JP6920151B2 - Forecasting device, forecasting method and forecasting program - Google Patents

Description

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

Farmers are required to predict the outbreak of pests and eliminate them because cultivating crops without exterminating the pests that occur in the crops causes problems such as deterioration of yield and quality. For example, it is common practice for workers to predict the time of pest outbreak based on past experience and intuition, and to suppress the damage of pests by spraying pesticides and the like during the predicted period.

JP-A-2009-106261 Japanese Unexamined Patent Publication No. 2006-115704

However, the above-mentioned conventional technique has a problem that it is not possible to notify an effective timing for exterminating pests.

For example, depending on the growing condition of the crop, even if a pest occurs, the effect on the crop is small, so it is not effective to predict the outbreak of the pest during such a growing condition. In addition, when the time of occurrence of pests is ambiguous, it is possible to exterminate the pests by extending the period of spraying pesticides, but in recent years, the health consciousness of customers has increased and many pesticides are used. That is not desirable.

In one aspect, it is an object of the present invention to provide a predictor, a predictor, and a predictor program capable of notifying effective timing of pest control.

In the first plan, the prediction device has a storage unit, a calculation unit, and a prediction unit. The storage unit contains weather information that links the date and temperature, the minimum temperature information required for the growth of pests, the integrated temperature information required for the pests to move to the next growth stage, and crops. Memorize information on the alarm period during which pests are damaged during the growth period. When the calculation unit receives the information on the reference date when the pests hatched, the calculation unit calculates the difference temperature between the air temperature and the temperature information for each date, using the reference date as the start date, and integrates the calculated difference temperature for each date. The second integrated temperature is calculated. Based on the first integrated temperature and the second integrated temperature, the prediction unit predicts the occurrence date at which the growth stage of the pest becomes a predetermined growth stage for each different generation, and predicts the predicted occurrence dates of the plurality of occurrence dates. Of these, the occurrence date included in the alarm period is determined.

It is possible to notify the effective timing of exterminating pests.

FIG. 1 is a diagram showing a configuration of a system according to this embodiment. FIG. 2 is a diagram showing an example of a data structure of meteorological information. FIG. 3 is a diagram showing an example of the data structure of the second weather information. FIG. 4 is a functional block diagram showing the configuration of the prediction device according to the present embodiment. FIG. 5 is a diagram showing an example of a data structure of pest growth temperature information. FIG. 6 is a diagram showing an example of a data structure of forecast date information. FIG. 7 is a diagram showing an example of the data structure of the alarm period table. FIG. 8 is a diagram showing an example of a prediction result regarding paddy rice. FIG. 9 is a diagram showing an example of the prediction result regarding soybean. FIG. 10 is a diagram showing an example of a prediction result regarding wheat. FIG. 11 is a diagram showing an example of the prediction result regarding green soybeans. FIG. 12 is a diagram showing an example of the content of the e-mail notified by the notification unit. FIG. 13 is a flowchart showing a processing procedure of the prediction device according to the present embodiment. FIG. 14 is a diagram showing an example of a hardware configuration of a computer that realizes the same function as the prediction device.

Hereinafter, examples of the prediction device, prediction method, and prediction program disclosed in the present application will be described in detail with reference to the drawings. The present invention is not limited to this embodiment.

FIG. 1 is a diagram showing a configuration of a system according to this embodiment. As shown in FIG. 1, this system includes user terminals 5a to 5c, meteorological robots 10a to 10f, a server 20, an AMeDAS (Automated Meteorological Data Acquisition System) 30, and a prediction device 100.

The user terminals 5a to 5c are connected to the prediction device 100 via the network 50. The meteorological robots 10a to 10f are connected to the server 20. Further, the meteorological robots 10a to 10f are installed at predetermined positions determined in advance. In the following description, the user terminals 5a to 5c are collectively referred to as the user terminal 5. The meteorological robots 10a to 10f are collectively referred to as the meteorological robot 10. The server 20, the AMeDAS 30, and the prediction device 100 are connected to the network 50.

The user terminal 5 is a terminal device used by a farmer's worker. The user terminal 5 corresponds to a PC (Personal Computer), a smartphone, a tablet PC, a notebook PC, and the like. The worker operates the user terminal 5 to execute data communication with the prediction device 100. When the worker regularly observes the crop and confirms the hatching of the pest, the worker operates the user terminal 5 to notify the prediction device 100 of the information of the "reference date". For example, the information on the reference date includes information such as identification information of the pest (pest name), the date when the pest (growth stage: larva) was confirmed, and the place where the pest was confirmed.

Further, when the user terminal 5 receives the information of the prediction result regarding the occurrence of the pest from the prediction device 100, the user terminal 5 displays the received information of the prediction result on the display screen of the user terminal 5.

In addition, the worker may operate the user terminal 5 to transmit the information on the growth record of the crop to the prediction device 100. The growth record of the crop includes the item and the content of the growth record. For example, information such as "item: paddy rice" and "heading date: 7/28" is included as the growth record.

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

The server 20 is a device that acquires and holds each weather information from each weather robot 10a to 10f. Each meteorological information acquired by the server 20 from the meteorological robots 10a to 10f is appropriately referred to as the first meteorological information. The server 20 transmits the first weather information to the prediction device 100. When the server 20 receives the request for the 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. Although not shown here, the data structure of the first meteorological information is data in which the meteorological information shown in FIG. 2 is summarized for each meteorological robot 10.

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

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

The prediction device 100 predicts the date when the pest occurs for each generation, starting from the reference date when the pest actually hatched, based on the information received from the user terminal 5, the server 20, and the AMeDAS 30. The prediction device 100 predicts the date within the alarm period in which the pest is damaged during the entire growth period of the crop among the calculated dates when the pest occurs for each generation, and notifies the user terminal 5 of the prediction result.

The configuration of the prediction device 100 shown in FIG. 1 will be described. FIG. 4 is a functional block diagram showing the configuration of the prediction device according to the present embodiment. As shown 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 executes data communication between the user terminal 5, the server 20, and the AMeDAS 30 via the network 50. The communication unit 110 corresponds to a communication device. The control unit 150, which will be described later, exchanges data with the user terminal 5, the server 20, and the AMeDAS 30 via the communication unit 110.

The input unit 120 is an input device for inputting 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, which will be described later. For example, the display unit 130 corresponds to a display, a touch panel, or the like.

The storage unit 140 has pest growth temperature information 141, first weather information 142, second weather information 143, prediction date information 144, alarm period table 145, and prediction result information 146. The storage unit 140 corresponds to a storage device such as a semiconductor memory element such as a RAM (Random Access Memory), a ROM (Read Only Memory), or a flash memory (Flash Memory).

The pest growth temperature information 141 includes information on the minimum temperature required for the growth of the pest to proceed and the integrated temperature required for the pest to move to the next growth stage. FIG. 5 is a diagram showing an example of a data structure of pest growth temperature information. As shown in FIG. 5, the pest growth temperature information 141 associates the item, the pest name, the growth stage, the growth zero point, and the effective integrated temperature.

The item indicates the item of the crop. The pest name is the name of the pest that harms the item in question. For example, the pest names of the pests that harm the item "paddy rice" are "Rice leafminer, Ryukyu robin, Ryukyu robin, Ryukyu robin".

The growth stage indicates the growth stage of pests that are harmful to the crop. For example, for rice leafminer leafminer, the growth stage is from egg to larva and from larva to pupa.

The zero growth point indicates the minimum temperature required for the growth of pests to proceed. The zero growth point of the rice leafminer leafminer will be described as an example. When the growth stage of the rice leafminer leafminer is "egg", the growth zero point is "7.7 ° C". For this reason, it means that the eggs of Agromyzidae do not grow unless the temperature becomes higher than "7.7 ° C". When the growth stage of the rice leafminer leafminer is "larva", the growth zero point is "4.4 ° C". For this reason, it means that the larvae of Agromyzidae do not grow unless the temperature becomes higher than "4.4 ° C". When the growth stage of leafminer leafminer is "pupa", the zero growth point is "7.6 ° C". Therefore, the "pupa" of the leafminer leafminer means that the growth does not proceed unless the temperature becomes 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 the temperature obtained by subtracting the growth zero point from the average temperature of a certain day and integrating it for each date. The effective integrated temperature of the leafminer leafminer will be described as an example. For example, the eggs of Agromyzidae grow from eggs to larvae when the integrated temperature exceeds the effective integrated temperature "41.8 ° C". The rice leafminer leafminer larva grows from a larva to a pupa when the integrated temperature exceeds the effective integrated temperature "155.5 ° C". The pupae of Agromyzidae grow from pupae to adults when the integrated temperature exceeds the effective integrated temperature "113.6 ° C".

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

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

Predicted date information 144 includes information on predicted dates for transitioning to each growth stage of the pest. FIG. 6 is a diagram showing an example of a data structure of forecast date information. As shown in FIG. 6, the predicted date information 144 associates the item, the pest name, the generation, the larva hatching predicted date, the adult development predicted date, the spawning predicted date, and the place identification information.

The item indicates the item of the crop. The pest name is the name of the pest that harms the item in question. The generation is information indicating the generation of the pest in a predetermined period (for example, May to October). The predicted larval hatching date is the predicted date at which the larva hatches from the egg. The predicted adult emergence date is the predicted date of adult emergence. The predicted egg laying date is the predicted date for the pest to lay eggs. The location identification information is information that uniquely identifies a location.

The alarm period table 145 is a table that holds information on the period (alarm period) of the crop growth period that is damaged by pests. FIG. 7 is a diagram showing an example of the data structure of the alarm period table. As shown in FIG. 7, the alarm period table 145 associates the item, the pest name, the growth stage, and the alarm period.

The item indicates the item of the crop. The pest name is the name of the pest that harms the item in question. The growth stage indicates the growth stage of the pest that harms the item in question during the alarm period. For example, for the item "paddy rice", the pest name "Ryukyu robin" indicates that it causes harm in the growth stage "adult".

The alarm period indicates the period during which the applicable item is damaged by pests. It was shown that the item "paddy rice" will be damaged by Ryukyu robin (adult) and Ryukyu robin (adult) during the alarm period "7 / 29-8 / 8 (from the heading date to 10 days later)". ing.

For the item "soybean", two types of alarm periods are included. During the first alarm period "8 / 1-8 / 15 (immediately after germination)", it is shown that the dark sword grass (larva) is damaged. During the second alarm period "6 / 25-10 / 20 (from sowing to harvesting)", it is shown that Spodoptera litura (larvae), Spodoptera litura (larvae), and Mameshiniga (larvae) are damaged. ..

It has been shown that the item "wheat" will be damaged by the wheat aphid (adult) during the alarm period "5 / 10-5 / 28 (around heading)".

The item "edamame" includes two types of alarm periods. During the first alarm period "8 / 1-8 / 15 (immediately after germination)", it is shown that the dark sword grass (larva) is damaged. During the second alarm period "6 / 7-9 / 5 (from sowing to harvesting)", it is shown that Spodoptera litura (larvae), Spodoptera litura (larvae), and Mameshiniga (larvae) are damaged. ..

The prediction result information 146 is information indicating the prediction result within the alarm period in which the pest is damaged. In the following, as an example, the prediction results of the alarm period for paddy rice, soybean, wheat, and green soybean will be described.

FIG. 8 is a diagram showing an example of a prediction result regarding paddy rice. In the prediction result 160a, the alarm period of the paddy rice is the alarm period 60a (from the heading date to 10 days later). The pests that damage the alarm period 60a are the adult Ryukyu robin and the adult Ryukyu robin. In the example shown in FIG. 8, the date on which the adult Ryukyu robin is found is "8/1, 8/31", which is included in the alarm period 60a. Further, the dates on which the adults of Akasujikasumikame occur are "8/1, 9/1", which are included in the alarm period 60a.

FIG. 9 is a diagram showing an example of the prediction result regarding soybean. In the prediction result 160b, the alarm period of soybean is the alarm period 61a (immediately after germination) and the alarm period 61b (from sowing to harvesting). The pest that damages the alarm period 61a is the larva of Dark sword grass. In the example shown in FIG. 9, the date when the larva of Dark sword grass is generated is "8/11", which is included in the alarm period 61a.

The pests that damage the alarm period 61b are the larvae of Spodoptera litura, the larvae of Spodoptera litura, and the larvae of Mameshiniga. In the example shown in FIG. 9, the dates on which the Spodoptera litura larvae occur are "7/17, 8/28", which are included in the alarm period 61b. The dates on which the larvae of Mitsumonkinuwaba occur are "6/28, 8/1, 8/31, 10/14" and are included in the alarm period 61b. The date on which the larvae of Mameshiniga occur is "9/2" and is included in the alarm period 61b.

FIG. 10 is a diagram showing an example of a prediction result regarding wheat. In the prediction result 160c, the alarm period of wheat is the alarm period 62a (around heading). The pest that damages the alarm period 62a is the adult aphid aphid. In the example shown in FIG. 10, the date on which the adult aphid aphid appears is "5/11, 5/25", which is included in the alarm period 62a.

FIG. 11 is a diagram showing an example of the prediction result regarding green soybeans. In the prediction result 160d, the alarm period of green soybean is the alarm period 63a (immediately after germination) and the alarm period 61b (from sowing to harvesting). The pest that damages the alarm period 63a is the larva of Dark sword grass. In the example shown in FIG. 9, the date when the larva of Dark sword grass is generated is "8/11", which is included in the alarm period 63a.

The pests that damage the alarm period 63b are the larvae of Spodoptera litura, the larvae of Spodoptera litura, and the larvae of Mameshiniga. In the example shown in FIG. 11, the dates on which the Spodoptera litura larvae occur are “7/17, 8/28” and are included in the alarm period 63b. The dates on which the larvae of Mitsumonkinuwaba occur are "6/28, 8/1, 8/31" and are included in the alarm period 63b. The date on which the larvae of Mameshiniga occur is "9/2" and is included in the alarm period 63b.

Returning to the description of FIG. The control unit 150 includes an acquisition unit 151, a prediction unit 152, an update unit 153, and a notification unit 154. The control unit 150 corresponds to, for example, an integrated device such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). Further, the control unit 150 corresponds to, for example, 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.

Further, when the acquisition unit 151 receives the information on the reference date from the user terminal 5, the acquisition unit 151 outputs the information on the reference date to the prediction unit 152. As described above, the information on the reference date includes information such as identification information (pest name) of the pest that occurred, the date when the pest (growth stage: larva) was confirmed, and the place where the pest was confirmed.

The prediction unit 152 calculates the integrated temperature related to the degree of growth of the pest based on the information of the reference date, the pest growth temperature information 141, the first weather information 142, and the second weather information 143. Further, the prediction unit 152 predicts the occurrence date at which the growth stage of the pest becomes each growth stage for each different generation based on the integrated temperature related to the growth degree of the pest. The prediction unit 152 determines the occurrence date included in the alarm period among the predicted occurrence dates.

First, an example of a process in which the prediction unit 152 calculates the integrated temperature will be described. In the following, as an example, a case where the prediction unit 152 predicts the degree of occurrence of "Ryukyu robin" will be described at a certain place.

First, the prediction unit 152 sets the date on which the pest (growth stage: larva) included in the reference date information is confirmed to the date on which the egg of the first generation Ryukyu robin, Ryukyu robin, hatched (the date on which the larva emerged). Identify as.

The prediction unit 152 compares the place where the pest is confirmed and the information of the second weather information 143 included in the information of the reference date, and detects the future temperature of the place where the pest is confirmed. The prediction unit 152 calculates the integrated temperature while advancing the date one day at a time, starting from the date when the pest is confirmed.

For example, the prediction unit 152 calculates the difference temperature based on the equation (1) and calculates the integrated temperature by integrating the daily difference temperature. In equation (1), the average temperature indicates the average daily temperature on the corresponding date. The zero growth point indicates the zero growth point of the corresponding growth stage. For example, in the case of "Ryukyu robin, Ryukyu robin" in FIG. 5, when the growth stage is an egg, the growth zero point is "10.5 ° C.". When the growth stage is a larva, the growth zero point is "13.4 ° C". When the growth stage is the early stage of spawning of adults, the growth zero point is "14.2 ° C".

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

An example of a process in which the prediction unit 152 calculates the larva hatching prediction date, the adult development prediction date, and the spawning prediction date of the pest will be described. Here, the Ryukyu robin, Ryukyu robin, and Ryukyu robin will be described as an example. The date when the eggs of the first generation Ryukyu robin, Ryukyu robin, hatched (estimated date of larva hatching) is the date when the pest (growth stage: larva) included in the reference date information is confirmed.

The prediction unit 152 sets the growth zero point to "13.4 ° C." on the assumption that the growth stage of the red-spotted turtle is "larva", and sets the date X1 (the pest (growth stage: included in the information of the reference date). The integrated temperature is calculated by calculating the differential temperature based on the equation (1) and integrating each differential temperature while advancing the date one day at a time, starting from the date when the larva) was confirmed. The prediction unit 152 identifies the "date X2" whose integrated temperature exceeds the effective integrated temperature of the larva "165.3 ° C." for the first time. Such a date X2 is a date (predicted adult development date) when the larvae of the first-generation Ryukyu robin, Ryukyu robin, become adults.

Subsequently, the prediction unit 152 sets the growth zero point to "14.2 ° C", assuming that the growth stage of the Ryukyu robin is "adult", sets the date X2 as the starting date, and advances the date one day at a time. However, the integrated temperature is calculated by calculating the differential temperature based on the equation (1) and integrating each differential temperature. The prediction unit 152 identifies the "date X3" whose integrated temperature exceeds the effective integrated temperature "39.8 ° C." of the adult (early spawning period of the adult) for the first time. Such date X3 is the predicted spawning date of the first generation.

Assuming that the growth stage of the Ryukyu robin is "egg", the prediction unit 152 sets the growth zero point to "10.5 ° C", starts from date X3, and advances the date one day at a time. The integrated temperature is calculated by calculating the difference temperature based on (1) and integrating each difference temperature. The prediction unit 152 identifies the "date X4" whose integrated temperature exceeds the effective integrated temperature of the egg "100.2 ° C." for the first time. Such date X4 is the predicted hatching date of the second generation larvae.

The prediction unit 152 calculates the second-generation adult development prediction date and the second-generation spawning prediction date in the same manner as in the above processing. Similarly, the prediction unit 152 calculates the nth generation larva hatching prediction date, the nth generation adult development prediction date, and the spawning prediction date by repeating the above processing.

In the above processing, the prediction unit 152 has described an example of calculating the larval hatching prediction date, the adult development prediction date, and the spawning prediction date of the red-spotted turtle, but the same applies to the other pests. Calculate the predicted adult emergence date and the predicted spawning date. The prediction unit 152 registers each predicted prediction date as prediction date information.

Further, the prediction unit 152 updates the prediction date information 144 by repeatedly executing the above processing every time the information of the reference date is newly acquired. Here, when the information of the reference date includes the date when the pest (growth stage: adult) is confirmed, the prediction unit 152 indicates that the growth stage of the red-spotted turtle is "adult". As a starting point, the zero growth point is set to "14.2 ° C", the date X2 (the date when the pest (growth stage: adult) is confirmed) is set as the starting date, and the date is advanced day by day based on the formula (1). The integrated temperature is calculated by calculating the differential temperature and integrating each differential temperature. The prediction unit 152 identifies the "date X3" whose integrated temperature exceeds the effective integrated temperature "39.8 ° C." of the adult (early spawning period of the adult) for the first time.

Further, when the prediction date 152 includes the date when the pest (growth stage: larva) is confirmed in the reference date information, the generation of the pest is estimated by referring to the larva hatching prediction date in the prediction date information 144. Then, based on the estimation result, the larva hatching prediction date of each generation, the nth generation adult development prediction date, and the spawning prediction date may be predicted. For example, the predicted date 152 compares the predicted larval hatching date of each generation with the date when the pest (growth stage: larva) included in the reference date information is confirmed, and the pest (growth stage) included in the reference date information is compared. : The generation corresponding to the predicted larval hatching date closest to the date when the larva) was confirmed is used as the estimation result.

More specifically, the first-generation larva hatching prediction date "5/31", the second-generation larva hatching prediction date "7/12", and the pests (growth stage: larvae) included in the reference date information are confirmed. The date is "7/13". In this case, the prediction unit 152 estimates that the generation corresponding to the information on the reference date is the second generation.

The prediction unit 152 uses the first meteorological information 142 and the second meteorological information 143 instead of the reference date information to estimate the date of the pest (growth stage: larva) included in the reference date information. You may.

As described above, the prediction unit 152 predicts the larva hatching prediction date, the adult development prediction date, and the spawning prediction date in each generation of each pest, and then determines the prediction date included in the alarm period among the predicted prediction dates. Performs the judgment process.

The process of determining the forecast date included in the alarm period of the item "paddy rice" by the forecasting unit 152 will be described. The prediction unit 152 identifies the name of the pest that damages the paddy rice, the growth stage, and the alarm period with reference to the alarm period table 145. As shown in Fig. 7, the names of pests that damage paddy rice are "Ryukyu robin" and "Akasujikasumikame", the growth stage is "adult", and the alarm period is "7 / 29-8 / 8". be.

The prediction unit 152 refers to the prediction date information 144, and among the predicted adult outbreak dates of each generation of "Ryukyu robin" and "Akasujikasumikame", the adult outbreak included in the alarm period 7/29 to 8/8. The predicted date is determined as the predicted date to be notified.

The process of determining the forecast date included in the alarm period of the item "soybean" by the forecasting unit 152 will be described. As shown in FIG. 7, the soybean alarm period has two alarm periods depending on the pest name and the set of growth stages.

First, the alarm period "8 / 1-8 / 15" will be described. During this alarm period, the name of the pest that damages soybean is "Dark sword grass" and the growth stage is "larva". The prediction unit 152 refers to the prediction date information 144, and among the larva hatching prediction dates of each generation of "Tamanayaga", the larva hatching prediction date included in the alarm period "8 / 1-8 / 15" is notified. Judge as the predicted date.

Next, the alarm period "6/25 to 10/20" will be described. During this alarm period, the names of pests that damage soybeans are "Spodoptera litura", "Mitsumonkinuwaba", and "Mameshiniga", and the growth stage is "larva". The prediction unit 152 refers to the prediction date information 144, and sets the alarm period "6/25 to 10/20" among the larva hatching prediction dates of each generation of "Spodoptera litura", "Mitsumonkinuwaba", and "Mameshiniga". The included larva hatching predicted date is determined as the predicted date to be notified.

The process of determining the forecast date included in the alarm period of the item "wheat" by the forecasting unit 152 will be described. The prediction unit 152 identifies the name of the pest that damages wheat, the growth stage, and the alarm period with reference to the alarm period table 145. As shown in FIG. 7, the name of the pest that damages wheat is "wheat aphid", the growth stage is "adult", and the alarm period is "5 / 10-5 / 28".

The prediction unit 152 refers to the prediction date information 144, and notifies the predicted adult outbreak date included in the alarm period "5 / 10-5 / 28" among the predicted adult outbreak dates of each generation of "Mugikubirea aphid". Judge as the predicted date of the target.

The process of determining the forecast date included in the alarm period of the item "edamame" by the forecasting unit 152 will be described. As shown in FIG. 7, there are two alarm periods for green soybeans, depending on the pest name and the set of growth stages.

First, the alarm period "8 / 1-8 / 15" will be described. During this alarm period, the name of the pest that damages green soybeans is "Dark sword grass" and the growth stage is "larva". The prediction unit 152 refers to the prediction date information 144, and among the larva hatching prediction dates of each generation of "Tamanayaga", the larva hatching prediction date included in the alarm period "8 / 1-8 / 15" is notified. Judge as the predicted date.

Next, the alarm period "6/7 to 9/5" will be described. During this alarm period, the names of pests that damage soybeans are "Spodoptera litura", "Mitsumonkinuwaba", and "Mameshiniga", and the growth stage is "larva". The prediction unit 152 refers to the prediction date information 144, and sets the alarm period "6 / 7-9 / 5" among the larva hatching prediction dates of each generation of "Spodoptera litura", "Mitsumonkinuwaba", and "Mameshiniga". The included larva hatching predicted date is determined as the predicted date to be notified.

By performing the above processing, the prediction unit 152 predicts the prediction date to be notified for each item and registers it in the prediction result information 146. When the prediction unit 152 receives the information that the alarm period table 145 has been updated from the update unit 153, which will be described later, the prediction unit 152 updates the prediction date information by performing the above processing again.

The update unit 153 is a processing unit that updates the alarm period in the alarm period table 145 when the update of the alarm period is received. When receiving the information on the growth record of the crop, the update unit 153 estimates the alarm period based on the growth record, and updates the alarm period table 145 based on the estimated alarm period.

For example, when the update unit 153 receives information on the heading date of paddy rice, the period from the received heading date to 10 days later is set as a new alarm period. When the update unit 153 receives the growth record information "item: paddy rice" and "heading date: 7/28" when the alarm period for the current paddy rice is "7/29 to 8/8", , Update the alarm period to "7 / 28-8 / 7". Further, when the alarm period table 145 is updated, the update unit 153 outputs the updated information to the prediction unit 152.

The notification unit 154 is a processing unit that notifies the user terminal 5 of the prediction result information 146 predicted by the prediction unit 152. For example, when the notification unit 154 notifies the user terminal 5 of the prediction result information 146, the notification unit 154 causes the user terminal 5 to display the information described with reference to FIGS. 8 to 11. Further, the notification unit 154 notifies the prediction unit 152 of only the predicted date (predicted date included in the alarm period) to be notified. Alternatively, the notification unit 154 may notify the predicted date of each generation and highlight the predicted date included in the alarm period.

In addition to the prediction results shown in FIGS. 8 to 11, the notification unit 154 may generate e-mail information as shown in FIG. 12 and notify the user terminal 5. FIG. 12 is a diagram showing an example of the content of the e-mail notified by the notification unit. For example, as shown in FIG. 12, the content of this email includes the place where the pest occurs, the name of the pest, the item, the generation, and the predicted date of occurrence (the predicted date to be notified). Further, in addition to the information in FIG. 12, information such as a predicted larva hatching date and a predicted spawning date may be included.

The notification unit 154 repeatedly executes the above process every time the reference date information is transmitted from the user terminal 5 and the prediction result information 146 is updated by the prediction unit 152. The notification unit 154 executes the above process when the prediction result information 146 is updated and the prediction date to be notified is included after the current date. On the other hand, the notification unit 154 suppresses the process of transmitting the prediction result information 146 and the mail to the user terminal 5 when the prediction date to be notified is not included after the current date.

Further, the notification unit 154 notifies the user terminal 5 of the changed prediction date information when the prediction result information 146 is updated by the prediction unit 152 and the prediction date within the alarm period is changed. You may.

Next, an example of the processing procedure of the prediction device 100 according to this embodiment will be described. FIG. 13 is a flowchart showing a processing procedure of the prediction device according to the present embodiment. As shown in FIG. 13, the acquisition unit 151 of the prediction device 100 receives the information of the reference date from the user terminal 5 (step S101).

The prediction unit 152 of the prediction device 100 acquires the pest growth temperature information 141 (step S102). The prediction unit 152 calculates the integrated temperature based on the second air temperature information 143 (step S103).

The prediction unit 152 predicts the hatching prediction date, the adult development prediction date, and the spawning prediction date of each generation by comparing the integrated temperature with the effective integrated temperature (step S104). The prediction unit 152 compares the alarm period with the hatching prediction date and the adult development prediction date of each generation, and identifies the hatching prediction date and the adult development prediction date of each generation included in the alarm period (step S105).

The notification unit 154 of the prediction device 100 notifies the user terminal 5 of the prediction result (step S106). When the processing is continued (step S107, Yes), the prediction device 100 proceeds to step S101. On the other hand, if the prediction device 100 does not continue the processing (steps S107, No), the prediction device 100 ends the processing.

Next, the effect of the prediction device 100 according to this embodiment will be described. The prediction device 100 calculates the date on which the pest occurs for each generation, starting from the reference date when the pest actually hatched. Then, the prediction device 100 uses the effective timing for exterminating the pests by identifying and notifying the dates within the alarm period in which the pests are damaged during the entire growth period of the crop among the calculated plurality of dates. Can be notified. Furthermore, by notifying the effective timing of exterminating pests, it is possible to eliminate unnecessary spraying of pesticides.

The prediction device 100 predicts the date of the first stage of hatching from an egg, the date of the second stage of becoming an adult from a larva, and the date of the third stage of spawning of an adult for each different generation as development dates. Therefore, the date of each growth stage included in the alarm period can be notified to the user.

The prediction device 100 updates the alarm period according to the growth record of the crop. Therefore, an appropriate alarm period can be set according to the growth record of the crop, and an effective timing for exterminating the pest can be notified more accurately.

Each time the prediction device 100 newly receives the information on the reference date, the prediction device 100 updates the prediction date based on the received information on the reference date. Since the forecast date is updated using the latest reference date, the forecast date included in the alarm period can be set to a more accurate date.

Since the prediction device 100 calculates the prediction date for each different pest, it is possible to notify the prediction date for each different pest included in the alarm period.

Next, an example of a computer hardware configuration that realizes the same functions as the prediction device 100 shown in the above embodiment will be described. FIG. 14 is a diagram showing an example of a hardware configuration of a computer that realizes the same function as the prediction device.

As shown in FIG. 14, the computer 200 includes a CPU 201 that executes various arithmetic processes, an input device 202 that receives data input from a user, and a display 203. Further, the computer 200 has a reading device 204 that reads a program or the like from a storage medium, and an interface device 205 that exchanges data between the recording device or the like via a wired or wireless network. Further, the computer 200 has a RAM 206 for temporarily storing various information and a hard disk device 207. Then, each of the devices 201 to 207 is connected to the bus 208.

The hard disk device 207 has a prediction program 207a, an update program 207b, and a notification program 207c. The CPU 201 reads out the programs 207a to 207f and deploys them in the RAM 206.

The prediction program 207a functions as a prediction process 206a. Update 207b functions as update process 206b. The notification program 207c functions as the notification process 206c.

The processing of the prediction process 206a corresponds to the processing of the prediction unit 152. The process of the update process 206b corresponds to the process of the update unit 153. The processing of the notification process 206c corresponds to the processing of the notification unit 154.

The programs 207a to 207c do not necessarily have to be stored in the hard disk device 207 from the beginning. For example, each program is stored in a "portable physical medium" such as a flexible disk (FD), a CD-ROM, a DVD disk, a magneto-optical disk, or an IC card inserted into the computer 200. Then, the computer 200 may read and execute each of the programs 207a to 207c.

5a, 5b, 5c User terminal 10a, 10b, 10c, 10d, 10e, 10f Meteorological robot 20 Server 30 AMeDAS 100 Predictor

Claims (7)

Meteorological information that links the date and temperature, the minimum temperature information required for the growth of pests, the information on the first integrated temperature required for the pests to move to the next growth stage, and the crop's A storage unit that stores information on the alarm period during which pests are damaged during the growth period,
When the information on the reference date when the pest hatched was received, the difference temperature between the temperature and the temperature information was calculated for each date with the reference date as the start date, and the difference temperature for each calculated date was integrated. The second integrated temperature is calculated, and based on the first integrated temperature and the second integrated temperature, the occurrence date at which the growth stage of the pest becomes a predetermined growth stage is predicted for each different generation. , A prediction device comprising a prediction unit for determining an occurrence date included in the alarm period among a plurality of predicted occurrence dates. The growth stage of the pest includes a first stage of hatching from an egg, a second stage of becoming an adult from a larva, and a third stage of spawning of an adult. The prediction device according to claim 1, wherein the date of the second stage and the date of the third stage are predicted as the occurrence date. The prediction device according to claim 1 or 2, further comprising an update unit that updates the alarm period according to the growth record of the crop. The method according to claim 1, 2 or 3, wherein the prediction unit updates the occurrence date based on the newly received reference date each time the information on the reference date is newly received. Predictor. The temperature information and the integrated temperature information are associated with each different pest, and the prediction unit predicts the occurrence date for each different pest, which is any one of claims 1 to 4. The predictor described in. It ’s a computer-based prediction method.
Meteorological information that links the date and temperature, the minimum temperature information required for the growth of pests, the information on the first integrated temperature required for the pests to move to the next growth stage, and the crop's Refer to the storage device that stores the information of the alarm period that is damaged by pests during the growth period.
When the information on the reference date when the pest hatched is received, the difference temperature between the temperature and the temperature information is calculated for each date with the reference date as the start date.
Calculate the second integrated temperature by integrating the calculated difference temperature for each date.
Based on the first integrated temperature and the second integrated temperature, the date of occurrence at which the growth stage of the pest becomes a predetermined growth stage is predicted for each different generation.
A prediction method characterized by executing a process of determining an occurrence date included in the alarm period among a plurality of predicted occurrence dates. On the computer
Meteorological information that links the date and temperature, the minimum temperature information required for the growth of pests, the information on the first integrated temperature required for the pests to move to the next growth stage, and the crop's Refer to the storage device that stores the information of the alarm period that is damaged by pests during the growth period.
When the information on the reference date when the pest hatched is received, the difference temperature between the temperature and the temperature information is calculated for each date with the reference date as the start date.
Calculate the second integrated temperature by integrating the calculated difference temperature for each date.
Based on the first integrated temperature and the second integrated temperature, the date of occurrence at which the growth stage of the pest becomes a predetermined growth stage is predicted for each different generation.
A prediction program characterized by executing a process of determining an occurrence date included in the alarm period among a plurality of predicted occurrence dates.

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