CN114723118A - Insect pest early warning system based on Internet of things - Google Patents
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Abstract
The invention discloses a pest early warning system based on the Internet of things, which comprises a data acquisition subsystem, a data storage subsystem, a data analysis subsystem and a pest early warning subsystem, wherein the data acquisition subsystem is used for acquiring data; the data acquisition subsystem comprises an environmental information acquisition module and an image acquisition module; the data storage subsystem comprises a pest basic database, a crop characteristic database and an environmental information database; and the data analysis subsystem is used for processing the data acquired by the data acquisition subsystem and outputting the processed data to the pest early warning subsystem through the crop phenological period prediction model and the pest effective accumulated temperature prediction model in combination with the pest basic database and the prediction characteristics of each model. The invention predicts the occurrence date of each pest state of different pests by combining the characteristics of the crop phenological period prediction model and the pest effective accumulated temperature prediction model, has accurate and effective prediction, is beneficial to the development of the pre-prevention and control work of crops, and has simple operation and larger application value.
Description
Technical Field
The invention relates to the technical field of agricultural Internet of things, in particular to a pest early warning system based on the Internet of things.
Background
The whole growth cycle from planting to harvesting of crops is always affected by various insect pests, and if the control is not timely carried out, the yield and the quality are reduced, and even great economic loss is caused in severe cases. The traditional control method requires a plant protection technician to enter the field and observe whether the crops have insect attack or not, the method is time-consuming and labor-consuming, some insects are preventable and cannot be controlled, and the optimal application time is possibly missed when the insects are found.
At present, informatization technology is gradually applied to agriculture. In the existing crop pest early warning method in the market, generally, pests are photographed and returned through Internet of things image acquisition equipment, the occurrence and severity of the crop pests are judged through machine learning and image recognition technology, and then corresponding prevention measures are taken. However, the image acquisition equipment required by the crop pest early warning method is expensive, the coverage area of a single land is limited, and a large amount of image acquisition equipment is usually required to be paved on one land, so that the cost is too high for common growers, the operation is difficult, and the popularization is very difficult.
There is currently no effective solution to these problems.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides a pest early warning system based on the Internet of things, which can overcome the defects in the prior art.
In order to achieve the technical purpose, the technical scheme of the invention is realized as follows:
a pest early warning system based on the Internet of things comprises a data acquisition subsystem, a data storage subsystem, a data analysis subsystem and a pest early warning subsystem;
the data acquisition subsystem comprises an environmental information acquisition module and an image acquisition module;
the data storage subsystem comprises a pest basic database, a crop characteristic database and an environmental information database;
the data analysis subsystem is used for processing the data acquired by the data acquisition subsystem and outputting the processed data to the pest early warning subsystem through the crop phenological period prediction model and the pest effective accumulated temperature prediction model in combination with the pest basic database and the prediction characteristics of the models;
the crop phenological period prediction model is used for inputting the obtained variety information of the crops planted in the plot and the corresponding image information collected by the image collecting equipment so as to obtain phenological period prediction result information of the crops, and meanwhile, the data analysis subsystem performs comparative analysis processing according to the prediction result information and in combination with the insect pest basic database and outputs a certain insect pest dynamic state in the period;
the insect pest effective accumulated temperature prediction model is used for processing the daily temperature data acquired by the temperature sensors in the plot and the acquired future W weather condition forecast data value of the meteorological station in the current year, and substituting the processed data into the corresponding insect pest effective accumulated temperature prediction model to obtain the specific date of a certain insect pest state in the current year and the date for forecasting the next insect state or the age of the insect in the current year;
the insect pest early warning subsystem comprises an early warning forecasting module and a prevention and control decision module; the early warning forecasting module is used for displaying the pest occurrence date calculated by the data analysis subsystem in a calendar mode through a Web end and a mobile phone end and sending the pest occurrence date to a user in a short message mode one week and the same day before the predicted occurrence; and the control decision module is used for outputting a control scheme made according to the pest basic database.
Furthermore, the environment information acquisition module comprises a temperature sensor, a humidity sensor and an illumination sensor; the image acquisition module comprises an image sensor internally provided with a GPS positioning device.
Further, the pest basic database is used for recording pest types, pest development stage data of each pest, a certain pest stage of the pests and a certain phenological stage of host crops associated with the pest stages and pest control methods; the crop characteristic database comprises a crop type image and observation position images of different phenological stages of a crop; the environmental information database comprises temperature, humidity and illumination data collected by each sensor of the environmental information collection module.
Further, the crop phenological period prediction model is obtained through deep neural network training based on a crop characteristic database.
Further, the effective temperature accumulating prediction model of the insect pests is established based on historical development stage data and associated historical daily average temperature data which are acquired from an insect pest basic database and an environment information database, wherein the historical development stage data of the insect pests comprise names of the insect pests and development starting point temperatures of the insect pests.
Further, the effective accumulated temperature expression of the insect pest effective accumulated temperature prediction model is
And
wherein T is0The starting temperature, T, for development of a certain insect stateiIs the daily average temperature, KiThe effective accumulated temperature per day, n is the number of days for completing the development of the insect status, and K is the effective accumulated temperature for completing the development of the insect status.
Further, the effective accumulated temperature prediction model of the insect pests is
Wherein N is0For a date that has stabilized past a starting temperature C for a certain insect state, N1For the predicted development date, Σ T is the accumulated temperature that has stabilized above a certain insect state starting point temperature C, T1The predicted temperature value of the air temperature in the next period is obtained.
The invention has the beneficial effects that: the invention predicts the occurrence date of each pest state of different pests by combining the characteristics of the crop phenological period prediction model and the pest effective accumulated temperature prediction model, has accurate and effective prediction, is beneficial to the development of the pre-control work of crops, and has low cost, simple operation and larger application value.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of an internet of things-based pest warning system according to an embodiment of the invention;
fig. 2 is a schematic view of a usage flow of a crop phenological period prediction model of the pest early warning system based on the internet of things according to the embodiment of the invention;
fig. 3 is a schematic view of a using process of an effective accumulated temperature prediction model of pests of the pest early warning system based on the internet of things according to the embodiment of the invention;
fig. 4 is a pest graph display diagram of red spiders in citrus according to a pest effective accumulated temperature prediction model of the pest early warning system based on the internet of things.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
As shown in fig. 1 to 3, according to the pest early warning system based on the internet of things in the embodiment of the invention, in the embodiment, a citrus red spider is selected as an example of a pest, and the pest early warning system comprises a data acquisition subsystem, a data storage subsystem, a data analysis subsystem and a pest early warning subsystem.
The data acquisition subsystem comprises an environmental information acquisition module and an image acquisition module, wherein the environmental information acquisition module comprises a temperature sensor, a humidity sensor and an illumination sensor, and the image acquisition module comprises an image sensor internally provided with a GPS positioning device.
The data storage subsystem comprises a pest basic database, a crop characteristic database and an environmental information database, wherein the pest basic database records pest species, data of each pest development stage, a pest adult stage, a certain phenological stage of a related host crop and a pest control method. The crop characteristic database comprises crop species images and observation position images of different phenological periods of crops. And the environment information database comprises temperature, humidity and illumination data collected by the sensors.
In a first embodiment, the recording pest species includes: citrus red spiders.
The data of each insect development period of the pests comprise: the insect states of each generation of red spider are eggs, young mites, former nymphs, latter nymphs and adults respectively, and the development starting temperatures of the insect states to be associated are 9.0 ℃, 10.7 ℃, 9.8 ℃, 11.0 ℃ and 12.5 ℃.
The citrus phenological period comprises: dormancy stage, flower bud differentiation stage, spring shoot germination stage, bud blooming stage, first physiological fruit drop stage, second physiological fruit drop stage, fruit expansion stage and fruit maturation stage.
The adult stage of the pest and a certain phenological stage of the associated host crop comprise: the citrus is characterized in that a certain phenological period is associated with a red spider occurrence key period, a citrus resting period-red spider overwintering oviposition period, a citrus flower bud differentiation period-red spider oviposition period, a citrus spring shoot germination period-red spider mite larval period, a citrus bud blooming period-red spider adult period, a first physiological fruit drop period-red spider mite larval period of citrus, a second physiological fruit drop period-red spider mite larval period of citrus, a citrus fruit expansion period-red spider adult period, and a citrus fruit maturation period-red spider oviposition period.
In the first embodiment, the data analysis and processing system comprises a crop phenological period prediction model and an effective accumulated temperature prediction model of insect pests, and more accurate results are output to the insect pest early warning subsystem according to the data acquired by the data acquisition subsystem through the crop phenological period prediction model and the effective accumulated temperature prediction model of insect pests and by combining the prediction characteristics of an insect pest basic database and each model.
The insect pest early warning subsystem comprises an early warning forecasting module and a prevention and control decision module, wherein the early warning forecasting module is used for displaying insect pest occurrence dates calculated by the data analysis and processing system in a calendar and chart mode through the Web end and the mobile phone end, and predicting that the insect pest occurrence dates are sent to users in a short message mode in the week and the day before the occurrence of the insect pest occurrences. And the control decision module outputs a control scheme according to the pest basic database.
The pest early warning system is used for realizing data acquisition and transmission based on mobile networks such as 2G and 4G, NBIoT, on the basis of MySQL, adopting Storm/Hadoop/Spark distributed computing framework to realize data online/offline storage and processing, integrating and developing by using open-source frameworks such as Spring Boot and Spring Cloud through B/S framework, and realizing based on MVC framework, DDD design and micro-service framework.
The crop phenological period prediction model specifically comprises the following steps:
step 1: training a deep neural network based on a crop characteristic database to obtain a crop phenological period prediction model;
step 2: acquiring variety information of the crops planted in the land;
and step 3: acquiring an image set of an observation part of the crop through the image acquisition equipment;
and 4, step 4: inputting image information of the observation part in the image set of the observation part into a crop phenological period prediction model to obtain phenological period prediction result information of the crop;
and 5: based on the prediction result obtained by the crop phenological period prediction model, the data analysis and processing system is combined with the pest basic database to compare, analyze, process and output the dynamics of a certain pest in the period.
The crop phenological period prediction model applies phenological knowledge to predict the pest emergence period, and the method is called a phenological prediction method. The growth cycle and seasonal behavior of biological organisms is the result of long-term adaptation to their living environment, with relative stability between the phenomena. The phenological method utilizes this feature in predicting the occurrence of pests. For example, a certain pest stage of a certain pest often occurs simultaneously with a certain growth stage of its host plant. Thus, the period of red spider may be estimated according to the appearance of a certain growing period of the citrus.
In the first embodiment, the insect pest effective accumulated temperature prediction model specifically comprises the following steps:
step 1: and acquiring historical development stage data of each insect state of the citrus red spiders and associated historical daily average temperature data from the insect pest basic database and the environmental information database.
In step 1, the historical development period data of each insect state comprises: the names of the insect states, namely the insect states of each generation of the red spider are eggs, young mites, former nymphs, latter nymphs and adults respectively, and the development starting temperatures of the insect states are respectively 9.0 ℃, 10.7 ℃, 9.8 ℃, 11.0 ℃ and 12.5 ℃.
Step 2: establishing an effective accumulated temperature prediction model of each insect pest state according to the historical development period data of each insect pest state and the historical daily average temperature data;
in step 2, the effective accumulated temperature expression is:
wherein, T0The starting temperature, T, for development of a certain insect stateiIs the daily average temperature, KiThe effective accumulated temperature per day, n is the number of days for completing the development of the insect status, and K is the effective accumulated temperature for completing the development of the insect status.
Establishing an effective accumulated temperature model of the insect pests according to the starting temperature C of the development of each insect state and the calculated effective accumulated temperature K required by the development of each insect state as follows:
wherein N is0For a date that has stabilized past a starting temperature C for a certain insect state, N1For the predicted development date, Σ T is the accumulated temperature that has stabilized above a certain insect state starting point temperature C, T1The predicted temperature value of the air temperature in the next period is obtained.
Step 3, acquiring a predicted annual real-time data value from an environmental information database: and processing daily temperature data acquired by temperature sensors in the plot, and substituting the daily temperature data into a corresponding effective accumulated temperature prediction model of the insect pest to obtain the specific date of a certain insect pest state in the current year.
Step 4, acquiring a recent forecast data value of the forecast annual weather station: and processing the obtained future W weather image forecast data values of the weather station in the current year, and substituting the processed data values into the corresponding effective integrated temperature forecast model of the insect pests to forecast the next insect state or the date of the occurrence of the insect pests in the current year.
In a second embodiment, the recording pest species includes: apple peach fruit borer.
The data of each insect development period of the pests comprise: the insect states of the apple peach fruit borer are egg, larva, prophase pupae, pupae and imago respectively, and the associated starting point temperatures of the insect states are 10.03 ℃, 9.4 ℃, 10.6 ℃, 9.5 ℃ and 9.2 ℃.
The apple phenological period comprises: dormancy stage, sap flowing stage, germination and flowering stage, fruit growth and development stage, autumn shoot differentiation stage, fruit expansion stage and fruit maturation stage.
Each pest stage of the pest and a certain phenological stage of the associated host crop comprise: the key period of occurrence of peach fruit borers is associated with a certain phenological period of the apple, the resting period of the apple is the pupal period of the peach fruit borers, the flowing period of the apple sap is the egg period of the peach fruit borers, the sprouting and flowering period of the apple is the larva period of the peach fruit borers, the growth and development period of the apple fruits is the adult period of the peach fruit borers, the autumn tip differentiation period of the apple is the prophase of the peach fruit borers, the expanding period of the apple fruits is the adult period of the peach fruit borers, and the mature period of the apple fruits is the adult period of the peach fruit borers.
In an embodiment, the insect pest effective accumulated temperature prediction model specifically comprises the following steps:
step 1: and acquiring historical development period data of each insect state of the apple peach fruit borer and associated historical daily average temperature data from the insect pest basic database and the environmental information database.
In step 1, the historical development period data of each insect state comprises: the names of the insect states, namely the insect states of the generations of the peach fruit borer are eggs, larvae, prophase pupae, pupae and imagoes respectively, and the starting temperatures of the development of the insect states, namely the associated starting temperatures of the development of the insect states are 10.03 ℃, 9.4 ℃, 10.6 ℃, 9.5 ℃ and 9.2 ℃ respectively.
The other steps are the same as the first embodiment to forecast the date of the next insect state or the age of the insects in the current year.
In conclusion, by means of the technical scheme, the date of occurrence of each pest state of different pests is predicted by combining the characteristics of the crop phenological period prediction model and the pest effective accumulated temperature prediction model, the prediction is accurate and effective, the crop prevention and control work in advance is facilitated, the cost is low, the operation is simple, and the application value is high.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A pest early warning system based on the Internet of things is characterized by comprising a data acquisition subsystem, a data storage subsystem, a data analysis subsystem and a pest early warning subsystem;
the data acquisition subsystem comprises an environmental information acquisition module and an image acquisition module;
the data storage subsystem comprises a pest basic database, a crop characteristic database and an environmental information database;
the data analysis subsystem is used for processing the data acquired by the data acquisition subsystem and outputting the processed data to the pest early warning subsystem through the crop phenological period prediction model and the pest effective accumulated temperature prediction model in combination with the pest basic database and the prediction characteristics of the models;
the crop phenological period prediction model is used for inputting the obtained variety information of the crops planted in the plot and the corresponding image information collected by the image collecting equipment so as to obtain phenological period prediction result information of the crops, and meanwhile, the data analysis subsystem performs comparative analysis processing according to the prediction result information and in combination with the insect pest basic database and outputs a certain insect pest dynamic state in the period;
the insect pest effective accumulated temperature prediction model is used for processing the daily temperature data acquired by the temperature sensors in the plot and the acquired future W weather condition forecast data value of the meteorological station in the current year, and substituting the processed data into the corresponding insect pest effective accumulated temperature prediction model to obtain the specific date of a certain insect pest state in the current year and the date for forecasting the next insect state or the age of the insect in the current year;
the insect pest early warning subsystem comprises an early warning forecasting module and a prevention and control decision module; the early warning forecasting module is used for displaying the pest occurrence date calculated by the data analysis subsystem in a calendar mode through a Web end and a mobile phone end and sending the pest occurrence date to a user in a short message mode one week and the same day before the predicted occurrence; and the control decision module is used for outputting a control scheme made according to the pest basic database.
2. The pest warning system based on the internet of things of claim 1, wherein the environment information acquisition module comprises a temperature sensor, a humidity sensor and an illumination sensor; the image acquisition module comprises an image sensor internally provided with a GPS positioning device.
3. The internet of things-based pest early warning system according to claim 1, wherein the pest basis database is used for recording pest types, data of various pest development stages of pests, a certain pest stage of pests and a certain phenological stage of host crops associated with the pest stages, and pest control methods; the crop characteristic database comprises a crop type image and observation position images of different phenological stages of a crop; the environmental information database comprises temperature, humidity and illumination data collected by each sensor of the environmental information collection module.
4. The internet of things-based pest early warning system according to claim 1, wherein the crop phenological period prediction model is obtained through deep neural network training based on a crop characteristic database.
5. The internet of things-based pest early warning system according to claim 1, wherein the effective accumulated temperature prediction model of the pest is established based on historical development stage data of each pest state and associated historical daily average temperature data, which are acquired from a pest basic database and an environmental information database, and the historical development stage data of each pest state comprises a name of each pest state and a temperature of a development starting point of each pest state.
6. The Internet of things-based pest early warning system according to claim 3, wherein an effective accumulated temperature expression of the pest effective accumulated temperature prediction model is
And
wherein T is0The starting temperature, T, for development of a certain insect stateiIs the daily average temperature, KiThe effective accumulated temperature per day, n is the number of days required for completing the development of the insect status, and K is the effective accumulated temperature required for completing the development of the insect status.
7. A pest early warning system based on the Internet of things according to claim 4, wherein the pest effective accumulated temperature prediction model is
Wherein N is0For a date that has stabilized past a starting temperature C for a certain insect state, N1For the predicted development date, Σ T is the accumulated temperature that has stabilized above a certain insect state starting point temperature C, T1The predicted temperature value of the air temperature in the next period is obtained.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115983533A (en) * | 2023-02-15 | 2023-04-18 | 广东省农业科学院植物保护研究所 | Method and system for identifying and evaluating potential harm of litchi fruit borers |
| CN117634744A (en) * | 2023-12-04 | 2024-03-01 | 星景科技有限公司 | Urban greening disease and pest management decision method and system |
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| CN117634744A (en) * | 2023-12-04 | 2024-03-01 | 星景科技有限公司 | Urban greening disease and pest management decision method and system |
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