JP2002305971A - Method for forecasting crop disease and system therefor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for forecasting a crop disease that realizes prediction of economical efficiency of a pesticide spray and a system therefor. SOLUTION: The method comprises a processing for asking the application conditions of the pesticide from the amount of an infection source giving crops disease damages and a permissible percentage of the damage (application conditions input processing 418), a processing for calculating the final damage (final damage calculating processing 414), and a processing for determining whether the application of the pesticide is suitable or not (judging processing 424), whereby the crop damage by the disease is predicted and whether the application of the pesticide is needed or not is judged to increase the economy of the application of the pesticide, and environmental pollution also can be inhibited, since the frequency and volume of pesticide application can be reduced within the region.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and system for predicting disease occurrence used for predicting disease occurrence in a field such as a tea plantation.

[0002]

2. Description of the Related Art Conventionally, there has been a system which obtains a suitable date for rice blast disease by using weather information such as rainfall, humidity, wind speed and the like, and predicts the amount of rice blast disease and the progress of the disease. The purpose of this system is to determine the appropriate time for drug application from the preferred date of disease transmission.

[0003]

However, in the conventional system, although it is possible to obtain a suitable period for spraying the medicine,
Neither the necessity of spraying nor the cost is determined. For this reason, even if the user knows the appropriate time for spraying the medicine, the necessity of the spraying of the medicine, the economic efficiency, and the like still depend on intuition and experience.

Accordingly, an object of the present invention is to provide a disease occurrence prediction method and a system for realizing the prediction of the economic efficiency of drug application.

[0005]

The disease occurrence forecasting method according to the present invention comprises a process of obtaining a spraying condition of a medicine from a crop of interest, a source of the disease of interest and an allowable damage rate (spraying condition input process 41).
8), a process of calculating the final damage amount of the crop (final damage amount calculation process 414), and a process of determining whether or not the medicine is to be sprayed on the crop (spraying appropriateness determination process 424). I do. In other words, chemicals that protect crops from diseases are not always effective from the viewpoint of environmental pollution and economics, and it is necessary to predict the damage situation due to crop diseases and determine whether or not chemical spraying is necessary. Accordingly, it is possible to improve the economics of spraying the medicine, and to suppress the environmental pollution and the like by reducing the amount of spraying the medicine and the number of times of spraying in a region or the like.

The method for predicting disease occurrence according to the present invention comprises the following steps: the wet time of the leaves of a crop, temperature, humidity or rainfall, or data including these (measurement data 402); And from the correlation with the optimal time of drug application,
A process of determining a suitable infection date, a disease incidence, and a suitable period of application of the medicine of the disease source (judgment process 408), a process of obtaining the application condition of the drug from the amount of the infection source and the permissible damage ratio (application process 418). A process of calculating the final damage amount of the crop based on data representing or including sensitivity and weather conditions of the crop at each development stage (final damage amount calculation process 414); Determining whether or not the medicine is to be sprayed (spraying determination processing 424). That is, in addition to the conventional determination of the appropriate period of the application of the medicine, the necessity of the application of the medicine, the cost, and the like are also determined, so that the amount of the applied medicine can be reduced.

A disease occurrence forecasting system according to the present invention is a disease occurrence forecasting system for predicting a disease occurrence of a crop by using a computer. Or a means (sensor group 16, central processing unit 4) for fetching data including these in time series and storing the data in the storage means (RAM 12), the data stored in the storage means, and the infection related to the disease. A means for determining a suitable infection date, a disease incidence and a suitable time for spraying the drug from the correlation between the date, the disease incidence and a suitable time for spraying the drug (central processing unit 4); and a means for capturing the spraying condition of the drug from the amount of the source of infection and the allowable damage rate. (Central processing unit 4),
A means (central processing unit 4) for calculating the final damage amount of the crop based on data representing or showing sensitivity and weather conditions for each development stage of the crop, and determining whether or not the chemical is to be sprayed based on the processing. Determination means (central processing unit 4). In other words, this is a system for performing the disease occurrence prediction method, which also determines whether or not it is necessary to spray a medicine, the cost, etc., and makes it possible to reduce the amount of sprayed medicine.

[0008]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention and embodiments thereof will be described below in detail with reference to embodiments shown in the drawings.

FIG. 1 and FIG. 2 show an embodiment of a disease occurrence prediction method and system according to the present invention. FIG. 1 shows the computer system, and FIG. 2 shows the system configuration.

The disease occurrence prediction method and system include a database 2, a central processing unit 4, a memory 6, an input device 8, an output device 10, a RAM 12 as storage means, and the like, and a bus for transmitting and receiving data and the like. It comprises a computer linked in 14.

The database 2 includes, as background data, first, based on the past and past occurrences of various diseases, the past and past occurrences of normal diseases, the amount of overwintering of the germs and bacteria from the previous year, the amount of previous occurrence, and the variety characteristics of the target crop. Pathogenesis data, second, effects of drugs (protective / therapeutic agents), runoff / decomposition models, third
In addition, the relationship with the amount of various diseases caused by differences in fertilizer components,
Fourth, data on invasion, latentness, disease occurrence, and the like of disease and bacteria during the growth process of the target crop for disease prediction are stored.

The central processing unit 4 is a data processing means, which measures the degree of wetness of the leaves of the crop, the time series measurement processing of the weather condition data and the data accumulation processing, the suitable date of disease infection, the disease incidence, the chemical spraying. Judgment processing such as appropriate time, sensitivity at each development stage, final damage amount calculation processing from weather conditions, etc., input of drug spraying conditions from infectious sources, damage tolerance rate, etc.,
Processing such as the application cost of the medicine, the applicability of the medicine, and the suitability judgment processing of the optimum medicine and the like are performed.

The memory 6 stores fixed data and the like in addition to the processing program.

The input device 8 includes a sensor group 16 as a sensor group 16 and a wet leaf sensor 20 for measuring the degree of wetting of the crop via a measuring unit 18, and a temperature measuring means for measuring weather conditions as environmental conditions of the crop. Measurement data is added from a sensor 22, a humidity sensor 24 for measuring humidity, a rainfall sensor 26 for measuring rainfall, and the like. The wet leaf sensor 20 is a means for electrically detecting, for example, the wetness of the leaf of the crop and the wet state due to evaporation thereof, and obtains data different from the humidity in the air. Further, a keyboard 28 or the like is connected to the input device 8 as other input means, and the spraying conditions of the medicine, the amount of the infectious source, the damage tolerance, and the like are input.

A display device 30 and a printer 32 are connected to the output device 10.
First, suitable dates for disease infection, disease incidence, appropriate time for drug application, etc.
Secondly, the final damage amount, thirdly, the spraying cost of the medicine, whether or not the medicine is sprayed, the optimum medicine, and the like are displayed. These display outputs are also printed by the printer 32.

The RAM 12 temporarily stores data applied to the input device 8 and data output from the output device 10, and is protected from loss due to a power failure or the like by a backup power supply (not shown). .

As shown in FIG. 2, a system realized by using this computer executes measurement data 40 from the sensor group 16 as processing in the central processing unit 4.
2, a measurement process 404, a determination process 408 for obtaining a determination result 406 of a suitable disease infection date, a disease incidence, or a drug spraying appropriate time period based on the measurement process 404 and the measurement data. Damage amount calculation processing 414 for obtaining the final damage amount 412 from input data 410 such as sensitivity, weather condition, etc. for each development stage representing
And input data 4 of the amount of transmission source and damage tolerance
Perform 16 spraying condition input processes 418, and
Data 420 obtained from the above, spraying condition input processing 41
8 and the result of the final damage amount calculation processing 414, the spraying cost of the medicine, the suitability of spraying, and the judgment result 42 of the optimum medicine.
2 is performed.

That is, in the measurement processing 404, the wet time measured by the wet leaf sensor 20, the temperature condition data measured by the temperature sensor 22, the humidity sensor 24 and the rainfall sensor 26, the weather condition data such as the rainfall and the rainfall sensor 26, and other sensors. Is stored as time-series data. The measurement data 402 is stored in the RAM 12. In the determination process 408, a suitable infection date, disease incidence, and a suitable time for spraying the medicine are obtained. In the final damage amount calculation processing 414, the final damage amount 412 is calculated based on the sensitivity of each development stage of the crop, weather conditions, and the like. In the spraying condition input process 418, the infectious source amount and the damage tolerance in the crop are input from the keyboard 28 or the like.
It may be input from an external storage device such as a floppy disk. Then, in the spraying propriety determination processing 424, the propriety of the drug spraying is determined based on various conditions such as measurement data.

By the way, the spraying condition input processing 418 may be input by a human or may be automatically input using a sensor. Furthermore, the damage tolerance may be automatically determined by extracting the target disease and the price and work amount of the medicine used for the target disease from the database 2 and the like. For example, a medicine P is used for treating a disease Q, and the medicine P takes x circles per 10 ares and the spraying time is y hours per 10 ares. This cost is equivalent to z% of the total harvest when converted to the harvest.

Based on such processing, measurement processing 404
Based on the data measured in step 408, a suitable infection date, disease incidence and a suitable period of application of the medicine are determined in a judgment process 408, and in the final damage amount calculation process 414, the amount of the infectious source and the number of disease (leaf / M ² )
The final damage amount 412 is calculated by referring to the wet leaf data, the weather condition data, and the like, and taking into account the sensitivity of each growth stage of the crop. Then, in the spraying propriety determination processing 424, the propriety of spraying is determined by comparing with the cost required for responding to these calculation results.

Therefore, this disease occurrence forecasting method and system can determine a suitable date of infection, predict the amount of disease occurrence and the progress of disease onset, and use the amount of infectious sources and the allowable damage rate to reduce the economic efficiency of drug application. That is, the cost B required for spraying the medicine is compared with the damage A when the spraying is not performed. When A> B, the spraying of the medicine is determined to be necessary, which contributes to the improvement of the cultivation efficiency.

Next, FIG. 3 shows a process for predicting the preferred date of infection in this disease occurrence prediction method and its system. In this embodiment, tea is described as an example of a target crop.

In this process, in step S1, as a target crop, for example, the wet time H (h) corresponding to the disease of tea leaves,
The average temperature T (° C.) and the average humidity W (%) are determined. For example, H = 10, T = 22, and W = 80 are determined as predetermined values.

After the determination of these three conditions, in step S2, measurement of the wet time is started from 0 hour, and in step S3
Then, the state is grasped from the measurement data 402 of the sensor group 16. That is, weather condition data such as the wet state of tea leaves, temperature, humidity, and rainfall are grasped.

In step S4, it is determined whether or not the tea leaves are wet. If not, the process returns to step S2, and if so, the process proceeds to step S5. In step S5, the wet time is measured, and after a predetermined time, the process proceeds to step S6.

In step S6, the wet time is a predetermined time H
(= 10) It is determined whether the value is larger than (h). If the value is not larger, the condition is not a suitable condition for infection.
The process returns to step S7 if the value is large.

In step S7, the average temperature is set to the predetermined temperature T.
(= 22) It is determined whether or not it is larger than (° C.). If it is not larger, it is not a suitable condition for infection.
Returning to step S8, if it is large, the process proceeds to step S8.

In step S8, the average humidity is equal to the predetermined humidity W.
(= 80) (%) is determined, and if not, it is not a suitable condition for infection.
The process returns to step S9 if the value is large.

In step S9, a suitable infection date at which the tea leaves are infected with the disease is determined from the three conditions of the wet time H, the average temperature T, and the average humidity W. In step S10, the determination result is notified, and the next processing is performed. Transition. This notification content is output by the display device 30 or the printer 32.

Next, FIG. 4 shows a final damage amount calculation process 414 after determining a suitable date of disease transmission.

In step S11, crop type recognition (K) for the target crop type is performed. In step S12,
The pathogens causing the disease are identified (D), and step S
In step 13, the disease incidence rate recognition (N) for the disease incidence rate is performed. In the case of tea leaves, the disease incidence rate is the number of leaves per unit area (N / m ² ).

In step S14, the growth state of the tea is recognized (S), and in step S15, the recognition of the presence or absence of tea leaves (P) is performed. That is, regarding the growth stage of tea, the presence or absence of plucking before and after the S-th tea is grasped. After grasping these recognitions, in step S16,
As the final damage amount calculation by the diseased bacteria and progeny, the damage X is obtained from X = f (K, D, N, S, P) (1) and displayed on the display device 30. This damage X
Represents the final damage amount. In this case, the disease and crop data and progeny data are obtained from the database 2 for each disease, crop variety, and development stage, and the number of days of invasion, the number of incubation days, and the number of disease days are obtained.

Next, FIG. 5 shows the final damage amount calculation processing 414.
424 shows a chemical spraying propriety determination process 424 based on the.

In step S21, recognition of the crop variety (K), which is the necessity of spraying the chemical, is performed. In step S22, recognition of the pathogenic bacteria (D), which is the cause of the disease, is performed. Perform state recognition (S),
In step S24, a pruning presence / absence recognition (P) regarding the presence / absence of plucking of tea leaves is performed. In step S25, the sprayed medicine and the cost (B) are calculated for the medicine to be sprayed, and in step S26, the final damage rate recognition (X) when the medicine is not sprayed is performed. The relationship between the final damage rate and the final damage amount is obtained by multiplying the final damage rate by the harvest amount. Then, step S27
Then, the amount of damage (A) is calculated for the tea leaf type because the amount of damage differs for each tea leaf.

In step S28, the calculated damage amount A is compared with the cost B. If A is not larger than B, the process proceeds to step S29, where it is determined that the spraying of the medicine is unnecessary, and the display device displays the result. 30 and the printer 32,
This processing ends. If A is larger than B, the process proceeds to step S30, where it is determined that the medicine needs to be sprayed, and the result is output to the display device 30 or the printer 32, and similarly, this process ends.

As described above, the allowable damage level is determined for each disease, crop variety, and development stage from the database 2, and the final damage rate (X sheets / m ² ) corresponds to, for example, a bud number type and a bud weight type as a tea leaf type. The disease, crop varieties and development stage are determined, and the final damage rate is multiplied by the yield to obtain the final damage amount. Thus, by comparing the damage amount A calculated from these data with the cost B, it is determined whether or not the medicine needs to be sprayed. That is, the necessity of spraying the chemical is determined based on not only the intuition and experience of the worker but also the scientific analysis, and the cultivation efficiency can be improved by referring to the determination result.

In the embodiment, tea leaves have been described as an example. However, the target crop for predicting the occurrence of disease may be rice, vegetables, or the like in addition to tea, and the present invention can be applied to various crops. Things.

[0038]

As described above, according to the present invention,
The following effects are obtained. a It is possible to predict the economic efficiency of the chemicals to be sprayed on the crop, and increase the cultivation efficiency. b) It is possible to determine the optimal date of infection and predict the economic efficiency of drug application from the amount of infectious sources and damage tolerance in addition to predicting the amount of disease and the progress of disease. c) It is easy to predict the optimal control time of chemicals such as protective / therapeutic agents, to select / instruct the optimal chemical, and to judge the necessity of spraying control necessity. d By the optimal control, reduced pesticide cultivation can be performed, and a crop with high environmental protection and high safety can be obtained. e Can reduce drug resistance and resurgence problems. f. Occurrence prediction based on differences between crop varieties is possible. g By using the simulation model, it can be used for forecasting occurrence by region based on weather forecast data, and it is possible to formulate drug spraying by region.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an embodiment of a disease occurrence prediction method and system according to the present invention.

FIG. 2 is a block diagram showing a system configuration in an embodiment of the disease occurrence prediction method and the system according to the present invention.

FIG. 3 is a flowchart showing a suitable infection date grasping process.

FIG. 4 is a flowchart showing a final damage amount calculation process.

FIG. 5 is a flowchart showing a process for determining whether or not a medicine is to be sprayed;

[Explanation of symbols]

 4 Central Processing Unit 16 Sensor Group 20 Wet Leaf Sensor 22 Temperature Sensor 24 Humidity Sensor 26 Rainfall Sensor 402 Measurement Data 408 Judgment Processing 414 Final Damage Amount Calculation Processing 418 Spraying Condition Input Processing 424 Spraying Appropriate / Abstract Determination Processing

Claims (3)

[Claims] 1. A process for obtaining a condition for spraying a medicine from an amount of an infectious source causing a disease to a crop and a damage tolerance, a process for calculating a final damage amount of the crop, and judging whether or not the spraying of the drug on the crop is appropriate. A method for predicting disease occurrence, comprising: 2. The disease is obtained from the correlation between the wet time, temperature, humidity, or rainfall of the leaves of the crop, or data including these, and the suitable date of infection of the disease transmission source, the disease incidence, and the optimal time of application of the drug. A process of determining a suitable date of infection of the source of infection, a disease incidence and a suitable period of application of the drug; a process of obtaining a condition of application of the drug from the amount of the source of infection and a permissible damage ratio; and representing sensitivity and weather conditions for each development stage of the crop. A disease occurrence prediction method, comprising: a process of calculating a final damage amount of the crop based on data or data including the same; and a process of determining whether or not the chemical is sprayed on the crop based on the process. . 3. A disease occurrence forecasting system for predicting disease occurrence of a crop using a computer, comprising: a wet time of leaf, a temperature,
Humidity or rainfall, or data containing these in a time series, means for storing in the storage means, the data stored in the storage means, the suitable date of infection related to the disease, the disease incidence and the appropriate time of drug spraying A means for determining a suitable infection date, a disease incidence rate, and a suitable time for spraying the drug from the correlation; a means for capturing the spraying condition of the drug from the amount of the source of infection and the permissible damage rate; and data representing the sensitivity and weather conditions for each growth stage of the crop. A disease occurrence forecasting system comprising: means for calculating the final damage amount of the crop based on data including the above; and means for determining whether or not the chemical is to be sprayed based on the processing.