JP2016184232A - Prediction apparatus and prediction method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a prediction apparatus capable of predicting timing of appearance of pests.SOLUTION: A prediction apparatus 100 acquires a piece of information on conditions required for pests to grow and a piece of weather information from a server 20 and AMeDAS 30. Based on the acquired information, the prediction apparatus 100 calculates an integration temperature in which differences between the temperature on the respective days and minimum temperature required for pests to grow, and predicts the growth degree of the pests using the calculated integration temperature and effective integration temperature required for the pests to grow up to larvas or adults.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a prediction device and the like.

  Farmers are required to predict and eliminate the occurrence of pests because growing crops without destroying the pests occurring on the crops causes problems such as yield and quality degradation. For example, workers generally rely on past experiences and intuitions to predict the occurrence of pests, and spray pesticides and the like during the expected period to suppress pest damage. In addition, when the occurrence time of pests is ambiguous, it may be possible to remove the pests by extending the period of pesticide pesticide. However, in recent years, the health awareness of customers has increased, and many pesticides are used. That is not preferable.

JP 2009-106261 A JP 2006-115704 A

  In the above-described conventional technology, there is a problem that the generation time of the pest cannot be predicted.

  In one side, it aims at providing the prediction apparatus and the prediction method which can estimate the generation | occurrence | production time of a pest.

  In the first plan, the prediction device includes an acquisition unit and a prediction unit. The acquisition unit includes meteorological information that associates date, temperature, and location, minimum temperature information required for the growth of the pest, and a first integrated temperature that is required for the pest to move to the next growth stage. And get information. The prediction unit calculates the difference temperature between the temperature and the temperature information for each date, and grows the pest based on the second integrated temperature obtained by integrating the calculated difference temperature for each date and the first integrated temperature. Predict the degree for each location.

  According to one embodiment of the present invention, the generation time of a pest can be predicted.

FIG. 1 is a diagram illustrating the configuration of the system according to the first embodiment. FIG. 2 is a diagram illustrating an example of a data structure of weather information. FIG. 3 is a diagram illustrating an example of a data structure of the second weather information. FIG. 4 is a functional block diagram illustrating the configuration of the prediction apparatus according to the first embodiment. FIG. 5 is a diagram illustrating an example of a data structure of pest growth temperature information. FIG. 6 is a diagram illustrating an example of a prediction result of the prediction unit. FIG. 7 is a diagram (1) illustrating an example of report data regarding pest occurrence prediction. FIG. 8 is a diagram (2) illustrating an example of report data regarding pest occurrence prediction. FIG. 9 is a flowchart illustrating the processing procedure of the prediction apparatus according to the first embodiment. FIG. 10 is a diagram illustrating the configuration of the system according to the second embodiment. FIG. 11 is a functional block diagram illustrating the configuration of the prediction apparatus according to the second embodiment. FIG. 12 is a diagram illustrating an example of a data structure of crop growth temperature information. FIG. 13 is a diagram (1) illustrating an example of report data related to crop harvest time. FIG. 14 is a diagram (2) illustrating an example of report data relating to crop harvest time. FIG. 15 is a flowchart illustrating the processing procedure of the prediction apparatus according to the second embodiment.

  Hereinafter, embodiments of a prediction apparatus and a prediction method disclosed in the present application will be described in detail with reference to the drawings. Note that the present invention is not limited to the embodiments.

  A configuration of the system according to the first embodiment will be described. FIG. 1 is a diagram illustrating the configuration of the system according to the first embodiment. As shown in FIG. 1, this system includes meteorological robots 10 a to 10 f, a server 20, an AMeDAS (AMeDAS: Automated Meteorological Data Acquisition System) 30, and a prediction device 100. The weather robots 10a to 10f are connected to the server 20. The weather robots 10a to 10f are installed at predetermined positions. In the following description, the weather robots 10a to 10f are collectively referred to as the weather robot 10 as appropriate. The server 20, the AMeDAS 30, and the prediction device 100 are connected to the network 50.

  The weather robot 10 is a device that measures temperature, precipitation, wind direction, wind speed, and sunshine duration. The weather robot 10 generates weather information based on the measured information and transmits it to the server 20. FIG. 2 is a diagram illustrating an example of a data structure of weather information. As shown in FIG. 2, the weather information associates robot identification information, date and time, temperature, precipitation, wind direction, wind speed, and sunshine duration. The robot identification information is information that uniquely identifies a weather robot. The date and time indicate the date and time when the corresponding temperature, precipitation, wind direction, wind speed, and sunshine duration were measured.

  The server 20 is a device that acquires and holds weather information from the weather robots 10a to 10f. Each piece of weather information acquired by the server 20 from each of the weather robots 10a to 10f is appropriately described as first weather information. The server 20 transmits the first weather information to the prediction device 100. When the server 20 receives a request for weather information from the prediction device 100, the server 20 may transmit the first weather information to the prediction device 100, or periodically transmit the first weather information to the prediction device 100. You may do it. Although not shown here, the data structure of the first weather information is data in which the weather information shown in FIG.

  The AMeDAS 30 is a system that measures temperature, precipitation, wind direction, wind speed, and sunshine duration. In the following description, information on the temperature, precipitation, wind direction, wind speed, and sunshine duration measured by the AMeDAS 30 will be appropriately described as second weather information. FIG. 3 is a diagram illustrating an example of a data structure of the second weather information. As shown in FIG. 3, the second weather information associates region identification information, date and time, temperature, precipitation, wind direction, wind speed, and sunshine duration. The area identification information is information that uniquely identifies an area. The date and time indicate the date and time when the corresponding temperature, precipitation, wind direction, wind speed, and sunshine duration were measured.

  The AMeDAS 30 transmits the second weather information to the prediction device 100. When the AMeDAS 30 receives a request for the second weather information from the prediction device 100, the AMeDAS 30 may transmit the second weather information to the prediction device 100, or periodically, the second weather information may be transmitted to the prediction device 100. You may send to.

  The prediction device 100 is a device that predicts the occurrence time and location of pests based on the first weather information acquired from the server 20 and the second weather information acquired from the AMeDAS 30. The prediction device 100 generates report data related to the pest occurrence time based on the prediction result, and notifies the generated report data to a terminal device (not shown). This terminal device is, for example, a terminal device used by a worker who cultivates crops, and corresponds to a notebook PC, a portable terminal, and a tablet terminal.

  A configuration of the prediction apparatus 100 illustrated in FIG. 1 will be described. FIG. 4 is a functional block diagram illustrating the configuration of the prediction apparatus according to the first embodiment. As illustrated in FIG. 4, the prediction device 100 includes a communication unit 110, an input unit 120, a display unit 130, a storage unit 140, and a control unit 150.

  The communication unit 110 is a processing unit that performs data communication between the server 20, the AMeDAS 30, and a terminal device (not shown) via the network 50. The communication unit 110 corresponds to a communication device. The control unit 150 described later exchanges data with the server 20, the AMeDAS 30, and the terminal device via the communication unit 110.

  The input unit 120 is an input device that inputs various types of information. For example, the input device corresponds to a keyboard, a mouse, a touch panel, and the like.

  The display unit 130 is a display device that displays information output from the control unit 150 described later. For example, the display unit 130 corresponds to a display, a touch panel, or the like.

  The storage unit 140 includes pest growth temperature information 141, first weather information 142, and second weather information 143. The storage unit 140 corresponds to a storage device such as a semiconductor memory element such as a random access memory (RAM), a read only memory (ROM), and a flash memory.

  The pest growth temperature information 141 includes information on the minimum temperature required for the growth of the pest and the integrated temperature required for the pest to move to the next growth stage. FIG. 5 is a diagram illustrating an example of a data structure of pest growth temperature information. As shown in FIG. 5, the pest growth temperature information 141 associates items, pest names, growth stages, growth zero points, and effective integrated temperatures.

  The item indicates a crop item. The pest name is a name of a pest that causes harm to the corresponding item. For example, the pest names of the pests that cause harm to the item “paddy rice” are “Rhizophorus edulis, Rhizobium medakae, Akasujikasikameka”.

  The growth stage indicates the growth stage of pests that harm the crop. For example, for the rice leafhopper, the growth stage is from eggs to larvae and from larvae to pupae.

  The zero growth point indicates the minimum temperature required for pest growth to proceed. An explanation will be given by taking as an example the zero growth point of the rice leafhopper. When the growth stage of the rice leafhopper is “egg”, the growth zero point is “7.7 ° C.”. For this reason, it means that the growth of rice eggs does not proceed unless the temperature is higher than “7.7 ° C.”. When the growth stage of the rice leafhopper is “larvae”, the growth zero point is “4.4 ° C.”. For this reason, the rice larvae larva means that the growth does not proceed unless the temperature is higher than “4.4 ° C.”. When the growth stage of the rice leafhopper is “蛹”, the growth zero point is “7.6 ° C.”. For this reason, the “蛹” of the rice leafhopper means that the growth does not proceed unless the temperature is higher than “7.6 ° C.”.

  The effective integrated temperature indicates the integrated temperature required for the pest to move to the next growth stage. The integrated temperature is a temperature obtained by integrating the temperature obtained by subtracting the growth zero point from the average temperature on a certain day for each date. The effective integrated temperature of the rice leafhopper will be described as an example. For example, an egg of the rice leafhopper grows from an egg to a larva when the accumulated temperature exceeds the effective accumulated temperature “41.8 ° C.”. The larvae of the rice leafhopper grow from larvae to pupae when the accumulated temperature exceeds the effective accumulated temperature “155.5 ° C.”. When the integrated temperature exceeds the effective integrated temperature “113.6 ° C.”, the rice wing fly grows into an adult.

  The first weather information 142 corresponds to the first weather information received from the server 20 described above. The first weather information 142 includes weather information measured by each of the weather robots 10a to 10f. The data structure of each weather information is the data structure described in FIG.

  The second weather information 143 corresponds to the second weather information received from the AMeDAS 30 described above. The data structure of the second weather information 143 is the data structure shown in FIG.

  The control unit 150 includes an acquisition unit 151, a prediction unit 152, and a generation unit 153. The control unit 150 corresponds to an integrated device such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The control unit 150 corresponds to an electronic circuit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit).

  The acquisition unit 151 is a processing unit that acquires pest growth temperature information 141, first weather information 142, and second weather information 143. The acquisition unit 151 acquires the first weather information 142 from the server 20 and stores the acquired first weather information 142 in the storage unit 140. The acquisition unit 151 acquires the second weather information 143 from the AMeDAS 30, and stores the acquired second weather information 143 in the storage unit 140. The acquisition unit 151 acquires the pest growth temperature information 141 from the input unit 120 or an external terminal (not shown), and stores the acquired pest growth temperature information 141 in the storage unit 140.

  The prediction unit 152 is a processing unit that predicts the degree of pest growth for each place based on the pest growth temperature information 141, the first weather information 142, and the second weather information 143. The prediction unit 152 outputs the prediction result to the generation unit 153. Hereinafter, as an example, a case will be described in which the prediction unit 152 predicts the degree of occurrence of the “rice-spotted fly” for a certain place 60A.

  First, the prediction unit 152 acquires weather information from a date in the past to the current date and weather information from the current date to a date in the future by executing the following processing. The prediction unit 152 acquires weather information from a certain date in the past to the present from the first weather information 142 for the place 60A. For example, the prediction unit 152 measures the weather robot 10 located at the location 60A based on a table that associates the robot identification information with the location where the weather robot identified by the robot identification information is installed. For weather information, obtain temperature information for the relevant period. In addition, although a certain date in the past may be specified in any way, for example, the prediction unit 152 sets the past date that has exceeded the zero point of the development of the rice leafhopper in the same year as the current year. A date is assumed.

  The prediction unit 152 predicts weather information from the present to a certain date in the future based on the information of the second weather information 143 for the location 60A. A certain date in the future may be set in any way, for example, a date obtained by adding a predetermined number of days to the current date. It is assumed that the location 60A is associated with area identification information with the second weather information 143.

  For example, for the location 60A, the prediction unit 152 sets the past weather information from the same date as last year to a certain date last year as future weather information from the present to a certain date in the future. More specifically, for example, the current date is July 7, 2015, and a future date is October 30, 2015. In this case, the prediction unit 152 obtains the past weather information from July 7, 2014 to October 30, 2014 in the second weather information 143 from the current date to the future date. And

  The prediction unit 152 obtains the weather information from a past date to the current date and the weather information from the current date to a future date for the location 60A, and then uses the past date as the starting date. Calculate the integrated temperature while advancing the process one day at a time. Then, the predicting unit 152 identifies the date when the accumulated temperature exceeds the effective accumulated temperature as the date when the pest is generated.

  For example, the prediction unit 152 calculates the integrated temperature by calculating the differential temperature based on the formula (1) and integrating the differential temperatures for each day. In Equation (1), the average temperature indicates the average temperature of the day on the corresponding date. The growth zero point indicates the growth zero point of the corresponding growth stage. For example, in the case of the rice leafhopper of FIG. 5, when the growth stage is an egg, the growth zero point is “7.7 ° C.”. When the growth stage is a larva, the growth zero point is “4.4 ° C.”. When the growth stage is drought, the growth zero point is “7.6 ° C.”.

  Difference temperature = average temperature-zero growth point (1)

  An example of processing in which the prediction unit 152 calculates the larva hatching prediction date and the adult occurrence prediction date as the degree of growth of the pest will be described. Here, the explanation will be made by taking a rice leafhopper as an example.

  First, the prediction unit 152 assumes that the growth stage of the rice leafhopper is “egg”, sets the growth zero point to “7.7 ° C.”, sets a certain date in the past as the starting date, and advances the date by one day. However, the differential temperature is calculated based on the formula (1), and the integrated temperature is calculated by integrating the differential temperatures. The predicting unit 152 identifies “date X1” whose accumulated temperature exceeds the egg effective accumulated temperature “41.8 ° C.” for the first time. The date X1 is the first generation larvae hatching prediction date.

  Subsequently, the predicting unit 152 sets the growth zero point of the rice leafhopper to “larvae”, sets the growth zero point to “4.4 ° C.”, sets the date X1 as the starting date, and advances the date one day at a time. The differential temperature is calculated based on the equation (1), and the integrated temperature is calculated by integrating the differential temperatures. The predicting unit 152 identifies “date X2” whose accumulated temperature exceeds the effective accumulated temperature “155.5 ° C.” of the larva for the first time. The date X2 is the date on which the first-generation rice wing fly larvae become pupae.

  Subsequently, the predicting unit 152 sets the growth zero point of the rice leafhopper to “7.6”, sets the growth zero point to “7.6 ° C.”, sets the date X2 as the starting date, and advances the date one day at a time. The differential temperature is calculated based on the equation (1), and the integrated temperature is calculated by integrating the differential temperatures. The prediction unit 152 identifies “date X3” whose accumulated temperature exceeds the effective accumulated temperature “113.6 ° C.” for the first time. This date X3 is the first generation adult occurrence prediction date.

  The predicting unit 152 sets the second generation larvae hatching prediction date, the second generation larva hatching prediction date, and the second generation larva hatching prediction date as a starting date, using the first generation adult occurrence prediction date or a date obtained by adding a predetermined date to the adult occurrence prediction date Calculate the adult occurrence forecast date of the generation. Similarly, the prediction unit 152 repeats the above process to calculate the nth generation larva hatching prediction date and the nth generation adult occurrence prediction date.

  In the above processing, the location 60A has been described, but the predicting unit 152 calculates the nth generation larvae prediction date and the nth generation adult occurrence prediction date by performing the same processing for other locations. To do.

  In the above processing, the example in which the predicting unit 152 calculates the larvae hatching prediction date and the adult occurrence prediction date of the rice leafhopper is described, but the larva hatching prediction date and the adult occurrence prediction date are similarly calculated for other pests. calculate. The prediction unit 152 may accept designation of a pest to be predicted via the farmer's terminal device or the input unit 120.

  FIG. 6 is a diagram illustrating an example of a prediction result of the prediction unit. As shown in FIG. 6, this prediction result associates items, pest names, generations, larvae hatching prediction dates, adult occurrence prediction dates, and location identification information. The explanation about the item name, pest name, generation, larvae hatching prediction date, and adult occurrence prediction date is the same as the above description. The location identification information is information that uniquely identifies a location. The prediction unit 152 outputs prediction result information to the generation unit 153.

  The generation unit 153 is a processing unit that generates report data regarding pest occurrence prediction based on the prediction result. The generation unit 153 may display the report data on the display unit 130 or may notify the worker's terminal device. FIG. 7 and FIG. 8 are diagrams illustrating an example of report data regarding pest occurrence prediction. As shown in FIG. 7, the report data 160 associates pest names, location identification information, and pest occurrence prediction dates. The pest name corresponds to the name of the pest. The location identification information is information that uniquely identifies a location. The pest occurrence prediction date visually indicates the above-mentioned larval hatching prediction date and the adult occurrence prediction date. For example, the horizontal axis 160a corresponds to the time axis, the black circle is arranged at a position corresponding to the predicted adult occurrence date, and the white circle is arranged at a position corresponding to the predicted larva hatching date.

  As shown in FIG. 8, the report data 170 associates pest names with pest occurrence prediction dates. The generation unit 153 may associate the full moon date in addition to the pest name and the pest occurrence prediction date. The pest name corresponds to the name of the pest. The pest occurrence prediction date visually indicates the above-mentioned larval hatching prediction date and the adult occurrence prediction date. For example, the horizontal axis 170a corresponds to the time axis, and in the larvae row, a black circle is arranged at a position corresponding to the predicted date of larva hatching. Also, in the adult row, a black circle is arranged at a position corresponding to the adult occurrence predicted date.

  Next, a processing procedure of the prediction apparatus 100 according to the first embodiment will be described. FIG. 9 is a flowchart illustrating the processing procedure of the prediction apparatus according to the first embodiment. As shown in FIG. 9, the prediction unit 152 of the prediction device 100 accepts selection of pests (step S101), and acquires pest growth temperature information 141 (step S102).

  The prediction unit 152 determines whether or not an actual measurement value exists (step S103). When there is a measured value by the weather robot 10 (Yes at Step S103), the prediction unit 152 acquires the first weather information 142 (Step S104), and proceeds to Step S106.

  On the other hand, the prediction part 152 acquires the 2nd weather information 143 (step S105), when the actual value by a weather robot does not exist (step S103, No), and transfers to step S106.

  The prediction unit 152 determines whether or not the temperature exceeds the growth zero point (step S106). When the temperature does not exceed the growth zero point (No at Step S106), the prediction unit 152 proceeds to Step S103.

  On the other hand, the prediction unit 152 updates the integrated temperature (step S107) when the growth zero point is exceeded (step S106, Yes). The prediction unit 152 determines whether or not the integrated temperature is equal to or higher than the effective integrated temperature (step S108). If the integrated temperature is not equal to or higher than the effective integrated temperature (No at Step S108), the predicting unit 152 proceeds to Step S103.

  When the integrated temperature is equal to or higher than the effective integrated temperature (step S108, Yes), the prediction unit 152 generates report data (step S109).

  Next, the effect of the prediction device 100 according to the first embodiment will be described. The prediction device 100 calculates an integrated temperature obtained by integrating the difference between the temperature of each date and the minimum temperature for the growth of the pests, and the effective temperature required for the calculated integrated temperature and the pests to become larvae or adults. Estimate the degree of pest growth using the integrated temperature. For this reason, according to the prediction apparatus 100, the time and place where a pest occurs can be predicted and notified in advance.

  In addition, since the predicting apparatus 100 predicts the degree of growth of the pests using the accumulated temperature until the pests become eggs and larvae, and the accumulated temperature until the pests become moths and adults, Measures can be taken according to

  By the way, the prediction unit 152 may further correct the larva hatching prediction date and the adult occurrence prediction date by further using information on the wind direction and the wind speed. For example, when all of the following conditions 1 to 3 are satisfied, the prediction unit 152 updates the pest occurrence prediction date of the location 60A to the pest occurrence prediction date of the location 60B. For example, when the following conditions 1 to 3 are satisfied, it is based on the grounds that part of the pests generated at the location 60B move to the location 60A due to the influence of the wind. The prediction unit 152 updates the predicted larval hatching date and the adult occurrence predicted date of the subsequent generation after updating the estimated adult date of the place 60A. By executing such processing, it is possible to improve the prediction accuracy of the pest occurrence prediction date and the larva hatching prediction date.

Condition 1: The wind speed in the area including the place 60A and the place 60B is equal to or greater than the threshold value.
Condition 2: The position of the location 60A is leeward of the location of the location 60B.
Condition 3: The distance between the location 60A and the location 60B is less than the threshold value.

  A configuration of a system according to the second embodiment will be described. FIG. 10 is a diagram illustrating the configuration of the system according to the second embodiment. As shown in FIG. 10, this system includes weather robots 10 a to 10 f, a server 20, an AMeDAS 30, and a prediction device 200. The weather robots 10a to 10f are installed at predetermined positions. In the following description, the weather robots 10a to 10f are collectively referred to as the weather robot 10 as appropriate. The server 20, the AMeDAS 30, and the prediction device 200 are connected to the network 50.

  The description regarding the meteorological robot 10, the server 20, and the AMeDAS 30 in FIG. 10 is the same as the description regarding the meteorological robot 10, the server 20, and the AMeDAS 30 illustrated in FIG.

  The prediction device 200 is a device that predicts the crop harvest time based on the first weather information acquired from the server 20 and the second weather information acquired from the AMeDAS 30. The prediction device 200 generates report data related to the harvest time based on the prediction result, and notifies the generated report data to a terminal device (not shown). This terminal device is, for example, a terminal device used by a worker who cultivates crops, and corresponds to a notebook PC, a portable terminal, and a tablet terminal.

  A configuration of the prediction apparatus 200 illustrated in FIG. 10 will be described. FIG. 11 is a functional block diagram illustrating the configuration of the prediction apparatus according to the second embodiment. As illustrated in FIG. 11, the prediction device 200 includes a communication unit 210, an input unit 220, a display unit 230, a storage unit 240, and a control unit 250.

  The communication unit 210 is a processing unit that performs data communication between the server 20, the AMeDAS 30, and a terminal device (not shown) via the network 50. The communication unit 210 corresponds to a communication device. A control unit 250 described later exchanges data with the server 20, the AMeDAS 30, and the terminal device via the communication unit 210.

  The input unit 220 is an input device that inputs various types of information. For example, the input device corresponds to a keyboard, a mouse, a touch panel, and the like.

  The display unit 230 is a display device that displays information output from the control unit 250 described later. For example, the display unit 230 corresponds to a display, a touch panel, or the like.

  The storage unit 240 includes crop growth temperature information 241, first weather information 242, and second weather information 243. The storage unit 240 corresponds to a storage device such as a semiconductor memory element such as a RAM, a ROM, or a flash memory.

  The crop growth temperature information 241 includes information on the effective integrated temperature, the accumulated precipitation amount, and the accumulated sunshine duration required until the crop is harvested. FIG. 12 is a diagram illustrating an example of a data structure of crop growth temperature information. As shown in FIG. 12, the crop growth temperature information 241 includes items, varieties, worker identification information, effective integrated temperatures, accumulated precipitation, and accumulated sunshine hours. The item indicates a crop item. Variety indicates the variety of the crop. Worker identification information is information that uniquely identifies a worker. Although illustration is omitted here, it is assumed that the worker identification information is associated with the place identification information for uniquely identifying the place where the worker is growing the corresponding crop.

  The effective accumulated temperature, the accumulated precipitation amount, and the accumulated sunshine duration shown in FIG. 12 are set by the worker based on the past performance at each place. For places where there is no past record, the effective integrated temperature, precipitation accumulation, and sunshine duration accumulation required for general crops to be harvested, as defined in the literature information, are set.

  The effective integrated temperature indicates the integrated temperature required until the crop is harvested, starting from the heading date of the crop. The effective integrated temperature has a minimum effective integrated temperature and a maximum effective integrated temperature. That is, the time when the integrated temperature from the heading date is included from the lowest to the highest effective integrated temperature is the crop harvesting time based on the effective integrated temperature. In the following description, the crop harvest time based on the effective integrated temperature is referred to as the first harvest time as appropriate.

  Accumulated precipitation indicates the accumulated precipitation required by the harvest time of the crop, starting from the crop heading date. The total precipitation has the lowest total precipitation and the highest total precipitation. In other words, the time when the accumulated precipitation from the heading date is included from the lowest to the highest accumulated precipitation is the crop harvesting time based on the accumulated precipitation. In the following description, the crop harvesting time based on the accumulated precipitation amount will be referred to as the second harvesting time as appropriate.

  The accumulated sunshine hours indicate the accumulated sunshine hours required until the crop is harvested, starting from the crop heading date. The accumulated sunshine hours have the lowest accumulated sunshine hours and the highest accumulated sunshine hours. That is, the time when the accumulated sunshine hours from the heading date are included from the lowest to the highest accumulated sunshine hours is the crop harvesting time based on the accumulated sunshine hours. In the following description, the crop harvest time based on the accumulated sunshine hours is referred to as the third harvest time as appropriate.

  The first weather information 242 corresponds to the first weather information received from the server 20 described above. The first weather information 242 includes weather information measured by the weather robots 10a to 10f. The data structure of each weather information is the data structure described in FIG.

  The second weather information 243 corresponds to the second weather information received from the AMeDAS 30 described above. The data structure of the second weather information 243 is the data structure shown in FIG.

  The control unit 250 includes an acquisition unit 251, a prediction unit 252, and a generation unit 253. The control unit 250 corresponds to, for example, an integrated device such as an ASIC or FPGA. Moreover, the control part 250 respond | corresponds to electronic circuits, such as CPU and MPU, for example.

  The acquisition unit 251 is a processing unit that acquires the crop growth temperature information 241, the first weather information 242, and the second weather information 243. The acquisition unit 251 acquires the first weather information 242 from the server 20 and stores the acquired first weather information 242 in the storage unit 240. The acquisition unit 251 acquires the second weather information 243 from the AMeDAS 30, and stores the acquired second weather information 243 in the storage unit 240. The acquisition unit 251 acquires the crop growth temperature information 241 from the input unit 220 or an external terminal (not shown), and stores the acquired crop growth temperature information 241 in the storage unit 240.

  The prediction unit 252 is a processing unit that predicts the crop harvest time based on the crop growth temperature information 241, the first weather information 242, and the second weather information 243. The prediction unit 252 outputs the prediction result to the generation unit 253. Hereinafter, as an example, a case will be described in which the place where the worker of the worker identification information “U101” is growing a crop is the place 70A, and the prediction unit 252 predicts the harvest time of “Akitakomachi” for the place 70A.

  First, the prediction unit 252 receives heading start date information from the terminal device or the input unit 220, and acquires weather information from the heading start date to a date in the future by executing the following processing.

  First, a case where the heading date is a date earlier than the current date will be described. In this case, the weather information from the heading start date to the present is acquired from the first weather information 242 for the place 70A. For example, the prediction unit 252 measures the weather robot 10 located at the location 70A based on a table that associates the robot identification information with the location where the weather robot identified by the robot identification information is installed. For weather information, obtain information on temperature, precipitation, and sunshine duration for the relevant period.

  The prediction unit 252 predicts weather information from the present to a certain date in the future based on the information of the second weather information 243 for the place 70A. A certain date in the future may be set in any way, for example, a date obtained by adding a predetermined number of days to the current date. It is assumed that the location 70 </ b> A is associated with area identification information with the second weather information 243. In addition, the process which estimates the weather information from the present to a certain date in the future based on the information of the 2nd weather information 243 is the same as that of the prediction part 152 of Example 1. FIG.

  Next, a case where the heading date is a date that is in the future from the current date will be described. In this case, the prediction is based on weather information from the heading date to a certain date in the future. Note that the process of predicting weather information from the heading start date to a certain date in the future based on the information of the second weather information 243 is the same as the prediction unit 152 of the first embodiment.

  The prediction unit 252 obtains weather information from the heading start date to a certain date in the future for the location 70A, and then advances the date one day at a time, starting from the heading start date, and the accumulated temperature, the accumulated precipitation, and the sunshine. Calculate the accumulated time.

  The predicting unit 252 predicts, as the first harvest time, a period in which the integrated temperature starting from the heading date is included from the lowest to the highest effective integrated temperature defined in the crop growth temperature information 241. Note that the predicting unit 252 does not integrate the temperature when the temperature is less than 0 ° C.

  The predicting unit 252 predicts, as the second harvest time, a period in which the accumulated precipitation with the heading start date as the starting date is included from the lowest to the highest accumulated precipitation defined in the crop growth temperature information 241.

  The prediction unit 252 predicts, as the third harvesting time, a period in which the accumulated sunshine hours starting from the heading start date are included from the lowest to the highest accumulated sunshine hours defined in the crop growth temperature information 241.

  In the above processing, the location 70A has been described, but the prediction unit 252 calculates the first harvest time, the second harvest time, and the third harvest time by performing the same processing for other locations. The prediction unit 252 outputs a prediction result in which the location is associated with the first harvest time, the second harvest time, and the third harvest time to the generation unit 253.

  The generation unit 253 is a processing unit that generates report data related to the crop harvest time based on the prediction result. The generation unit 253 may display the report data on the display unit 230 or may notify the worker terminal device. FIG. 13 and FIG. 14 are diagrams illustrating an example of report data regarding crop harvest time. As shown in FIG. 13, the report data 260 associates items, varieties, location identification information, sowing dates, heading dates, and harvest times. The item indicates a crop item. Variety indicates the variety of the crop. The location identification information is information that uniquely identifies a location. The location identification information may be a pen. The sowing date is the date of sowing the crop seed. For example, the generation unit 253 receives information related to the sowing date from the input unit 220 or the terminal device.

  The generation unit 253 sets the harvest time to a time when the first harvest time, the second harvest time, and the third harvest time overlap. Note that the generation unit 253 determines that the first harvest time and the second harvest time overlap when there is no time when the first harvest time, the second harvest time, and the third harvest time overlap. The first harvest time and the third harvest time are set to overlap. Moreover, the production | generation part 253 is good also considering a harvest time as the 1st harvest time, when the 1st harvest time does not overlap with the 2nd harvest time and the 3rd harvest time.

  Further, the generation unit 253 may visually display the harvest time as shown in the region 260a. For example, the horizontal axis of the region 260a corresponds to the time axis. For example, the harvest time of Akitakomachi is time 260a. The harvest time of Tatsukomochi is time 260b. The harvest time of Nebarigosi is time 260c. For Ryuhou, the harvest time in the place identification information “E1-2, E1-3” is time 260d. For Ryuho, the harvest time in the place identification information “E1-4” is time 260e.

  Further, as illustrated in FIG. 14, the generation unit 253 comprehensively displays the first harvest time 271, the second harvest time 272, and the third harvest time 273, and finally the harvest time determined. 274 may be displayed. The horizontal axis of the graph in FIG. 14 corresponds to the date. The left axis of the upper graph is an axis indicating the integrated temperature. The axis on the right side of the upper graph is the axis indicating precipitation. The vertical axis of the lower graph is an axis indicating sunshine hours.

  Next, a processing procedure of the prediction apparatus 200 according to the second embodiment will be described. FIG. 15 is a flowchart illustrating the processing procedure of the prediction apparatus according to the second embodiment. As shown in FIG. 15, the prediction unit 252 of the prediction device 200 accepts selection of a crop (step S201) and determines whether there is a record of last year (step S202). When there is no last year's record (No at Step S202), the prediction unit 252 identifies the effective integrated temperature of the crop, the accumulated precipitation amount, and the accumulated sunshine duration based on the literature information (not shown) (Step S203). The process proceeds to S205.

  When there is a record of last year (Yes at Step S202), the prediction unit 252 specifies the effective integrated temperature of the crop, the accumulated precipitation amount, and the accumulated sunshine time based on the unillustrated record information (Step S204). The process proceeds to step S205.

  The prediction unit 252 determines whether or not an actual measurement value by the weather robot 10 exists (step S205). The prediction unit 252 acquires the first weather information 242 (step S206) when there is an actual measurement value by the weather robot 10 (step S205, Yes), and proceeds to step S208.

  On the other hand, the prediction part 252 acquires the 2nd weather information 243 (step S207), when the actual value by a weather robot does not exist (step S205, No), and transfers to step S208.

  The prediction unit 252 determines whether or not the temperature is 0 degrees or higher (step S208). When the temperature is not 0 ° C. or more (No at Step S208), the prediction unit 252 proceeds to Step S205.

  On the other hand, when the temperature is equal to or higher than 0 degrees (step S208, Yes), the prediction unit 252 updates the integrated temperature (step S209). The prediction unit 252 determines whether or not the integrated temperature is equal to or higher than the effective integrated temperature (step S210). When the integrated temperature is not equal to or higher than the effective integrated temperature (No at Step S210), the predicting unit 252 proceeds to Step S205.

  When the integrated temperature is equal to or higher than the effective integrated temperature (step S210, Yes), the prediction unit 252 determines whether there is a past record of precipitation and sunshine hours (step S211). The prediction part 252 transfers to step S213, when the past results of precipitation and sunshine time do not exist (step S211, No).

  When there is a past record of precipitation and sunshine hours (step S211, Yes), the prediction unit 252 narrows down the harvest time (step S212). The prediction unit 252 determines an optimal harvest time (step S213) and generates report data (step S214).

  Next, the effect of the prediction device 200 according to the second embodiment will be described. The prediction device 200 identifies the first harvest time, the second harvest time, and the third harvest time based on the crop growth temperature information 241, the first weather information 242, and the second weather information 243, and determines the optimum harvest time. Predict. For this reason, according to the prediction apparatus 200, the crop harvest time can be predicted and notified in advance.

  By the way, among the processes described in the present embodiment, all or a part of the processes described as being automatically performed can be manually performed, or the processes described as being performed manually can be performed. All or a part can be automatically performed by a known method. In addition, the processing procedure, control procedure, specific name, and information including various data and parameters shown in the above-described document and drawings can be arbitrarily changed unless otherwise specified.

  Furthermore, each processing function performed by each device may be realized by a CPU and a program that is analyzed and executed by the CPU, or may be realized as hardware by wired logic.

100,200 Prediction device 151,251 Acquisition unit 152,252 Prediction unit 153,253 Generation unit

Claims (6)

Weather information that associates date, temperature, and location, minimum temperature information required for the growth of the pest, and information on the first integrated temperature required for the pest to move to the next growth stage An acquisition unit to acquire;
The difference temperature between the temperature and the temperature information is calculated for each date, and the growth of the pest is based on the second integrated temperature obtained by integrating the calculated difference temperatures for each date and the first integrated temperature. A prediction unit for predicting the degree for each place;
The prediction apparatus characterized by having.   The first integrated temperature includes an integrated temperature required for a pest to grow from an egg to a larva and an integrated temperature required for a pest to grow from an larva to an adult, and the prediction unit includes the first integrated temperature and The prediction apparatus according to claim 1, wherein, based on the second integrated temperature, it is predicted for each place whether the pest grows to a larva or an adult.   The said acquisition part further acquires the information regarding a wind direction, and the said prediction part correct | amends the growth degree of the pest which generate | occur | produces in each place based on the information regarding the said wind direction. The prediction device described. Meteorological information in which date, temperature, precipitation, sunshine duration, and location are associated with each other, and information on the first integrated temperature, the first integrated precipitation, and the first integrated sunshine required until the crop is harvested. An acquisition unit for acquiring
Based on the weather information, a second accumulated temperature obtained by integrating the temperature for each date, a second accumulated precipitation obtained by integrating the precipitation for each date, and a second accumulated value for integrating the sunshine duration for each date. A first integrated temperature and the second integrated temperature; the first integrated precipitation and the second integrated precipitation; the first integrated sunshine amount and the second integrated amount; And a prediction unit for predicting the harvest time of the crop based on the accumulated amount of sunlight. A prediction method performed by a computer,
Weather information that associates date, temperature, and location, minimum temperature information required for the growth of the pest, and information on the first integrated temperature required for the pest to move to the next growth stage Acquired,
The difference temperature between the temperature and the temperature information is calculated for each date, and the growth of the pest is based on the second integrated temperature obtained by integrating the calculated difference temperatures for each date and the first integrated temperature. A prediction method characterized by executing a process of predicting the degree for each place. A prediction method performed by a computer,
Meteorological information in which date, temperature, precipitation, sunshine duration, and location are associated with each other, and information on the first integrated temperature, the first integrated precipitation, and the first integrated sunshine required until the crop is harvested. Get
Based on the weather information, a second accumulated temperature obtained by integrating the temperature for each date, a second accumulated precipitation obtained by integrating the precipitation for each date, and a second accumulated value for integrating the sunshine duration for each date. A first integrated temperature and the second integrated temperature; the first integrated precipitation and the second integrated precipitation; the first integrated sunshine amount and the second integrated amount; A prediction method comprising: executing each process for predicting the harvest time of the crop based on the accumulated amount of sunlight.

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