Sogatella furcifera occurrence degree prediction method, apparatus and device, and storage medium

Abstract

The invention provides a sogatella furcifera occurrence degree prediction method and device, equipment and a storage medium. The method comprises the steps that the adult density, the male-female ratio and the egg laying amount of a single female insect of sogatella furcifera are obtained; predicting the density of next generation of sogatella furcifera nymphs based on the adult density, the male-female ratio and the egg laying amount of a single female; and determining the sogatella furcifera occurrence degree based on the density of the next generation of sogatella furcifera nymphs. By means of the method, the sogatella furcifera occurrence degree can be accurately predicted, and therefore a foundation is laid for scientific pest control.

Description

Technical Field

[0001] The present disclosure relates to the technical field of pest prediction, and in particular to a method, device, equipment and storage medium for predicting the occurrence degree of white-backed planthopper. Background Art

[0002] White-backed planthopper [Sogatella furcifera (Horváth), belonging to the Delphacidae family, is one of the most important rice pests in my country and is included in the first category of my country's list of crop pests and diseases. In addition to directly feeding on phloem sap to cause harm to rice, white-backed planthoppers can also cause mechanical damage to rice during feeding and egg-laying, secretion of certain chemical substances (such as a variety of hydrolases), and excretion of "honeydew" on the surface of rice plants that is prone to breeding mold, which can cause the growth of rice to weaken or even die. In addition, it can also cause indirect harm to rice by spreading viruses such as southern rice black-streaked dwarf virus (SRBSDV). Accurately predicting the occurrence of white-backed planthoppers is a prerequisite for scientific pest control. Therefore, how to accurately predict the occurrence of white-backed planthoppers is a technical problem that needs to be solved urgently by technicians in this field. Summary of the invention

[0003] In view of this, the present disclosure proposes a method, device, equipment and storage medium for predicting the occurrence degree of white-backed plant hoppers, which can accurately predict the occurrence degree of white-backed plant hoppers.

[0004] According to a first aspect of the present disclosure, a method for predicting the occurrence degree of white-backed planthopper is provided, comprising:

[0005] Obtain the adult density, sex ratio and egg laying of single female of white-backed planthopper;

[0006] Predicting the density of next generation white-backed planthopper nymphs based on the adult density, the male-female ratio and the egg-laying amount of a single female;

[0007] Based on the density of white-backed plant hopper nymphs of the next generation, the occurrence degree of white-backed plant hopper is determined.

[0008] In a possible implementation, before obtaining the adult density, the female-male ratio and the egg laying amount of a single female of the white-backed planthopper, the following steps are performed:

[0009] An intelligent insect monitoring lamp is arranged in a rice field planting area, and white-backed planthoppers are attracted by the intelligent insect monitoring lamp;

[0010] Identifying white-backed plant hopper adults from the trapped white-backed plant hoppers, and counting the number of trapped white-backed plant hopper adults;

[0011] It is determined whether the number of traps is greater than a preset trap threshold value. When the number of traps is greater than the trap threshold value, a field survey is prompted to obtain the adult density and the male-female ratio of the white-backed plant hopper.

[0012] In a possible implementation, when obtaining the adult density and the sex ratio of the white-backed planthopper through field survey, it is achieved by performing cross sampling at 5 points in a preset range of rice field planting areas.

[0013] In a possible implementation, when determining the occurrence degree of white-backed plant hoppers based on the next generation white-backed plant hopper nymph density, the method includes:

[0014] The density of the next generation of white-backed planthopper nymphs is revised using a preset correction factor;

[0015] The occurrence extent of white-backed planthopper was determined based on the revised next generation white-backed planthopper nymph density.

[0016] In a possible implementation, the occurrence degree of the white-backed planthopper includes at least one of a light occurrence, a relatively light occurrence, a medium occurrence, a relatively heavy occurrence, and a large occurrence.

[0017] According to a second aspect of the present disclosure, a device for predicting the occurrence degree of white-backed planthoppers is provided, comprising:

[0018] A data acquisition module is used to obtain the adult density, male-female ratio and egg laying amount of a single female white-backed planthopper;

[0019] A density prediction module, used for predicting the density of next generation white-backed planthopper nymphs based on the adult density, the male-female ratio and the egg-laying amount of a single female;

[0020] The occurrence degree prediction module is used to determine the occurrence degree of white-backed plant hopper based on the density of the next generation of white-backed plant hopper nymphs.

[0021] In a possible implementation, when determining the occurrence degree of white-backed plant hopper based on the density of the next generation of white-backed plant hopper nymphs, the density prediction module is specifically used to:

[0022] The density of the next generation of white-backed planthopper nymphs is revised using a preset correction factor;

[0023] The occurrence extent of white-backed planthopper was determined based on the revised next generation white-backed planthopper nymph density.

[0024] In a possible implementation, the occurrence degree of the white-backed planthopper includes at least one of a light occurrence, a relatively light occurrence, a medium occurrence, a relatively heavy occurrence, and a large occurrence.

[0025] According to a third aspect of the present disclosure, a device for predicting the occurrence degree of white-backed planthoppers is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the method described in the first aspect of the present disclosure.

[0026] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions, when executed by a processor, implement the method described in the first aspect of the present disclosure.

[0027] In the present disclosure, a method for predicting the occurrence of white-backed planthoppers includes: obtaining the adult density, male-female ratio and egg-laying amount of a single female of the white-backed planthopper; predicting the density of the next generation of white-backed planthopper nymphs based on the adult density, male-female ratio and egg-laying amount of a single female; and determining the occurrence of the white-backed planthoppers based on the density of the next generation of white-backed planthopper nymphs. The method disclosed in the present disclosure can accurately predict the occurrence of the white-backed planthoppers, thereby laying a foundation for scientific pest control.

[0028] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0030] Figure 1 A flow chart showing a method for predicting the occurrence degree of white-backed planthoppers according to an embodiment of the present disclosure;

[0031] Figure 2 A schematic block diagram showing a device for predicting the occurrence degree of white-backed planthoppers according to an embodiment of the present disclosure is shown;

[0032] Figure 3 A schematic block diagram of a device for predicting the occurrence degree of white-backed planthoppers according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0033] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0034] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0035] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0036] <Method Example>

[0037] Figure 1 A flow chart of a method for predicting the occurrence of white-backed planthoppers according to an embodiment of the present disclosure is shown. Figure 1 As shown, the method includes steps S1100-S1300.

[0038] S1100, obtain the adult density, sex ratio and egg laying number of single female white-backed planthopper.

[0039] In a possible implementation, before obtaining the adult density, male-female ratio, and egg laying amount of a single female white-backed planthopper, the following steps need to be performed:

[0040] First, an intelligent insect monitoring lamp is installed in the rice field planting area, and the intelligent insect monitoring lamp is used to attract white-backed planthoppers. Specifically, an unobstructed position is selected in the center of the rice planting area to install the intelligent insect monitoring lamp, so as to attract the migrating white-backed planthoppers through the intelligent insect monitoring lamp. Among them, the model of the intelligent insect monitoring lamp can be JDCB type, which can attract white-backed planthoppers within 2,000 mu.

[0041] It should be noted here that the smart insect monitoring lights should be started after 80% of the rice seedlings within the coverage area of ​​the smart insect monitoring lights have been transplanted and all the transplants in the area where the smart insect monitoring lights are located have been completed, so as to improve the accuracy of later predictions.

[0042] Second, the adult white-backed plant hoppers were identified from the trapped white-backed plant hoppers, and the number of trapped white-backed plant hopper adults was counted.

[0043] Specifically, the intelligent insect monitoring light will perform image recognition on the lured white-backed planthoppers at preset time intervals, so as to identify the number of white-backed planthopper adults lured in each time period through image recognition technology.

[0044] After the intelligent insect monitoring light identifies the number of white-backed planthopper adults lured in each time period, it uploads it to the system that executes the method disclosed in the present invention. In this way, the system can obtain the number of white-backed planthopper adults lured in each time period and generate a white-backed planthopper migration curve.

[0045] It should be noted here that the intelligent insect monitoring light can upload the number of white-backed plant hopper adults lured in each time period in real time, and can also upload the number of white-backed plant hopper adults lured in each time period according to preset time points. For example, the number of white-backed plant hopper adults lured in each time period in the current period can be automatically uploaded at 0:00 every day, without specific limitation here.

[0046] Third, determine whether the amount of white-backed planthopper adults trapped is greater than the preset trapping threshold. When the amount of trapping is greater than the trapping threshold, it is prompted to conduct a field survey to obtain the adult density and male-female ratio of white-backed planthoppers through field surveys.

[0047] Specifically, after generating the migration curve of white-backed planthoppers, the system will judge whether the peak value of the daily trapping amount is greater than the preset trapping threshold value according to the generated migration curve of white-backed planthoppers, and when it is judged to be greater than the preset trapping threshold value, it is determined that the peak migration period of white-backed planthoppers has arrived, and at this time, a prompt will be given to carry out the operation of field investigation. When giving the prompt, the prompt can be given through text messages such as text messages, messages, etc., or through sounds such as alarms, which are not specifically limited here. Among them, the trapping threshold value can be set to 10, that is, when the trapping amount of white-backed planthopper adults is greater than 10, a prompt will be given to carry out field investigation.

[0048] In a possible implementation, when obtaining the adult density and male-female ratio of white-backed planthoppers through field surveys, it is achieved by performing 5-point cross sampling on the rice field planting area within a preset range. Specifically, within the preset range, 5-point cross sampling is adopted, 50 samples are surveyed at each sampling point, parallel jump sampling is adopted for each sample, and visual survey is conducted. Finally, the total number of white-backed planthopper adults and the female ratio surveyed within the preset range are counted, and the total number of white-backed planthopper adults is converted into the number of adults per hundred samples, which is the adult density of white-backed planthoppers. Among them, the calculation formula for the number of adults per hundred samples is as follows:

[0049] Number of adults per 100 clumps = (number of adults surveyed / number of clumps surveyed) * 100

[0050] In the formula, the number of surveyed adults refers to the total number of white-backed planthopper adults surveyed within the preset range, and the number of surveyed clusters refers to the number of all surveyed clusters within the preset range.

[0051] In a possible implementation, when obtaining the egg laying amount of a single female white-backed planthopper, the following steps may be included:

[0052] First, based on the number of white-backed planthopper adults trapped, the peak period of white-backed planthopper adult migration is determined. Specifically, as described above, after obtaining the number of white-backed planthopper adults trapped in each time period, the system will generate a white-backed planthopper migration curve, and then take the time on the white-backed planthopper migration curve when the peak value of the single-day trapped amount is greater than the trapping threshold as the peak period of white-backed planthopper adult migration.

[0053] Second, the average temperature within the set time after the peak migration period is counted. Specifically, in addition to the intelligent insect monitoring lamp, the rice field planting area is also provided with a field weather monitor (the model can be MLS-1306). After the system determines that the white-backed planthopper adults have entered the peak migration period, it will start the field weather monitor to monitor the temperature, humidity, rainfall and other meteorological information within the set time after the peak migration period, and calculate the average temperature within the set time after the peak migration period. Among them, the set time after the peak migration period can be determined according to the time when the female insect enters the egg-laying period. For example, in the case where the female insect enters the egg-laying period 5 days after migration, the set time is set to 5 days, that is, the average temperature within 5 days after the peak migration period is counted.

[0054] Third, based on the average temperature, obtain the egg-laying amount of a single female insect of the white-backed planthopper. It should be noted here that the egg-laying amount of each female insect varies with the temperature. At 18-25°C, the egg-laying amount of a single female insect is 119 eggs, at 26-28°C, the egg-laying amount of a single female insect is 293 eggs, at 29-32°C, the egg-laying amount of a single female insect is 104 eggs, and when the temperature exceeds 33°C, no eggs are laid, that is, the egg-laying amount of a single female insect is 0. Therefore, the mapping relationship between the above temperature range and the egg-laying amount of a single female insect can be pre-constructed. In this way, after obtaining the above average temperature, the egg-laying amount of a single female insect of the white-backed planthopper can be obtained by querying the constructed mapping relationship.

[0055] After obtaining the adult density, male-female ratio and egg laying amount of a single female of the white-backed plant hopper, step S1200 can be performed to predict the next generation white-backed plant hopper nymph density based on the adult density, male-female ratio and egg laying amount of a single female. Specifically, the product of the adult density, male-female ratio and egg laying amount of a single female can be used as the next generation white-backed plant hopper nymph density.

[0056] In one possible implementation, in order to improve the accuracy of the prediction of the occurrence degree of white-backed planthoppers, after calculating the density of white-backed planthopper nymphs of the next generation, the density of white-backed planthopper nymphs of the next generation will be revised using a pre-set correction coefficient; and then the occurrence degree of white-backed planthoppers will be determined based on the revised density of white-backed planthopper nymphs of the next generation.

[0057] In this embodiment, the revised calculation formula for the next generation white-backed planthopper nymph density is as follows:

[0058] f(x)=E*ε*α*x

[0059] Where f(x) is the revised density of next generation white-backed planthopper nymphs, E is the male-female ratio, α is the number of eggs laid by a single female, x is the adult density, and ε is the correction coefficient.

[0060] In a possible implementation, the correction coefficient is determined based on the egg laying amount, egg hatching rate and nymph development rate of a single female white-backed planthopper. Specifically, the egg laying amount, egg hatching rate and nymph development rate of a single female white-backed planthopper can be multiplied, and the product of the three can be used as the correction coefficient ε. For example, within the scope of Guizhou Province, the egg laying amount of a single female varies due to different temperatures. According to observations, it is about 90%, and the nymph egg hatching rate is about 85%. At the same time, according to observations, about 10% of the hatched nymphs cannot develop normally due to various factors. Therefore, the correction coefficient can be taken as 0.689.

[0061] S1300, based on the density of the next generation of white-backed planthopper nymphs, determine the occurrence degree of white-backed planthoppers. Specifically, the occurrence degree of white-backed planthoppers includes at least one of light occurrence, light occurrence, medium occurrence, heavy occurrence and large occurrence. For each occurrence degree, a corresponding density range of the next generation of white-backed planthopper nymphs is set. In this way, after calculating the density of the next generation of white-backed planthopper nymphs, the corresponding occurrence degree of white-backed planthoppers can be determined by the density range to which it belongs. Among them, each occurrence degree is set with a corresponding density range of the next generation of white-backed planthopper nymphs density, which can be shown in Table 1:

[0062] Table 1

[0063] level 1 2 3 4 5 degree Light Occurrence Mild occurrence Moderate occurrence Occurrence Big Happening Hundreds of insects (head) <500 500～1000 1000～2000 2000～3000 >3000

[0064] The present disclosure provides a method for predicting the occurrence degree of white-backed planthoppers, including obtaining the adult density, male-female ratio and egg laying amount of single female white-backed planthoppers; predicting the nymph density of the next generation of white-backed planthoppers based on the adult density, male-female ratio and egg laying amount of single female white-backed planthoppers; and determining the occurrence degree of white-backed planthoppers based on the nymph density of the next generation of white-backed planthoppers. The method disclosed in the present disclosure can accurately predict the occurrence degree of white-backed planthoppers, thereby laying a foundation for scientific pest control.

[0065] <Device Example>

[0066] Figure 2 FIG. 1 is a schematic block diagram of a device for predicting the occurrence of white-backed planthoppers according to an embodiment of the present disclosure. Figure 2 As shown, the device 100 includes:

[0067] The data acquisition module 110 is used to obtain the adult density, male-female ratio and egg laying amount of a single female white-backed planthopper;

[0068] A density prediction module 120 is used to predict the density of next generation white-backed planthopper nymphs based on the adult density, the male-female ratio and the egg-laying amount of a single female;

[0069] The occurrence degree prediction module 130 is used to determine the occurrence degree of white-backed plant hopper based on the density of white-backed plant hopper nymphs of the next generation.

[0070] In a possible implementation, when determining the occurrence degree of white-backed plant hoppers based on the density of next-generation white-backed plant hopper nymphs, the density prediction module is specifically used to:

[0071] The pre-set correction factor is used to revise the density of white-backed planthopper nymphs in the next generation;

[0072] The occurrence extent of white-backed planthopper was determined based on the revised next generation white-backed planthopper nymph density.

[0073] In a possible implementation, the occurrence degree of white-backed planthoppers includes at least one of light occurrence, relatively light occurrence, moderate occurrence, relatively heavy occurrence and severe occurrence.

[0074] <Equipment Embodiment>

[0075] Figure 3 FIG. 1 is a schematic block diagram of a device for predicting the occurrence of white-backed planthoppers according to an embodiment of the present disclosure. Figure 3 As shown, the device 200 for predicting the occurrence degree of white-backed planthoppers includes: a processor 210 and a memory 220 for storing executable instructions of the processor 210. The processor 210 is configured to implement any of the above-mentioned methods for predicting the occurrence degree of white-backed planthoppers when executing the executable instructions.

[0076] Here, it should be noted that the number of processors 210 can be one or more. At the same time, in the white-backed planthopper occurrence prediction device 200 of the embodiment of the present disclosure, an input device 230 and an output device 240 may also be included. Among them, the processor 210, the memory 220, the input device 230 and the output device 240 may be connected through a bus or in other ways, which are not specifically limited here.

[0077] The memory 220 is a computer-readable storage medium that can be used to store software programs, computer executable programs, and various modules, such as the program or module corresponding to the method for predicting the occurrence degree of white-backed planthoppers in the embodiment of the present disclosure. The processor 210 executes various functional applications and data processing of the device 200 for predicting the occurrence degree of white-backed planthoppers by running the software programs or modules stored in the memory 220.

[0078] The input device 230 may be used to receive input numbers or signals. The signals may be key signals related to user settings and function control of the device / terminal / server. The output device 240 may include display devices such as display screens.

[0079] <Storage Medium Embodiment>

[0080] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium is also provided, on which computer program instructions are stored. When the computer program instructions are executed by the processor 210, any of the above-mentioned methods for predicting the occurrence degree of white-backed planthoppers is implemented.

[0081] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting the occurrence degree of white-backed planthopper, characterized in that: include: Obtain the adult density, sex ratio and egg laying of single female of white-backed planthopper; Predicting the density of next generation white-backed planthopper nymphs based on the adult density, the male-female ratio and the egg-laying amount of a single female; Based on the density of white-backed plant hopper nymphs of the next generation, the occurrence degree of white-backed plant hopper is determined.

2. The method according to claim 1, characterized in that Before obtaining the adult density, sex ratio and egg laying of single female white-backed planthoppers, the following should be included: An intelligent insect monitoring lamp is arranged in a rice field planting area, and white-backed planthoppers are attracted by the intelligent insect monitoring lamp; Identifying white-backed plant hopper adults from the trapped white-backed plant hoppers, and counting the number of trapped white-backed plant hopper adults; It is determined whether the number of traps is greater than a preset trap threshold value. When the number of traps is greater than the trap threshold value, a field survey is prompted to obtain the adult density and the male-female ratio of the white-backed plant hopper.

3. The method according to claim 2, characterized in that When obtaining the adult density and the sex ratio of the white-backed planthopper through field survey, it is achieved by performing cross sampling at 5 points in the rice field planting area within a preset range.

4. The method according to claim 1, characterized in that When determining the occurrence degree of white-backed plant hopper based on the density of white-backed plant hopper nymphs of the next generation, it includes: The density of the next generation of white-backed planthopper nymphs is revised using a preset correction factor; The occurrence extent of white-backed planthopper was determined based on the revised next generation white-backed planthopper nymph density.

5. The method according to claim 1, characterized in that The occurrence degree of the white-backed planthopper includes at least one of a mild occurrence, a relatively mild occurrence, a moderate occurrence, a relatively severe occurrence and a large occurrence.

6. A device for predicting the occurrence of white-backed planthoppers, characterized in that: include: A data acquisition module is used to obtain the adult density, male-female ratio and egg laying amount of a single female white-backed planthopper; A density prediction module, used for predicting the density of next generation white-backed planthopper nymphs based on the adult density, the male-female ratio and the egg-laying amount of a single female; The occurrence degree prediction module is used to determine the occurrence degree of white-backed plant hopper based on the density of the next generation of white-backed plant hopper nymphs.

7. The device according to claim 6, characterized in that When determining the occurrence degree of white-backed plant hopper based on the density of the next generation white-backed plant hopper nymphs, the density prediction module is specifically used to: The density of the next generation of white-backed planthopper nymphs is revised using a preset correction factor; The occurrence extent of white-backed planthopper was determined based on the revised next generation white-backed planthopper nymph density.

8. The device according to claim 6, characterized in that The occurrence degree of the white-backed planthopper includes at least one of a mild occurrence, a relatively mild occurrence, a moderate occurrence, a relatively severe occurrence and a large occurrence.

9. A device for predicting the occurrence of white-backed planthoppers, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the method described in any one of claims 1 to 5 when executing the executable instructions.

10. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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