CN114723118B - 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; the data acquisition subsystem comprises an environment information acquisition module and an image acquisition module; the data storage subsystem comprises a pest basic database, a crop characteristic database and an environment information database; the data analysis subsystem is used for processing the data acquired by the data acquisition subsystem through the crop weather period prediction model and the insect pest effective accumulation temperature prediction model and combining the prediction characteristics of the insect pest basic database and each model, and outputting the processed data to the insect pest early warning subsystem. According to the invention, the date of each pest state of different pests is predicted by combining the characteristics of the crop weather period prediction model and the pest effective accumulation temperature prediction model, the prediction is accurate and effective, the pre-control work of crops is facilitated, the operation is simple, and the application value is high.
Description
Technical Field
The invention relates to the technical field of the Internet of things of agriculture, in particular to a pest early warning system based on the Internet of things.
Background
The whole growth period from planting to harvesting of crops is often subjected to attack by various insect pests, if not timely controlled, not only can the yield and quality be reduced, but also serious economic losses can be caused. The traditional control method requires plant protection technicians to enter the field to observe whether crops are infected by insect pests or not, the method is time-consuming and labor-consuming, and the insect pests can be prevented or not, and when the method is found, the optimal application time is possibly missed.
At present, informatization technology has been gradually applied in agriculture. The existing crop pest early warning method in the market generally comprises the steps of photographing and returning diseases and pests through image acquisition equipment of the Internet of things, judging the occurrence condition and the severity of the crop pests through machine learning and image recognition technology, and then taking corresponding prevention and treatment measures. However, the crop pest early warning method requires expensive image acquisition equipment, has a limited single coverage area, and a lot of image acquisition equipment is often 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:
the 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 environment information acquisition module and an image acquisition module;
The data storage subsystem comprises a pest basic database, a crop characteristic database and an environment information database; the data analysis subsystem is used for processing the data acquired by the data acquisition subsystem through the crop weather period prediction model and the insect pest effective accumulation temperature prediction model and combining the prediction characteristics of the insect pest basic database and each model, and outputting the processed data to the insect pest early warning subsystem;
The crop weatherperiod prediction model is used for inputting the obtained variety information of the land-block planted crops and the corresponding image information acquired by the image acquisition equipment so as to obtain the weatherperiod 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 in the period;
The insect pest effective accumulated temperature prediction model is used for processing daily temperature data acquired by a temperature sensor in a land block and the acquired future W weather forecast data value of a weather station in the current year, and substituting the data into the corresponding insect pest effective accumulated temperature prediction model to obtain a specific date of a certain insect pest state in the current year and a date for forecasting the occurrence of the next insect state or insect age in the current year;
The pest early warning subsystem comprises an early warning and forecasting module and a control decision module; the early warning and forecasting module is used for displaying the insect 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 insect pest occurrence date to a user in a short message mode one week and on the same day before the occurrence is predicted; the control decision module is used for outputting control schemes made according to the insect pest basic database.
Further, 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 the species of the pest, the data of each pest state development period of the pest, a certain climatic period of a host crop related to the pest and a pest control method; the crop characteristic database comprises crop type images and observation part images of different crop weathers;
the environment information database comprises temperature, humidity and illumination data acquired by each sensor of the environment information acquisition module.
Further, the crop weathered period prediction model is obtained by training a deep neural network based on a crop characteristic database.
Further, the pest effective accumulated temperature prediction model is established based on historical pest state development period data and associated historical daily average temperature data which are acquired from a pest basic database and an environment information database, wherein the historical pest state development period data comprise pest state names and pest state development starting point temperatures.
Further, the effective accumulation temperature expression of the insect pest effective accumulation temperature prediction model is thatAndWherein T 0 is the starting point temperature of the development of a certain insect state, T i is the daily average air temperature, K i is the daily effective accumulated temperature, n is the number of days required for completing the development of the insect state, and K is the effective accumulated temperature required for completing the development of the insect state.
Further, the pest effective accumulation temperature prediction model is thatWhere N 0 is the date that the temperature C has stabilized past the onset of a certain worm state, N 1 is the predicted development date, Σt is the cumulative temperature that has stabilized past the onset of a certain worm state, and T 1 is the temperature prediction value of the air temperature for the next period of time.
The invention has the beneficial effects that: according to the invention, the date of each pest state of different pests is predicted by combining the characteristics of the crop weather period prediction model and the pest effective accumulation temperature prediction model, the prediction is accurate and effective, the pre-control work of crops is facilitated, the cost is low, the operation is simple, and the application value is high.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are 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 other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a pest early warning system based on the internet of things according to an embodiment of the invention;
fig. 2 is a schematic diagram of a usage flow of a crop weather forecast model of the pest early warning system based on the internet of things according to an embodiment of the invention;
fig. 3 is a schematic diagram of a pest effective accumulation temperature prediction model using flow of a pest early warning system based on the internet of things according to an embodiment of the invention;
Fig. 4 is a diagram showing an insect pest diagram of citrus red spiders of an insect pest effective accumulation temperature prediction model of an insect pest early warning system based on the internet of things according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the invention, fall within the scope of protection of the invention.
1-3, According to an embodiment of the present invention, in an embodiment of the present invention, pest selection citrus red spider is exemplified by a pest early warning system based on the internet of things, including a data acquisition subsystem, a data storage subsystem, a data analysis subsystem, and a pest early warning subsystem.
The data acquisition subsystem comprises an environment information acquisition module and an image acquisition module, wherein the environment 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 environment information database, wherein the pest basic database records the pest species, the data of each pest state development period of pests, the adult period of the pests, a certain weather period of an associated host crop and a pest control method. The crop characteristic database comprises crop type images and observation part images of different crop weathers. The environment information database comprises temperature, humidity and illumination data acquired by the sensor.
In a first embodiment, the recording the pest species includes: citrus red spider.
The data of each pest status development period of the pests comprises: the red spider is in the form of eggs, young mites, front spider mites, rear spider mites and adults, and the development starting point temperature of the desired spider is 9.0 ℃, 10.7 ℃, 9.8 ℃, 11.0 ℃ and 12.5 ℃.
The citrus weathering period includes: dormancy stage, flower bud differentiation stage, spring tip germination stage, bud emergence stage, first physiological fruit drop stage, second physiological fruit drop stage, fruit expansion stage, and fruit maturity stage.
The adult stage of the pest and a certain waiting period of the associated host crop include: the method comprises the following steps of (1) a critical occurrence period of red spiders associated with a certain physical period of citrus, namely a citrus dormancy period, a red spiders wintering egg period, a citrus flower bud differentiation period, a red spiders egg period, a citrus spring tip germination period, a red spiders young mite period, a citrus bud bloom period, a red spiders adult full period, a citrus first physiological fruit dropping period, a red spiders young mite period, a citrus second physiological fruit dropping period, a red spiders if mite period, a citrus fruit swelling period, a red spiders full period and a citrus fruit maturation period.
In a first embodiment, the data analysis processing system includes a crop weather period prediction model and a pest effective accumulation temperature prediction model, and the data collected by the data collection subsystem is output to the pest early warning subsystem more accurately by combining the prediction characteristics of the pest basic database and each model through the crop weather period prediction model and the pest effective accumulation temperature prediction model.
The pest early warning subsystem comprises an early warning and forecasting module and a control decision module, wherein the early warning and forecasting module is used for displaying pest occurrence dates calculated by the data analysis and processing system in a calendar and chart mode through a Web end and a mobile phone end, and predicting that the pest occurrence dates are sent to a user in a short message mode in the last week and the last day. And the control decision module outputs a control scheme according to the insect pest basic database.
The pest early warning system is based on mobile networks such as 2G, 4G, NBIoT and the like to realize data acquisition and transmission, based on MySQL, adopts a Storm/Hadoop/Spark distributed computing framework to realize online/offline data storage and processing, adopts Spring Boot, spring Cloud and other open source frameworks to integrate and develop through a B/S framework, and is based on an MVC framework, a DDD design and a micro-service framework.
The crop weather period prediction model specifically comprises the following steps:
Step 1: training the deep neural network based on the crop characteristic database to obtain a crop weather period prediction model;
Step 2: obtaining variety information of land block planted crops;
Step 3: acquiring an image set of an observation part of the crop through the image acquisition equipment;
Step 4: inputting the image information of the observation part in the image set of the observation part into a crop weather period prediction model to obtain weather period prediction result information of the crop;
Step 5: based on the prediction result obtained by the crop weathered period prediction model, the data analysis processing system combines the insect pest basic database to compare, analyze and process and output a certain insect pest dynamic at the present period.
The crop weathered period prediction model uses the knowledge of the weathers to predict the occurrence period of pests, and the method is called a weathered prediction method. The growth cycle and season of biological organisms are the result of long-term adaptation to their living environment, with relative stability between the phenomena. The physical method is to predict the occurrence of pests by utilizing the characteristic. For example, a certain period of a pest often occurs at the same time as a certain growth phase of its host plant. Thus, the possible occurrence period of the red spider can be estimated according to the occurrence period of the citrus fruits.
In a first embodiment, the pest effective accumulation temperature prediction model specifically includes the following steps:
Step 1: and acquiring historical data of each insect state development period of the citrus red spider and associated historical daily average temperature data from the insect pest basic database and the environment information database.
In step 1, the historical individual worm state development period data includes: the insect states of each generation of red spider are eggs, young mites, front spider mites, rear spider mites and adults respectively, and the development starting point temperature of each insect state is 9.0 ℃, 10.7 ℃, 9.8 ℃, 11.0 ℃ and 12.5 ℃ respectively.
Step 2: establishing an effective accumulated temperature prediction model of each insect pest according to the historical development period data and the historical daily average temperature data of each insect pest;
In step2, the effective heat accumulation expression is:
Wherein T 0 is the starting point temperature of the development of a certain insect state, T i is the daily average air temperature, K i is the daily effective accumulated temperature, n is the number of days required for completing the development of the insect state, and K is the effective accumulated temperature required for completing the development of the insect state.
According to the temperature C of each insect development starting point and the calculated effective accumulated temperature K required by each insect development, establishing an insect pest effective accumulated temperature model as follows:
where N 0 is the date that the temperature C has stabilized past the onset of a certain worm state, N 1 is the predicted development date, Σt is the cumulative temperature that has stabilized past the onset of a certain worm state, and T 1 is the temperature prediction value of the air temperature for the next period of time.
Step 3, obtaining a predicted year real-time data value from an environment information database: and substituting the daily temperature data acquired by the temperature sensor in the land block into a corresponding effective pest accumulation temperature prediction model after processing to obtain a specific date of a certain pest state of a certain pest in the current year.
Step 4, obtaining a recent forecast data value of a weather station in a forecast year: and substituting the obtained future W weather forecast data value of the weather station in the current year into a corresponding effective pest accumulation temperature prediction model to forecast the occurrence date of the next insect state or insect age.
In a second embodiment, the recording the pest species includes: the apple peach fruit borer.
The data of each pest status development period of the pests comprises: the generation of the apple peach fruit borer is eggs, larvae, early pupae, pupae and adults, and the development starting point temperature of the related generation of the apple peach fruit borer is 10.03 ℃, 9.4 ℃, 10.6 ℃, 9.5 ℃ and 9.2 ℃.
Apple phenology includes: dormancy stage, sap flowing stage, bud bloom stage, fruit growth and development stage, autumn tip differentiation stage, fruit expansion stage and fruit maturity stage. Each pest period of the pest and a certain waiting period of the associated host crop include: the key period of occurrence of the peach fruit borer is related to a certain physical period of apples, namely, the dormancy period of apples, namely, the pupa period of the peach fruit borer, the flowing period of apple sap, namely, the ovum period of the peach fruit borer, namely, the sprouting and blooming period of apples, namely, the larva period of the peach fruit borer, namely, the adult period of the peach fruit borer, namely, the autumn tip differentiation period of apples, namely, the pupa period of the peach fruit borer, namely, the adult period of the apple fruit ripening period of the peach fruit borer.
In the second embodiment, the specific use of the pest effective accumulation temperature prediction model includes the following steps:
Step 1: and acquiring historical data of each insect state development period of the apple peach heart worm and associated historical daily average temperature data from the insect pest basic database and the environment information database.
In step 1, the historical individual worm state development period data includes: the insect state names of the peach fruit borers are eggs, larvae, early pupae, pupae and adults respectively, and the development starting point temperatures of the insect states are 10.03 ℃, 9.4 ℃, 10.6 ℃, 9.5 ℃ and 9.2 ℃ respectively.
The rest of the steps are the same as in the first embodiment to predict the date of occurrence of the next insect state or age.
In summary, by means of the technical scheme, the date of occurrence of each insect state of different insect pests is predicted by combining the characteristics of the crop weather period prediction model and the insect pest effective accumulation temperature prediction model, the prediction is accurate and effective, the pre-control work of crops is facilitated, the cost is low, the operation is simple, and the application value is high.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (6)
1. The 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 environment information acquisition module and an image acquisition module;
The data storage subsystem comprises a pest basic database, a crop characteristic database and an environment information database;
the effective accumulated temperature expression of the insect pest effective accumulated temperature prediction model is AndWherein T 0 is the starting point temperature of the development of a certain insect state, T i is the daily average air temperature, K i is the daily effective accumulated temperature, n is the number of days required for completing the development of the insect state, and K is the effective accumulated temperature required for completing the development of the insect state;
The data analysis subsystem is used for processing the data acquired by the data acquisition subsystem through a crop weather period prediction model and the insect pest effective accumulation temperature prediction model and combining the prediction characteristics of an insect pest basic database and each model, and outputting the processed data to the insect pest early warning subsystem;
The crop weatherperiod prediction model is used for inputting the obtained variety information of the land-block planted crops and the corresponding image information acquired by the image acquisition equipment so as to obtain the weatherperiod prediction result information of the crops, and meanwhile, the data analysis subsystem performs contrast analysis processing according to the prediction result information and in combination with the insect pest basic database and outputs a certain insect pest dynamic in the period;
The insect pest effective accumulated temperature prediction model is used for processing daily temperature data acquired by a temperature sensor in a land block and the acquired future W weather forecast data value of a weather station in the current year, and substituting the data into the corresponding insect pest effective accumulated temperature prediction model to obtain a specific date of a certain insect pest state in the current year and a date for forecasting the occurrence of the next insect state or insect age in the current year;
The pest early warning subsystem comprises an early warning and forecasting module and a control decision module; the early warning and forecasting module is used for displaying the insect 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 insect pest occurrence date to a user in a short message mode one week and on the same day before the occurrence is predicted; the control decision module is used for outputting control schemes made according to the insect pest basic database.
2. The pest early warning system based on the internet of things of claim 1, wherein the environmental 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 of claim 1, wherein the pest base database is used for recording pest species, data of each pest status development period, a certain period of a pest, a certain climatic period of a host crop associated therewith, and a pest control method; the crop characteristic database comprises crop type images and observation part images of different crop weathers; the environment information database comprises temperature, humidity and illumination data acquired by each sensor of the environment information acquisition module.
4. The pest warning system based on the internet of things of claim 1, wherein the crop weathers prediction model is obtained by training a deep neural network based on a crop characteristics database.
5. The pest early warning system based on the internet of things of claim 1, wherein the pest effective heat accumulation prediction model is established based on historical individual pest state development period data acquired from a pest base database and an environment information database, the associated historical daily average temperature data, the historical individual pest state development period data including individual pest state names and individual pest state development start temperatures.
6. The pest early warning system based on the internet of things of claim 4, wherein the pest effective accumulation temperature prediction model isWhere N 0 is the date that the temperature C has stabilized past the onset of a certain worm state, N 1 is the predicted development date, Σt is the cumulative temperature that has stabilized past the onset of a certain worm state, and T 1 is the temperature prediction value of the air temperature for the next period of time.
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| CN101052147A (en) * | 2007-05-24 | 2007-10-10 | 中国科学院合肥物质科学研究院 | Intelligent early warning system for field crop pest and disease disasters |
| CN103616482A (en) * | 2013-12-04 | 2014-03-05 | 吉林省农业科学院 | Automatic injurious insect emergence period early-warning device |
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| KR102658048B1 (en) * | 2017-01-17 | 2024-04-18 | 한국전자통신연구원 | Method and system for predicting damage from disease and harmful insects related to meteorology based on ICT |
| CN108764542A (en) * | 2018-05-16 | 2018-11-06 | 贾翔 | The remote sensing prediction method of phase and occurrence quantity occur for diversiform-leaved poplar spring looper |
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Patent Citations (2)
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|---|---|---|---|---|
| CN101052147A (en) * | 2007-05-24 | 2007-10-10 | 中国科学院合肥物质科学研究院 | Intelligent early warning system for field crop pest and disease disasters |
| CN103616482A (en) * | 2013-12-04 | 2014-03-05 | 吉林省农业科学院 | Automatic injurious insect emergence period early-warning device |
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