodiment, in the search process, the field corresponding to the “field ID” is determined based on the appearance pattern of the timing at which the “change rate of NIR / R” becomes a decrease rate and an increase rate equal to or greater than a predetermined threshold. When it is cultivated land, it is estimated which crop field it is.

[Configuration of Cultivated Land Determination Device According to Example 3]
FIG. 8 is a block diagram illustrating an example of a functional configuration of the abandoned farmland determination device according to the third embodiment. As illustrated in FIG. 8, the abandoned farmland determination device 10-3 according to the third embodiment is a storage unit instead of the storage unit 11-2, as compared to the abandonment farmland determination device 10-2 according to the second embodiment. 11-3 and a control unit 12-3 instead of the control unit 12-2. The storage unit 11-3 further includes a field state determination reference data storage unit 11d. The field condition determination reference data storage unit 11d stores field condition determination reference data 11d-1 and 11d-2. Moreover, the control part 12-3 of the cultivation abandonment land determination apparatus 10-3 has the determination part 12c-3 instead of the determination part 12c-2, as shown in FIG. The other structure of the cultivation abandonment land determination apparatus 10-3 is the same as that of the cultivation abandonment land determination apparatus 10-2.

[Field condition determination reference data according to Example 3]
FIG. 9A and FIG. 9B are diagrams illustrating an example of field state determination reference data according to the third embodiment. As shown in FIG. 9A, the field condition determination reference data 11d-1 includes “NIR / R change rate pattern 1”, “NIR / R change rate pattern 2”, and “NIR / R change rate pattern” in each “period”. 3 ”is stored in association with“ farm field state ”. In FIG. 9A, for example, “◎” indicates that the “rate of change of NIR / R” is reduced by, for example, 20% or more compared to the previous “period”. In FIG. 9A, for example, “◯” indicates that “NIR / R change rate” is increased by, for example, 20% compared to the previous “period”.

  FIG. 9A shows, for example, that “NIR / R change rate pattern 1” is “◎” after “1 month” and “O” after “4 months”, and the field is “wheat”. Indicates that the farm field Then, wheat is harvested “after one month”, sowed or seedlings are planted after “four months”, and between “after one month” and “after three months” indicates soil exposure.

  Similarly, in FIG. 9A, for example, if “NIR / R change rate pattern 2” is “◎” in “after 2 months” and “◯” in “after 5 months”, the field is "Indicates that the field is farming. Then, the potato is harvested at “2 months later”, and the potato is seeded or seeded at “after 5 months”, and the period between “after 2 months” to “after 4 months” indicates soil exposure.

  Similarly, in FIG. 9A, for example, if “NIR / R change rate pattern 3” is “◎” in “after 3 months” and “◯” in “after 5 months”, the field is “Tensai”. "Indicates that the field is farming. Then, the potato is harvested “after 3 months”, and the potato is seeded or seeded after “5 months”, and “after 3 months” to “after 4 months” indicate soil exposure.

  Further, as shown in FIG. 9B, the field condition determination reference data 11d-2 associates “field state” with “NIR / R pattern 1” and “NIR / R pattern 2” in each “period”. Remember. In FIG. 9B, “NIR / R pattern 1” is “1.00”, “0.97” to “0.96”, that is, “1” in “reference date”, “1 month later” to “6 months later”, respectively. If it is about “within 0.000 ± 0.005”, it indicates that the field is “abandonment of farming (wasteland)”.

  Further, in FIG. 9B, “NIR / R pattern 2” is “1.71”, “1.75” to “1.41” in “reference date”, “1 month later” to “6 months later”, respectively. If present, it indicates that the field is “abandonment of cultivation (grassland)”. In Example 3, if the “NIR / R” value of the field to be determined is about “within 1.00 ± 0.005”, it is determined that the field is “abandonment of farming (wasteland)”. To do. In Example 3, if the “NIR / R” value of the field to be judged is not about “within 1.00 ± 0.005”, the field is “abandoned farming (grassland)”. Assume that there is. However, the present invention is not limited to this, and “NIR / R” is “1.71”, “1.75” to “1.41” in the “reference date”, “1 month later” to “6 months later”, and a predetermined error. If the patterns match within the range, the field may be determined to be “abandonment of cultivation (grassland)”. Furthermore, “NIR / R” does not match “1.71” “1.75” to “1.41” within the predetermined error range in “reference date” “after 1 month” to “after 6 months”. If the pattern is a pattern, it may be determined that the field is “abandonment of farming (others)”.

  The field condition determination reference data 11d-1 and 11d-2 are based on the knowledge obtained by analyzing the accumulated data of the spectrum time-series data 11a-1 for which the field condition is known in advance. It is generated as data indicating the characteristics of the field condition. The field condition determination reference data 11d-1 and 11d-2 are stored in advance in the field condition determination reference data storage unit 11d.

  Returning to the description of the configuration of the cultivation abandonment land determination device 10-3. The determination unit 12c-3 refers to the spectrum change rate time series data 11b-2 generated by the generation unit 12b-2, and among the time series data of “NIR / R change rate” for each “field ID”, It is determined whether or not there is a “period” in which the change rate decreases by, for example, 20% or more. That is, the presence of “period” in which the “rate of change of NIR / R” decreases by, for example, 20% or more has resulted in a state where the crop is harvested and the soil is exposed in the field corresponding to the “field ID”. It shows that. Further, the determination unit 12c-3 determines that the rate of change increases by, for example, 20% or more among time-series data in which the rate of change of NIR / R decreases by, for example, 20% or more. It is determined whether or not “period” exists.

  Then, the determination unit 12c-3 determines that there is a change rate at which the "NIR / R change rate" decreases by, for example, 20% or more, and the "NIR / R change rate" increases by, for example, 20% or more. A change rate appearance pattern is determined in time-series data of a change rate in which a change rate exists. That is, the determination unit 12c-3 determines, for example, which crop field the pattern of appearance of the change rate that decreases by 20% or more and the change rate that increases by 20% or more corresponds to the pattern indicating the field state. The determination is made with reference to the state determination reference data 11d-1.

  For example, the “period” corresponding to the rate of change in which the “NIR / R change rate” decreases by, for example, 20% or more is “after one month”, and the “NIR / R change rate” is, for example, 20% or more. Consider the case where the “period” corresponding to the increasing rate of change is “after 4 months”. In this case, the determination unit 12c-3 determines that the field corresponding to the “field ID” is “wheat field” and “cultivated land”. The same determination is made for the “potato field” and “tensai field”.

  “NIR / R change rate” corresponds to, for example, a change rate that decreases 20% or more, for example, “period” and “NIR / R change rate”, for example, corresponds to a change rate that increases 20% or more, for example. Consider a case where the “period” to be present does not exist in the field condition determination reference data 11d-1. In this case, the determination unit 12c-3 determines that the field corresponding to the “field ID” is “cultivated land” of “other crops” other than wheat, potato, and sugar beet.

  In addition, time-series data in which there is no change rate at which “change rate of NIR / R” decreases by, for example, 20% or more, or change rate at which “change rate of NIR / R” increases, for example, by 20% or more. Think. In this case, the determination unit 12c-3 determines whether or not “period” in which “NIR / R” is within 1.00 ± 0.05 is present in the time-series data. In the time series data, the determination unit 12c-3, when there is a “period” in which “NIR / R” is within 1.00 ± 0.05, for example, the field corresponding to the “field ID” is Judged as abandoned farmland (wasteland). Further, in the time series data, the determination unit 12c-3 corresponds to the “field ID” when “period” in which “NIR / R” is within 1.00 ± 0.05, for example, does not exist. It is determined that the field is abandoned cultivation (grassland).

  The output unit 12d generates a result of the abandoned farmland determination process based on the spectrum change rate time-series data 11b-2 to which the result of the abandoned farmland determination process is added, and outputs the result to the output device 2. For example, the output unit 12d associates actual map data with “field ID”, which is “wheat cultivated land” in the spectrum change rate time-series data 11b-2, and the field corresponding to the “field ID” is “ Outputs a map indicating that it is a wheat cultivated land.

  Further, for example, the output unit 12d associates an actual map with “agricultural field ID”, which is set as “abandoned farmland (wasteland)” in the spectrum change rate time-series data 11b-2, and corresponds to the “farm field ID”. A map indicating that the farm field to be used is “abandoned farmland (wasteland)” is output.

  In other words, the output unit 12d has the fields corresponding to the “field IDs” as “wheat cultivated land”, “potato cultivated land”, “tensai cultivated land”, “cultivated land of other crops”, “abandoned cultivated land (wasteland)”, and “cultivated land”. The information indicating whether the abandoned land (grassland) is associated with the map and output as a list. In this way, the user can refer to the output map so that each field is “wheat cultivated land”, “potato cultivated land”, “tensai cultivated land”, “cultivated land of other crops”, “abandoned cultivated land (wasteland) ) ”Or“ Abandoned farmland (grassland) ”.

[Cultivation Abandoned Land Determination Processing According to Example 3]
FIG. 10 is a flowchart illustrating an example of a procedure of the abandoned farmland determination process according to the third embodiment. The abandoned farmland determination process according to the third embodiment is different from the abandoned farmland determination process according to the second embodiment in the following points. That is, the cultivation abandonment land determination process according to the third embodiment executes step S13-3 in the case of step S13-2 Yes, and executes step S13-5 in the case of step S13-2 No. Moreover, the cultivation abandonment land determination process which concerns on Example 3 performs step S13-4 in step S13-3 Yes, and performs step S13-5 in step S13-3 No.

  Moreover, the cultivation abandonment land determination process which concerns on Example 3 performs step S14-1 in step S13-4Yes, and performs step S14-2 in step S13-4No. Moreover, the cultivation abandonment land determination process which concerns on Example 3 performs step S15-1 in step S13-5 Yes, and performs step S15-2 in step S13-5 No. The cultivation abandonment land determination process according to the third embodiment moves the process to step S16 when the execution of step S14-1, step S14-2, step S15-1, and step S15-2 ends. Other processes in the third embodiment are the same as those in the second embodiment.

  The determination unit 12c-3 of the abandoned cultivated land determination apparatus 10-3 is data in which the change rate is increased by, for example, 20% or more in the spectrum change rate time-series data 11a-1 corresponding to the “farm field ID” selected in step S12. Is determined (step S13-3). If the determination unit 12c-3 determines that there is data in which the change rate increases by, for example, 20% or more (Yes in Step S13-3), the process proceeds to Step S13-4. On the other hand, if the determination unit 12c-3 determines that there is no data in which the change rate increases by, for example, 20% or more (No in step S13-3), the determination unit 12c-3 moves the process to step S13-5.

  In step S13-4, the determination unit 12c-3 determines that the appearance pattern of the data is “wheat”, “barecho”, “yes” in the time-series data determined as Yes in the determination in Step S13-2 and Yes in the determination in Step S13-3. It is determined whether or not it matches any of “Tensai”. When the determination unit 12c-3 determines that the appearance pattern of the data matches any of “wheat”, “barejo”, and “tensai” (Yes in step S13-4), the field corresponding to the “field ID” matches. It is determined that the crop is cultivated land (step S14-1). On the other hand, when the determination unit 12c-3 determines that the appearance pattern of the data does not match any of “wheat”, “barejo”, and “tensai” (No in step S13-4), the field corresponding to the “field ID” is determined. It determines with it being the cultivation land of other crops (step S14-2). When the step S14-1 or the step S14-2 ends, the cultivation abandoned land determination device 10-3 moves the process to step S16.

  Further, in step S13-5, the determination unit 12c-3, based on the field state determination reference data 11d-2, sets each “NIR / R” of the spectrum change rate time-series data corresponding to the “field ID” selected in step S12. ] Is “1.00 ± 0.05”. When the determination unit 12c-3 determines that each “NIR / R” of the spectrum change rate time-series data corresponding to the “field ID” selected in step S12 is “within 1.00 ± 0.05”, for example. (Step S13-5 Yes), the process is moved to Step S15-1. On the other hand, the determination unit 12c-3 determines that each “NIR / R” of the spectrum change rate time-series data corresponding to the “field ID” selected in step S12 is not “within 1.00 ± 0.05”, for example. If (step S13-5 No), the process proceeds to step S15-2.

  In step S <b> 15-1, the determination unit 12 c-3 determines that the farm field corresponding to the “farm field ID” is “cultivated abandoned land (wasteland)”. On the other hand, in step S15-2, the determination unit 12c-3 determines that the farm field corresponding to the “farm field ID” is “cultivated abandoned land (grassland)”. The cultivation abandonment land determination apparatus 10-3 moves the process to step S16 when step S14-1, step S14-2, step S15-1, and step S15-2 are completed.

[Output Mode of Abandoned Cultivated Land Determination Processing Result According to Example 3]
FIG. 11 is a diagram illustrating an example of an output mode of the abandoned farmland determination processing result according to the third embodiment. The abandoned farmland determination device 10-3 displays “field ID (or information on the field name, location, etc.)” and “wheat farmland” to “abandoned farmland (grassland)” on the map display of each field in the map M2. Are combined and output. For example, in the map M2, the field “F001” is “wheat cultivated land”, the field “F002” is “potato cultivated land”, and the field “F003” is “tensai cultivated land”. . Further, for example, in the map M2, it is understood that the field “F004” is “abandoned farmland (wasteland)” and the field “F005” is “abandoned farmland (grassland)”. Further, for example, in the map M2, it is understood that the fields “F006” to “F009” are “cultivated land of other crops”. In this way, by composing and displaying the wheat cultivated land to the abandoned cultivated land (grassland) on the map, the user can easily determine which field is the cultivated land of any crop or the abandoned cultivated land. Can be determined.

  In the third embodiment, “NIR / R change rate” is used, but instead of “G change rate”, “R change rate”, “NIR change rate”, and “SWIR change rate” May be used.

[Effects of Example 3]
According to the third embodiment, the farm abandonment is judged and the type of the cultivated crop in the field is estimated if it is cultivated land. If the farm abandoned land is estimated whether it is wasteland or grassland, Can be grasped in more detail.

  In addition, each threshold value shown by the above Examples 1-3 is only an example, and according to the geographical conditions of the field, the growth conditions of the crops to be cultivated, etc. Appropriate values can be set for more precise estimation of crops. Further, for each “change rate” shown in the first to third embodiments, each previous period ratio in each time series data is used as time series data of the change rate. However, not limited to this, the reciprocal of each previous period, that is, each ratio obtained by dividing the spectrum intensity of the previous period for each "period" in each time series data by the spectrum intensity of each "period" as the time series data of the rate of change. Also good. In this case, the threshold for determining abandoned land and estimating crops is also appropriately determined according to each ratio obtained by dividing the spectral intensity of the previous period for each "period" by the spectral intensity of each "period" in each time-series data. . Instead of “change rate”, “change amount”, that is, “increase amount (rise amount)” and “decrease amount” may be used. The “change amount” is time series data of “change amount” obtained by subtracting the spectrum intensity of each “period” from the spectrum intensity of the previous period for each “period” in each time series data. In this case as well, the thresholds for determining abandoned farmland and estimating crops are appropriately determined according to the difference obtained by subtracting the spectrum intensity of each “period” from the spectrum intensity for each “period” in each time-series data. It is done.

  In Examples 1 to 3, the abandoned farmland determination devices 10 to 10-3 provide the above abandoned farmland determination service in a stand-alone manner that independently executes the above abandoned farmland determination program without depending on external resources. Although the case where it does is illustrated, other mounting forms are also employable. For example, as shown in FIG. 12, in the fourth embodiment, the abandoned farmland determination service described above connected to the input / output terminal devices 3-1 to 3-n for executing input / output processing for the user via the network N. An abandoned farmland determination device 50 as a server device that provides Thereby, cultivation abandonment land determination system S can also be constructed as a client server system. In this case, the cultivation abandonment land determination device 50 can be implemented by installing a cultivation abandonment land determination program that realizes the above-described cultivation abandonment land determination service as package software or online software. For example, the cultivation abandoned land determination device 50 may be implemented as a Web server that provides the above-described cultivation abandoned land determination service, or may be implemented as a cloud that provides the above-mentioned cultivation abandoned land determination service through outsourcing. It doesn't matter. For example, the time-series data of the reflection spectrum intensity for each position information obtained by remote sensing is input from the input / output terminal devices 3-1 to 3-n and transmitted to the abandoned farmland determination device 50. And the cultivation abandonment land determination apparatus 50 produces | generates the spectrum time series data 11a-1 from the time series data of the received reflection spectrum intensity | strength, and performs cultivation abandonment land determination processing based on the spectrum time series data 11a-1. And the cultivation abandonment land determination apparatus 50 may transmit the cultivation abandonment land determination processing result to the input / output terminal devices 3-1 to 3-n.

[Abandoned farmland judgment program]
The various processes described in the above embodiments can be realized by executing a prepared program on a computer such as a personal computer or a workstation. Therefore, in the following, an example of a computer that executes a farm abandoned land determination program having the same function as that of the above embodiment will be described with reference to FIG.

  FIG. 13: is a figure which shows an example of the hardware constitutions of the computer which executes the cultivation abandonment land determination program which concerns on Examples 1-3. As illustrated in FIG. 13, the computer 100 includes an operation unit 110, a display 120, and a communication unit 130. Further, the computer 100 includes a CPU 150, a ROM 160, an HDD 170, and a RAM 180. These units 110 to 180 are connected via a bus 140.

  As shown in FIG. 13, the HDD 170 includes an input unit 12a, a generation unit 12b (12b-2), a determination unit 12c (12c-2, 12c-3), and an output unit 12d described in the first to third embodiments. Abandoned farmland determination program 170a that performs the same function as is stored. This farming abandonment land determination program 170a includes the input unit 12a, the generation unit 12b (12b-2), the determination unit 12c (12c-2, 12c-3), and the output unit 12d described in the first to third embodiments. As with the components, they may be integrated or separated. That is, the HDD 170 does not necessarily have to store all the data shown in the first to third embodiments, and it is sufficient that data used for processing is stored in the HDD 170.

  Under such an environment, the CPU 150 reads the cultivation abandoned land determination program 170 a from the HDD 170 and develops it in the RAM 180. As a result, the cultivation abandonment land determination program 170a functions as a cultivation abandonment land determination process 180a as shown in FIG. This abandoned cultivated land determination process 180a expands various data read from the HDD 170 to an area allocated to the abandoned cultivated land determination process 180a in the storage area of the RAM 180, and performs various processes using the expanded various data. Run. For example, the process shown in FIGS. 3, 7, and 10 is included as an example of the process executed by the abandoned farmland determination process 180a. In the CPU 150, all the processing units shown in the first to third embodiments are not necessarily operated, and a processing unit corresponding to the process to be executed may be virtually realized.

  Note that the abandoned farmland determination program 170a does not necessarily have to be stored in the HDD 170 or the ROM 160 from the beginning. For example, each program is stored in a “portable physical medium” such as a flexible disk inserted into the computer 100, so-called FD, CD-ROM, DVD disk, magneto-optical disk, or IC card. Then, the computer 100 may acquire and execute each program from these portable physical media. In addition, each program is stored in another computer or server device connected to the computer 100 via a public line, the Internet, a LAN, a WAN, etc., and the computer 100 acquires and executes each program from these. It may be.

S cultivation abandoned land determination system N network 3-1,..., 3-n input / output terminal devices 10, 10-2, 10-3, 50 cultivation abandoned land determination devices 11, 11-2, 11-3 11a Spectral time series data storage unit 11a-1 Spectral time series data 11b, 11b2 Spectral change rate time series data storage units 11b-1, 11b-2 Spectral change rate time series data 11c Field information storage unit 11d Field state determination reference data storage Unit 11d-1, 11d-2 Field condition determination reference data 12, 12-2, 12-3 Control unit 12a Input unit 12b, 12b-2 Generation unit 12c, 12c-2, 12c-3 Determination unit 12d Output unit

Claims (10)

Computer
Processing to input time-series data relating to the intensity of one or more wavelength regions;
Depending on whether or not the input time-series data includes a section in which the intensity is greater than or equal to a predetermined change amount or a predetermined change rate before and after, the field associated with the time-series data is abandoned A method for determining whether abandoned cultivated land is provided.   Whether or not the field is abandoned cultivation depends on whether or not there is a section in the time-series data in which the amount of decrease or rate of decrease is less than or equal to a predetermined threshold. The method for determining abandoned cultivated land according to claim 1, wherein:   Whether or not the field is abandoned farming depends on whether or not there is a section in the time-series data in which the amount of increase or rate of increase is greater than or equal to a predetermined threshold in the time series data. It is determined, The cultivation abandonment land determination method of Claim 1 or 2 characterized by the above-mentioned.   In the time-series data, the determination process includes a section in which the amount of decrease in intensity is less than or equal to a first change amount before or after, or the interval in which the decrease rate of intensity is less than or equal to a first change rate, and It is determined whether or not the field is an abandoned farmland depending on whether or not there are both sections in which the amount of increase in intensity is greater than or equal to the second change amount or the rate of increase in intensity is greater than or equal to the second change rate. The farm abandoned land determination method according to claim 1, wherein: The input process inputs time-series data relating to the intensity of the first wavelength region and time-series data relating to the intensity of the second wavelength region,
The determination process is the intensity ratio between the first wavelength region and the second wavelength region obtained from time-series data regarding the intensity of the first wavelength region and time-series data regarding the intensity of the second wavelength region. 2. The method according to claim 1, wherein whether or not the field is an abandoned cultivation area is determined based on whether or not there is a section that is greater than or equal to a predetermined change amount or a predetermined change rate before and after. Cultivated abandoned land judgment method.   The time-series data relating to the intensity of at least one wavelength region of green light, red light, near-infrared light, and short-wavelength infrared light is input as the input processing. Cultivated abandoned land determination method described in 1. The input process inputs the time series data for each of a plurality of fields,
The abandoned farmland determination method according to any one of claims 1 to 6, wherein the determining process determines whether the field is an abandoned farmland for each of the plurality of fields. The computer is
The cultivation abandonment land determination method according to claim 7, further comprising executing a process of associating and outputting a determination result relating to each field to a map including the plurality of fields. On the computer,
Processing to input time-series data relating to the intensity of one or more wavelength regions;
Depending on whether or not the input time-series data includes a section in which the intensity is greater than or equal to a predetermined change amount or a predetermined change rate before and after, the field associated with the time-series data is abandoned A cultivated abandoned land determination program that executes a process for determining whether or not the land is a land. An input unit for inputting time-series data relating to the intensity of one or more wavelength regions;
Depending on whether or not the input time-series data includes a section in which the intensity is greater than or equal to a predetermined change amount or a predetermined change rate before and after, the field associated with the time-series data is abandoned A cultivated abandoned land determination device, comprising: a determination unit that determines whether or not the land is ground.

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