CN110619062A

CN110619062A - Intelligent agricultural production data monitoring and early warning control system and method

Info

Publication number: CN110619062A
Application number: CN201910882822.XA
Authority: CN
Inventors: 宋丽娟; 杜涛利; 马海容; 王迪; 金冰鑫
Original assignee: Ningxia University
Current assignee: Ningxia University
Priority date: 2019-09-18
Filing date: 2019-09-18
Publication date: 2019-12-27
Also published as: CN114491147A

Abstract

The invention belongs to the technical field of information and data processing, and discloses an intelligent agricultural production data monitoring and early warning control system and method, wherein the intelligent agricultural production data monitoring and early warning control system comprises: the data acquisition module is used for completing the acquisition and uploading of environmental data and video information; the new wave cloud application module is used for finishing the storage of data; the WeChat public number module is used for realizing equipment control and data query by binding the WeChat public number; the wolfberry leaf spot detection platform is used for detecting the existence of wolfberry leaf spots. The function of detecting the lesion of the Chinese wolfberry leaves can improve the accuracy of detecting the lesion of the Chinese wolfberry leaves by a user, and has certain early warning performance. Through the algorithm training of deep learning, the detection accuracy of the diseased spots and the non-diseased spots in the images of the Chinese wolfberry leaves is obviously improved.

Description

Intelligent agricultural production data monitoring and early warning control system and method

Technical Field

The invention belongs to the technical field of information and data processing, and particularly relates to an intelligent agricultural production data monitoring and early warning control system and method.

Background

Currently, the closest prior art: the agricultural networking can help the agriculture to solve the complex problem control technology that the traditional planting mode can not solve. In the future, planting, irrigation, fertilization, prevention and control and harvest are intelligently completed through the agricultural Internet of things, quality planting and fine cultivation are greatly improved, and 'smart agriculture and life change' are achieved in a real sense. At present, a mature and stable agricultural Internet of things pest monitoring and early warning system covers 20 provinces of the whole country, and the Internet of things is composed of an Internet of things technology, an identification model, a database and information processing equipment, so that real-time monitoring and effective control of agricultural pests are realized.

The agricultural Internet of things pest monitoring and early warning system collects environmental factors, pest dynamic high-definition images, soil information and crop growth vigor in all weather and in real time. And transmitting and automatically uploading daily acquired information data through a wireless network to form a system database. Environmental factors, current and historical data of plant diseases and insect pests can be freely retrieved by a user. Through system big data summarization, intelligent calculation, analysis grasp field environment, sick worm trend. The disease and insect prediction model automatically generates disease and insect species, quantity and occurrence time curve graphs, and a user can conveniently and visually analyze the type and the damage degree of the disease and insect pests in the area. The disease and pest prediction model conducts timely and appropriate temperature and humidity field regulation through the intelligent drip irrigation system remotely commanded by the system, creates environmental conditions which are not beneficial to growth, development, reproduction and harm of diseases and pests, and achieves the purposes of inhibiting the generation and harm of the diseases and pests and ensuring high yield and harvest of crops by changing ecological conditions.

However, most of the existing agricultural internet-of-things monitoring and early warning systems are directed at main economic crops in China, and no production data monitoring and early warning system special for medlar is provided. Because the medlar is used as a main economic crop of Ningxia, but the medlar cannot be popularized and planted in China, and Ningxia is used as inland province of China, the intelligent agricultural receiving level is low, the intelligent agricultural selling price is high, common farmers have certain difficulty in paying, and the farmers need a certain time period for receiving the intelligent agricultural. The establishment of the medlar disease picture database requires a large amount of preliminary investigation and collection in a planting area, and a large amount of energy and material resources are consumed. A large amount of research and collection of wolfberry leaf scab images are carried out in a wolfberry garden in the earlier stage of a system development team, the preliminary establishment of a wolfberry disease image database is completed, and the used sensor is low in cost and easy to accept by local farmers.

The difficulty of solving the technical problems is as follows:

the intelligent development of agriculture in China is late, and the intelligent coverage rate of agriculture is low. Agricultural intelligent products on the current market are high in selling price, general farmers cannot pay installation and use cost, common culture levels of local farmers are low, some agricultural intelligent products which are more complicated to use cannot be used by the local farmers proficiently, the early-stage medlar disease image database is not built to cover a Ningxia whole-area medlar garden, a large amount of data acquisition is needed, and the expansion of a medlar disease image database is completed.

The significance of solving the technical problems is as follows:

the rapid development of new generation information technologies such as the internet and the like promotes the integration of the information technologies and the agricultural development, the agricultural modernization pace is quickened, a new cooperation mode is formed with informatization, and the progress of the agricultural informatization is led to the leap development. The intelligent agricultural production data monitoring and early warning control system has the characteristics of flexibility, low cost, high ability of being suitable for complex farmland environments and dealing with sudden disasters and the like, the system fully utilizes the novel information technology to change the traditional agriculture, carries out digital design, intelligent management and accurate monitoring on an agricultural production mode, can accelerate the construction pace of the intelligent agriculture and promotes the integration of the agricultural modernization and the intelligent agriculture.

At present, the fusion of the internet of things technology and traditional agriculture is greatly popularized and applied across the country, and the effect is obvious. The medlar serving as one of the five treasures of Ningxia has the advantages of tonifying kidney, enriching blood, resisting fatigue and the like, and the Ningxia medlar has the unique advantages and is promoted to markets nationwide or even worldwide, so the medlar industry is one of the strategic leading industries with the most characteristics and advantages of Ningxia. In recent years, various diseases are easy to occur in the growth and development process of the medlar, the quality of the medlar is affected, and common diseases mainly comprise powdery mildew, gray leaf spot, gall mite, anthracnose and the like. At present, the disease diagnosis of the medlar is carried out by manually observing disease symptoms of medlar trees and comparing with the existing disease symptom pictures and text explanations by means of experience of plant protection experts and growers in the past year. Because the planting area of matrimony vine is wide, artifical real-time supervision is wasted time and energy, and probably has the condition of overlooking. Therefore, the enhancement of the disease control of the medlar in the growth process of the medlar is not slow at all. The intelligent monitoring system for the medlar garden solves the problems that a large amount of medlar is reduced in yield and manual real-time monitoring wastes time and labor due to various diseases in the growth and development process of medlar, and enables a grower to realize intelligent management of the medlar garden.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides an intelligent agricultural production data monitoring and early warning control system and method.

The invention is realized in this way, an intelligent agricultural production data monitoring and early warning control system, the intelligent agricultural production data monitoring and early warning control system includes:

the data acquisition module is used for completing the acquisition and uploading of environmental data and video information;

the new wave cloud application module is used for finishing the storage of data;

the WeChat public number module is used for realizing equipment control and data query by binding the WeChat public number;

the wolfberry leaf spot detection platform is used for detecting the existence of wolfberry leaf spots.

Further, the data acquisition module comprises an environment temperature and humidity acquisition module, a soil humidity acquisition module, an illumination intensity acquisition module and a video information acquisition module;

the system comprises an environmental temperature and humidity acquisition module, a DHT11 temperature and humidity sensor, an ESP8266WiFi module, a small fan and an LCD1602 character type liquid crystal screen are mounted on an STC12C5A60S2 development board, temperature data on the DHT11 are displayed on the LCD1602, the temperature and humidity data are sent to Xinlangyun application through the ESP8266, the small fan is triggered to rotate when the temperature is higher than or equal to 28 ℃, and the small fan stops rotating when the temperature is lower than 28 ℃;

the soil humidity acquisition module is provided with a soil humidity sensor, a relay, a small water pump and an LCD1602 liquid crystal screen on an STC12C5A60S2 development board, soil humidity data are displayed on the LCD1602 liquid crystal screen, and when the soil humidity is lower than 30%, the relay is attracted to trigger the small water pump to pump water;

the illumination intensity acquisition module is used for carrying a GY-30 illumination intensity sensor, a stepping motor and an LCD1602 liquid crystal display on an STC12C5A60S2 development board, and triggering the stepping motor to rotate forwards when the illumination intensity is greater than or equal to 9000 px; when the current is less than 9000px, the stepping motor rotates reversely;

the camera module and the environmental data acquisition module are integrated to form a data acquisition module. STM32F429 development board drive OV5640 camera module sends video information to computer video receiving window through the serial ports in real time.

Further, the Sina cloud application module comprises functions of MySQL database table storage, real-time data display and historical data query;

environmental temperature and humidity data are uploaded to a new romantic cloud application MySQL database table through WiFi on the data acquisition module, the table comprises ID, user binding time, equipment state, data acquisition time, various environmental data values and user binding ID3, and the data are updated and stored once every 3 seconds.

Another object of the present invention is to provide an intelligent agricultural production data monitoring and early warning control method for operating the intelligent agricultural production data monitoring and early warning control system, the intelligent agricultural production data monitoring and early warning control method comprising the steps of:

the method comprises the following steps of firstly, collecting and uploading environmental data and video information, and storing the data;

and secondly, the user realizes equipment control and data query through binding of the WeChat public number, and the Chinese wolfberry leaf scab detection platform completes the detection of the existence of the Chinese wolfberry leaf scab.

Further, the first step of collecting and uploading the environmental data and the video information specifically includes:

(1) the method comprises the steps that an STC12C5A60S2 development board is used for respectively carrying a DHT11 temperature and humidity sensor, a soil humidity sensor and a GY-30 illumination intensity sensor to complete collection of environmental temperature and humidity, soil humidity and illumination intensity data, and the data are transmitted to a new wave cloud application MySQL database table through a WiFi module; the comprehensive management of the surrounding environment of the medlar land and the growth condition of the medlar is realized through a monitoring camera arranged in a medlar garden; the growth condition of each leaf is shot through zooming and focusing, the leaf picture is transmitted to an application layer, and a Chinese wolfberry leaf scab detection system detects scabs to obtain the health condition of Chinese wolfberry and feeds the health condition back to a grower in real time; the medlar grower can know the growth condition of the medlar without being in the field, and the image acquisition enables the grower to check the medlar leaves and judge whether the medlar leaves have disease spots or not;

(2) the WiFi module reliably and safely transmits the sensor data to a MySQL database table applied to new wave cloud, and a Socket communication technology under a TCP/IP protocol is used;

(3) the data information table stores environmental data such as temperature, humidity, soil humidity, illumination intensity and the like collected by the sensor of the sensing layer, and a user inquires historical data by using time according to different numbers to process and analyze the data.

Further, the Xinlang cloud application server is provided with a designated port for monitoring and waiting for a connection request from the WiFi terminal; monitoring the request from the new wave cloud application end, and responding to the subsequent request in time; when the request arrives, the WiFi terminal sends data to the Xinlang cloud application terminal by using Socket writing operation, and the Xinlang cloud application terminal receives the data sent by the WiFi terminal by using Socket reading operation; setting a WiFi working mode to be a Station mode by using an ESP8266 debugging tool, setting a hotspot name and a password required to be connected by the WiFi module, and acquiring a Xinlang cloud application IP address as a destination address transmitted by the WiFi module; and entering a transparent transmission mode to transmit the data after packaging.

Further, the second step of user realizes equipment control and data query through binding of WeChat public numbers, and the detection that the sick spot of matrimony vine leaf has or not accomplished the sick spot of matrimony vine leaf by matrimony vine leaf testing platform includes:

(1) the WeChat public number comprises an equipment control menu, a data display menu and a user center main menu, wherein an equipment opening button, an equipment closing button and an equipment state viewing button are arranged below the equipment control menu, buttons of environment temperature and humidity, soil humidity, illumination intensity and disease type are arranged below the data display menu, and a user binding button is arranged below the user center menu;

(2) the medlar leaf spot monitoring platform realizes that the grower checks the leaf condition, and reminds the grower when a leaf spot exists; and (4) the user opens the system to select one Chinese wolfberry diseased leaf photo to finish detection.

The invention also aims to provide a computer program for realizing the intelligent agricultural production data monitoring and early warning control method.

The invention also aims to provide an information data processing terminal for realizing the intelligent agricultural production data monitoring and early warning control method.

Another objective of the present invention is to provide a computer-readable storage medium, which includes instructions that, when executed on a computer, cause the computer to execute the method for monitoring and controlling the intelligent agricultural production data.

In summary, the advantages and positive effects of the invention are: the invention realizes the functions of environment on-line monitoring, intelligent control, video monitoring and image acquisition and medlar leaf scab detection. Can show data developments, the environment humiture, soil moisture, illumination intensity data of looking over that can be clear like this can be more audio-visual carries out analysis and processing to the growing environment of matrimony vine. The intelligent control can check the state of the data collection module, so that remote operation and management are realized, and the management of the medlar garden is more convenient. And corresponding control equipment can be triggered by setting a threshold value, a simulation early warning effect is achieved, the obtained environmental data is always stored in the cloud, and the method has great significance for analyzing and researching the growth and yield increase of the medlar. The system is added with video monitoring and image acquisition functions, the camera module and the data acquisition module are integrated, the real-time image is transmitted to a computer through a serial port, and a user selects certain wolfberry leaf photos with good effects to detect the lesion spots of the wolfberry leaves. The video monitoring and image acquisition functions ensure the safety of the medlar garden on one hand, and on the other hand, the comprehensive understanding of the growth environment of the medlar can be realized without going out. The wolfberry leaf spot detection function can improve the accuracy of the detection of the wolfberry leaf spot by a user, and has certain early warning performance. Through the algorithm training of deep learning, the detection accuracy of the diseased spots and the non-diseased spots in the images of the Chinese wolfberry leaves is obviously improved.

Drawings

FIG. 1 is a schematic structural diagram of an intelligent agricultural production data monitoring and early warning control system provided by an embodiment of the invention;

FIG. 2 is a schematic structural diagram of a data acquisition module according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of a new wave cloud application module provided in an embodiment of the present invention;

in the figure: 1. a data acquisition module; 2. a new wave cloud application module; 3. a WeChat public number module; 4. medlar leaf spot detection platform.

Fig. 4 is a flowchart of an intelligent agricultural production data monitoring and early warning control method provided by the embodiment of the invention.

FIG. 5 is a schematic diagram of a test result of a scab leaf under the Incepision V3 model according to an embodiment of the present invention;

in the figure: (a) training the test result of the model under the parameters of batch 16 and epoch 10; (b) training the test result of the model under the parameters of batch 8 and epoch 10; (c) training the test result of the model under the parameters of batch 16 and epoch 30; (d) the test results of the model were trained for batch 8 and epoch 30 parameters.

FIG. 6 is a schematic diagram of a test result of a healthy blade under an inclusion V3 model according to an embodiment of the present invention;

FIG. 7 is a schematic diagram illustrating the test results of a diseased leaf blade under the DenseNet model according to an embodiment of the present invention;

in the figure: (a) training the test result of the model under the parameters of batch 16 and epoch 10; (b) training the test result of the model under the parameters of batch 16 and epoch 30; (c) training the test result of the model under the parameters of batch 8 and epoch 10; (d) the test results of the model were trained for batch 8 and epoch 30 parameters.

Fig. 8 is a schematic diagram of a test result of a scab leaf under the DenseNet model according to an embodiment of the present invention.

Fig. 9 is a schematic diagram of environment data stored in a MySQL database table according to an embodiment of the present invention.

Fig. 10 is a schematic view of an interface of a wolfberry leaf lesion detection platform according to an embodiment of the present invention.

Fig. 11 is a schematic view of a result interface of a wolfberry leaf spot detection platform according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Aiming at the problems in the prior art, the invention provides an intelligent agricultural production data monitoring and early warning control system and method, and the invention is described in detail below with reference to the accompanying drawings.

As shown in fig. 1, the intelligent agricultural production data monitoring and early warning control system provided by the embodiment of the invention comprises: the system comprises a data acquisition module 1, a Xinlangyun application module 2, a WeChat public number module 3 and a Chinese wolfberry leaf scab detection platform 4.

And the data acquisition module 1 is used for completing the acquisition and uploading of environmental data and video information.

And the Xinlang cloud application module 2 is used for finishing the storage of the data.

And the WeChat public number module 3 is used for realizing equipment control and data query by binding the WeChat public number.

And the Chinese wolfberry leaf spot detection platform 4 is used for detecting whether the Chinese wolfberry leaf spot exists or not.

The intelligent agricultural production data monitoring and early warning control method provided by the embodiment of the invention comprises the following steps:

the method comprises the steps of using an STC12C5A60S2 development board to respectively carry a DHT11 temperature and humidity sensor, a soil humidity sensor and a GY-30 illumination intensity sensor to complete collection of environmental temperature and humidity, soil humidity and illumination intensity data, and transmitting the data to a new wave cloud application MySQL database table through a WiFi module. The comprehensive management of the surrounding environment of the medlar field and the growth condition of the medlar can be realized through the monitoring camera arranged in the medlar garden. The growth condition of each leaf can be shot by means of zooming, focusing and the like, the picture of the leaf is transmitted to the application layer, the Chinese wolfberry leaf scab detection system detects the scab, the health condition of the Chinese wolfberry is obtained, and the health condition is fed back to a grower in real time. The video ensures the safety of equipment on one hand, and enables a medlar grower to know the growth condition of the medlar without visiting the scene on the other hand, thereby improving the working efficiency; image acquisition makes the grower need not go to the matrimony vine garden in person and look over the matrimony vine blade and judge whether the matrimony vine blade has the lesion spot with this, has saved manpower and material resources.

The WiFi module reliably and safely transmits the sensor data to a MySQL database table applied to the new wave cloud, and a Socket communication technology under a TCP/IP protocol is used. The specific communication process is as follows: the new wave cloud application server has a designated port for monitoring and waiting for a connection request from the WiFi terminal, and can monitor the request from the new wave cloud application terminal and respond to the subsequent request in time. Once the request arrives, they "handshake" is successful (establish communication connection). The WiFi terminal sends data to the Xinlang cloud application terminal by using Socket writing operation, and the Xinlang cloud application terminal receives the data sent by the WiFi terminal by using Socket reading operation. The method comprises the steps of setting a WiFi working mode to be a Station mode by using an ESP8266 debugging tool, setting hot spot names and passwords required to be connected by a WiFi module, obtaining a Xinlang cloud application IP address as a destination address for transmission of the WiFi module, entering a transparent transmission mode, packaging data and transmitting, wherein the maximum 2048 bytes of each packet is obtained. The WiFi module is used mainly because the transmission rate is high, and the installation is convenient.

The data information table is mainly used for storing environmental data such as temperature, humidity, soil humidity, illumination intensity and the like collected by the sensor of the sensing layer, and a user can query historical data by using time according to different numbers, so that the data can be conveniently processed and analyzed.

The WeChat is used as the largest social communication software in China and has the characteristics of convenience and easiness in use, so that the WeChat public number platform is suitable for a wide variety of Chinese wolfberry growers, and the Chinese wolfberry growers can use the functions in the WeChat public number only by paying attention to the WeChat public number and then binding the WeChat public number by one key. The WeChat public number mainly comprises three main menus of equipment control, data display and a user center, wherein equipment is opened and closed under the equipment control menu, an equipment state viewing button is arranged under the equipment control menu, buttons of environment temperature and humidity, soil humidity, illumination intensity and disease types are arranged under the data display menu, and a user binding button is arranged under the user center menu. The user can realize corresponding functions only by clicking the button, and the operation is convenient.

The medlar leaf spot monitoring platform can enable a grower not to look up the situation of the leaves in a medlar garden by himself, when the leaf spot exists, the grower can be timely reminded, and the condition that the yield of medlar is influenced because the grower misses the optimal pesticide application time is avoided. The user opens the system, selects one Chinese wolfberry diseased leaf photo and clicks the start detection button to finish detection. And the medlar leaf lesion spot detection platform uses Python language to compare the accuracy of an inclusion V3 model under a TensorFlow framework with an inclusion V3 and a DenseNet121 model under a Keras framework.

As shown in fig. 2, the data acquisition module 1 is mainly responsible for acquiring and uploading environmental data and video information of the lycium barbarum garden in real time, and is used as a source of data of the whole system, which plays a crucial role in implementing the system. The system mainly comprises an environment temperature and humidity acquisition module, a soil humidity acquisition module, an illumination intensity acquisition module and a video information acquisition module.

The environmental temperature and humidity acquisition module carries a DHT11 temperature and humidity sensor, an ESP8266WiFi module, a small fan and an LCD1602 character type liquid crystal screen on an STC12C5A60S2 development board, temperature data on the DHT11 are displayed on the LCD1602, the temperature and humidity data are sent to Xinlangyun application through the ESP8266, the small fan is triggered to rotate when the temperature is higher than or equal to 28 ℃, and the small fan stops rotating when the temperature is lower than 28 ℃; the soil humidity acquisition module is provided with a soil humidity sensor, a relay, a small water pump and an LCD1602 liquid crystal screen on an STC12C5A60S2 development board, soil humidity data are displayed on the LCD1602 liquid crystal screen, and when the soil humidity is lower than 30%, the relay is attracted to trigger the small water pump to pump water; the light intensity acquisition module is used for carrying a GY-30 light intensity sensor, a stepping motor and an LCD1602 liquid crystal display on an STC12C5A60S2 development board, and when the light intensity is greater than or equal to 9000px, the stepping motor is triggered to rotate forwards; when the current is less than 9000px, the stepping motor rotates reversely; and integrating the camera module and the environmental data acquisition module to form a data acquisition module. STM32F429 development board drive OV5640 camera module sends video information to computer video receiving window through the serial ports in real time. And opening video receiving software on the computer, and storing the image by selecting and storing the image.

As shown in fig. 3, the new wave cloud application module 3 receives the environmental data uploaded by the data acquisition module and is connected to the wechat public number module. The module comprises MySQL database table storage, real-time data display and historical data query functions.

And uploading the environmental temperature and humidity data to a MySQL database table of the new cloud application through WiFi on the data acquisition module, wherein the table comprises ID, user binding time, equipment state, data acquisition time, various environmental data values and user binding ID. And the data is stored once every 3 seconds.

Database table experimental data are as follows.

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CREATE TABLE IF NOT EXISTS`device`(

`ID`int(10)NOTNULLAUTO_INCREMENT，

`time`datetime DEFAULTNULL，

`state`int(1)DEFAULTNULL，

`time2`datetime DEFAULTNULL，

`temp`int(2)DEFAULTNULL，

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INSERT INTO`device`(`ID`，`time`，`state`，`time2`，`temp`，`humi`，`tr`，`gm`，`openid`)VALUES

(1，'2019-04-0214:28:49'，1，'2019-04-0214:28:52'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(2，'2019-04-0214:28:49'，1，'2019-04-0214:28:55'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(3，'2019-04-0214:28:49'，1，'2019-04-0214:28:58'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(4，'2019-04-0214:28:49'，1，'2019-04-0214:29:01'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(5，'2019-04-0214:28:49'，1，'2019-04-0214:29:04'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(6，'2019-04-0214:28:49'，1，'2019-04-0214:29:07'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(7，'2019-04-0214:28:49'，1，'2019-04-0214:29:10'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(8，'2019-04-0214:28:49'，1，'2019-04-0214:29:13'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(9，'2019-04-0214:28:49'，1，'2019-04-0214:29:16'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(10，'2019-04-0214:28:49'，1，'2019-04-0214:29:19'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(11，'2019-04-0214:28:49'，1，'2019-04-0214:29:22'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(12，'2019-04-0214:28:49'，1，'2019-04-0214:29:25'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(13，'2019-04-0214:28:49'，1，'2019-04-0214:29:28'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(14，'2019-04-0214:28:49'，1，'2019-04-0214:29:31'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(15，'2019-04-0214:28:49'，1，'2019-04-0214:29:34'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(16，'2019-04-0214:28:49'，1，'2019-04-0214:29:37'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(17，'2019-04-0214:28:49'，1，'2019-04-0214:29:40'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(18，'2019-04-0214:28:49'，1，'2019-04-0214:29:43'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(19，'2019-04-0214:28:49'，1，'2019-04-0214:29:46'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(20，'2019-04-0214:28:49'，1，'2019-04-0214:29:49'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(21，'2019-04-0214:28:49'，1，'2019-04-0214:29:52'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(22，'2019-04-0214:28:49'，1，'2019-04-0214:29:55'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(23，'2019-04-0214:28:49'，1，'2019-04-0214:29:58'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(24，'2019-04-0214:28:49'，1，'2019-04-0214:30:01'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')，

(25，'2019-04-0214:28:49'，1，'2019-04-0214:30:04'，23，19，35，7400，'oeof950gZ7gEWSQarCloRk75gaZ0')；

And displaying a piece of latest data of the data acquisition module on a data display interface, and clicking to search after selecting time to obtain all data in a selected time period.

The user can obtain the unique ID after paying attention to the binding account of the WeChat public number, and the user can check the state of the data acquisition module and carry out the latest environmental data through the WeChat public number.

And clicking an equipment opening (closing) button in the WeChat public number interface to open (close) the data collection module, and checking the equipment state by using an equipment state button. And clicking a corresponding environment data viewing button to obtain the latest environment data, wherein the disease type button has the function of introducing main diseases and insect pests of the Chinese wolfberry. And clicking a binding account button, and obtaining a unique ID after the user binds to know hundreds of links of more buttons.

The wolfberry leaf spot detection platform is a training model of a training set of a Chinese wolfberry leaf spot detection platform by using transfer learning under a Keras framework and a TensorFlow framework on the basis of learning a CNN structure. The platform is built by using a Python language with a Frame.

The user selects the medlar leaf photo, the medlar leaf photo is displayed on the platform, after the detection is finished, the results of the three models can be visually displayed in the corresponding areas, and the platform comprehensively analyzes the state of the leaves as health or disease spots according to the results.

As shown in fig. 4, the intelligent agricultural production data monitoring and early warning control method provided by the embodiment of the invention comprises the following steps:

s401: collecting and uploading environmental data and video information, and storing the data;

s402: the user realizes equipment control and data query through binding of the WeChat public number, and the Chinese wolfberry leaf scab detection platform completes the detection of the existence of the Chinese wolfberry leaf scab.

The technical effects of the present invention will be described in detail with reference to the tests below.

According to the growth characteristics and the requirements of the medlar garden, the automatic monitoring of various environmental factors in the medlar garden is realized through the sensor. The application not only saves cost and improves efficiency, but also has great progress in the aspect of intelligent control, and promotes the development of the technology of the Internet of things. Due to the development of intelligent agriculture, many foreign scholars have made intensive studies on crop yield, plant diseases and insect pests, fertilization and monitoring of the growth process of crops.

Currently, research on image processing has been actively conducted in the agricultural engineering field at home and abroad. In many western developed countries, the modernization process of agricultural rural areas is developed greatly by means of image processing technology, and good effects are achieved in the aspect of agricultural production. Compared with the prior identification method which is different from the prior identification method by manpower and personal experience, the image processing technology based on computer vision has better accuracy, objectivity and timeliness in the aspect of agricultural application, can accurately distinguish the diseases of different crops, and provides technical guarantee for the subsequent pesticide spraying and treatment of the crops. The image recognition technology based on the neural network algorithm is widely applied to recognition and processing of crop diseases, and has high recognition accuracy and good effect on plant leaf scab images.

The CNN realizes that the image recognition is roughly subjected to two processes of training and testing, and the pixel, the quality and the size of a training set image directly influence the training effect, so that the test result generates great deviation. In order to complete the development of the platform, the photos of the Chinese wolfberry leaves are mostly shot on the spot, and the Chinese wolfberry leaf photos are high in pixel and quality and suitable for model training. And the data set is expanded by data enhancement methods such as rotation, contrast enhancement, brightness enhancement and the like, the healthy leaf data set is expanded into 500, the scab leaf data set is expanded into 1013, and the huge data set is beneficial to feature extraction and loss function convergence in the training process.

The parameter batch involved in the training process is the batch size, i.e. the number of training samples taken from the training set during each training. epoch is the period, one period equals to one training time using all samples in the training set; the parameter loss related in the test result is a loss function value, and the function of the loss function is to describe the difference between the predicted value and the true value of the model. The lower the loss is, the smaller the difference between the predicted value and the true value of the model is proved to be, and the better the trained model effect is. and acc is the identification accuracy of the training set, val _ loss is a loss function value of the model when the model is verified in the verification set, val _ acc is the identification accuracy of the model when the model is verified in the verification set, and verification is performed after each epoch is executed.

(1) Under Keras frame

Different batch and epoch were set, and the parameter results obtained after training by comparing the inclusion v3 and the DenseNet model under the Keras framework are shown in table 1. From the analysis in the following table, when batch is 8 and epoch is 30, the loss value of the two models is the lowest, acc value is the highest and the training effect of the DenseNet model under the same parameters is better than that of the inclusion v3 model.

TABLE 1 different parameter training results table under Keras framework

The results of the same lesion leaf picture test using the inclusion V3 model trained with different parameters are shown in fig. 5.

Fig. 5(a) shows the result of the model trained under the condition of the parameter "batch" 16, the result of the model trained under the parameter "epoch" 10, the result of the model trained under the parameter "batch" 8, the parameter "epoch" 10, the result of the model trained under the parameter "batch" 16, the parameter "epoch" 30, the result of the model trained under the parameter "batch" 8, and the result of the model trained under the parameter "epoch" 30.

The results of the same healthy leaf picture test using the inclusion V3 model trained with different parameters are shown in fig. 6.

Fig. 6(a) shows the result of the model trained under the condition of the parameter "batch" 16, the result of the model trained under the parameter "epoch" 10, the result of the model trained under the parameter "batch" 8, the parameter "epoch" 10, the result of the model trained under the parameter "batch" 16, the parameter "epoch" 30, the result of the model trained under the parameter "batch" 8, and the result of the model trained under the parameter "epoch" 30. From the comparative analysis of the two figures, when batch is 8 and epoch is 30, the accuracy of detecting whether the lesion is present or not is the highest, and the model trained under the parameters is the optimal model of inclusion v 3.

The results of the same lesion leaf picture test using the DenseNet model trained with different parameters are shown in fig. 7.

Fig. 7(a) shows the result of the model trained under the condition of the parameter "batch" 16, the result of the model trained under the parameter "epoch" 10, the result of the model trained under the parameter "batch" 16, the parameter "epoch" 30, the result of the model trained under the parameter "batch" 8, the parameter "epoch" 10, the result of the model trained under the parameter "batch" 8, and the result of the model trained under the parameter "epoch" 30.

The test results of the same healthy leaf pictures using the DenseNet model trained with different parameters are shown in fig. 8.

Fig. 8(a) shows the result of the model trained under the condition of the parameter "batch" 16, the result of the model trained under the parameter "epoch" 10, the result of the model trained under the parameter "batch" 16, the parameter "epoch" 30, the result of the model trained under the parameter "batch" 8, the parameter "epoch" 10, the result of the model trained under the parameter "batch" 8, and the result of the model trained under the parameter "epoch" 30.

From the comparative analysis of the two figures, when batch is 8 and epoch is 30, the accuracy of detecting whether the lesion is present or not is the highest, and the model trained under the parameters is the best model of DenseNet.

As can be seen from comparison of fig. 5 to fig. 8, the accuracy of detecting whether lesion spots of the DenseNet model are detected in the optimal model obtained by training under the phase diagram parameters is higher than that of the inclusion v3 model.

(2) Under Tensorflow framework

Different batch and epoch settings were set, and the results of training parameters under different size data sets compared to the IncepotionV 3 model under the TensorFlow framework are shown in Table 2-2. From the analysis in the following table, when batch is 8 and epoch is 30, the larger the training set is, the lowest the loss value of the model is, and the highest the acc value is, i.e. the better the recognition effect is.

TABLE 2 different parameter training result table under TensorFlow framework

From the comparison, the effect of model training is affected by batch, epoch and training set size. The loss value is in direct proportion to the batch, in inverse proportion to the epoch and in inverse proportion to the size of the training set; the acc value is inversely proportional to batch, proportional to epoch, and proportional to training set size. And according to the analysis of the test result, the smaller the loss value is, the larger the acc value is, the better the recognition effect is, and the higher the recognition accuracy is.

The accuracy rate of the detection of the disease spots of the Incepison V3 model under the TensorFlow framework is slightly higher than that of the DenseNet model under the Keras framework.

It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.

The environmental data stored in the MySQL database table by the system of the invention is shown in FIG. 9, the interface of the lesion detection platform of the Chinese wolfberry leaf is shown in FIG. 10, and the result interface of the lesion detection platform of the Chinese wolfberry leaf is shown in FIG. 11.

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 and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims

1. The utility model provides an intelligence agricultural production data monitoring and early warning control system which characterized in that, intelligence agricultural production data monitoring and early warning control system includes:

2. The intelligent agricultural production data monitoring and early warning control system of claim 1, wherein the data acquisition module comprises an environment temperature and humidity acquisition module, a soil humidity acquisition module, an illumination intensity acquisition module, and a video information acquisition module;

the system comprises an environmental temperature and humidity acquisition module, a DHT11 temperature and humidity sensor, an ESP8266WiFi module, a small fan and an LCD1602 character type liquid crystal screen are mounted on an STC12C5A60S2 development board, temperature data of the DHT11 are displayed on the LCD1602, the temperature and humidity data are sent to Xinlangyun application through the ESP8266, the small fan is triggered to rotate when the temperature is higher than or equal to 28 ℃, and the small fan stops rotating when the temperature is lower than 28 ℃;

the camera module and the environmental data acquisition module are integrated to form a data acquisition module; STM32F429 development board drive OV5640 camera module sends video information to computer video receiving window through the serial ports in real time.

3. The intelligent agricultural production data monitoring and early warning control system of claim 1, wherein the new wave cloud application module comprises MySQL database table storage, real-time data display and historical data query functions;

4. An intelligent agricultural production data monitoring and early warning control method for operating the intelligent agricultural production data monitoring and early warning control system of claim 1, wherein the intelligent agricultural production data monitoring and early warning control method comprises the following steps:

5. The intelligent agricultural production data monitoring and early warning control method of claim 4, wherein the first step of collecting and uploading environmental data and video information, the data storage specifically comprises:

6. The intelligent agricultural production data monitoring and early warning control method of claim 5, wherein the Xinlang cloud application server has a designated port for monitoring, waiting for a connection request from a WiFi terminal; monitoring the request from the new wave cloud application end, and responding to the subsequent request in time; when the request arrives, the WiFi terminal sends data to the Xinlang cloud application terminal by using Socket writing operation, and the Xinlang cloud application terminal receives the data sent by the WiFi terminal by using Socket reading operation; setting a WiFi working mode to be a Station mode by using an ESP8266 debugging tool, setting a hotspot name and a password required to be connected by the WiFi module, and acquiring a Xinlang cloud application IP address as a destination address transmitted by the WiFi module; and entering a transparent transmission mode to transmit the data after packaging.

7. The intelligent agricultural production data monitoring and early warning control method of claim 4, wherein the second step of user binding through WeChat public number to implement equipment control and data query, and the detection of the medlar leaf scab by the medlar leaf scab detection platform comprises:

8. A computer program for implementing the intelligent agricultural production data monitoring and early warning control method of any one of claims 4 to 7.

9. An information data processing terminal for realizing the intelligent agricultural production data monitoring and early warning control method according to any one of claims 4 to 7.

10. A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the intelligent agricultural production data monitoring and warning control method of any one of claims 4 to 7.