Agricultural science and technology remote monitoring system and method based on Internet of Things

Abstract

The invention discloses an agricultural science and technology remote monitoring system and method based on the Internet of Things, and belongs to the technical field of data processing methods suitable for the monitoring purpose. Growth indexes of crops are collected, and the growth state of the crops is evaluated according to a crop growth state evaluation model; collecting pest and disease damage pictures, and analyzing the severity of crop pest and disease damage infection according to the pest and disease damage infection analysis model; evaluating the health degree of the crops according to the crop health state comprehensive evaluation model; importing the health degree of the crops into a pesticide dosage analysis model to obtain the spraying amount of the pesticide; according to the invention, growth indexes and pest and disease information of crops are collected, growth states and pest and disease infection conditions of the crops are combined to jointly analyze health conditions of the crops, the pesticide spraying amount is analyzed according to the health conditions of the crops, the dosage of the farmland can be accurate, the farmland treatment effect is enhanced, and the pest and disease control efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing methods suitable for monitoring purposes, and further relates to an agricultural technology remote monitoring system and method based on the Internet of Things. Background Art

[0002] Crop diseases and insect pests are one of the main agricultural disasters in my country. They are characterized by many types, great impacts, and frequent outbreaks. The scope and severity of their occurrence often cause heavy losses to my country's national economy, especially agricultural production. Crops are often harmed by diseases and insect pests from sowing, growth to harvesting, thus affecting the yield and quality of farmland. The following types of diseases and insect pests are common in my country's crops: rice planthoppers, powdery mildew, corn borers, cotton bollworms, wheat rust, cotton aphids, rice sheath blight, rice blast, wheat aphids, wheat red spiders, locusts, wheat ergot, etc. They have become major diseases and insect pests that seriously affect my country's agricultural production. Therefore, the prevention and control of crop diseases and insect pests is extremely important.

[0003] Most existing technologies determine the location and dosage of pesticides in farmland based on pest and disease conditions monitored by pictures or sensors. They lack the collection of crop growth indicators and pest and disease information, and do not combine crop growth status and pest and disease infection conditions to jointly analyze crop health conditions. They then analyze the amount of pesticide spraying based on the crop health conditions. As a result, they are unable to accurately control the amount of pesticides used in farmland, resulting in poor farmland management effects. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention proposes an agricultural science and technology remote monitoring system and method based on the Internet of Things. The present invention collects growth indicators of crops, and evaluates the growth status of crops according to a crop growth status evaluation model; collects pest and disease pictures, and analyzes the severity of crop pest and disease infection according to a pest and disease infection analysis model; introduces the growth status of crops and the severity of crop pest and disease infection into a comprehensive evaluation model for crop health status to evaluate the health of crops; introduces the health of crops into a dosage analysis model to obtain the spraying amount of pesticides; the present invention collects crop growth indicators and pest and disease information, combines the growth status of crops with the pest and disease infection situation to jointly analyze the crop health status, and analyzes the pesticide spraying amount according to the crop health status, so as to accurately determine the dosage of pesticides in farmland, enhance the farmland management effect, and thus improve the prevention and control efficiency of pests and diseases.

[0005] To achieve the above object, the present invention provides the following technical solutions: A remote monitoring method for agricultural technology based on the Internet of Things includes the following specific steps: Collecting crop growth indicators and evaluating crop growth status according to crop growth status evaluation model; Collect pest and disease pictures and analyze the severity of crop pest and disease infection based on the pest and disease infection analysis model; The growth status of crops and the severity of crop pests and diseases are introduced into the comprehensive assessment model of crop health status to evaluate the health of crops; Import the health status of crops into the dosage analysis model to obtain the amount of pesticide sprayed.

[0006] Furthermore, the collecting of crop growth indicators and evaluating the growth status of crops according to the crop growth status evaluation model comprises the following specific steps: S11, collecting growth indicators of crops, wherein the growth indicators of crops include plant height, leaf area and chlorophyll content; S12, importing the collected crop growth indicators into a crop growth status assessment model to assess the growth status of the crops, wherein the crop growth status assessment model includes a growth status abnormality assessment value calculation formula, and the growth status abnormality assessment value calculation formula is: ; In the formula, is the coefficient of abnormal plant height, is the leaf area abnormality coefficient, is the abnormal proportion coefficient of chlorophyll content, , is the plant height of the i-th crop collected, is the number of crops in the field to be harvested, is the standard plant height of crops corresponding to the growth cycle, is the leaf area of ​​the xth leaf of the ith crop collected, is the number of leaves for each crop, is the standard leaf area of ​​crops corresponding to the growth period, is the chlorophyll content of the xth leaf of the ith crop collected, It is the standard chlorophyll content of crops in the corresponding growth cycle.

[0007] Furthermore, the collecting of pest and disease images and analyzing the severity of crop pest and disease infection according to the pest and disease infection analysis model includes the following specific steps: S21, collecting pest and disease pictures, pre-processing the pest and disease pictures, and obtaining pest and disease area information, wherein the pest and disease area information includes the number of pest and disease areas, the area of ​​the pest and disease areas, and the distance between two pest and disease areas; S22, the number of pest and disease areas, the area of ​​the pest and disease areas, and the distance between two pest and disease areas are introduced into a pest and disease infection analysis model to analyze the severity of crop pest and disease infection, wherein the pest and disease infection analysis model includes a pest and disease infection severity value calculation formula, and the pest and disease infection severity value calculation formula is: ; In the formula, is the number of pest and disease pictures collected, is the area of ​​the vth pest-infected area in the uth image collected, is the maximum threshold of the preset safe area of ​​the pest infection area, is the number of areas infected by pests and diseases, is the distance between the vth pest-infected area and its nearest pest-infected area in the uth image collected, It is the minimum threshold of the safe distance between two preset pest and disease infection areas.

[0008] Furthermore, the step of introducing the growth status of crops and the severity of crop pests and diseases infection into the comprehensive assessment model of crop health status to assess the health status of crops includes the following specific steps: The calculated abnormal growth status evaluation value and the pest and disease infection severity value are obtained, and the abnormal growth status evaluation value and the pest and disease infection severity value are introduced into a comprehensive crop health status evaluation model to evaluate the health of the crops. The comprehensive crop health status evaluation model includes a crop health evaluation value calculation formula, which is: , where is the growth status abnormal assessment value ratio coefficient, is the coefficient of the severity of pests and diseases infection, .

[0009] Furthermore, the method of importing the health status of crops into the pesticide dosage analysis model to obtain the spraying amount of pesticides includes the following specific steps: The calculated crop health assessment value is obtained, and the crop health assessment value is introduced into a dosage analysis model to obtain the spraying amount of the pesticide. The dosage analysis model includes a crop dosage calculation formula, and the crop dosage calculation formula is: , where is the preset threshold value for crop health assessment. is the standard dosage per unit area of ​​farmland. The area of ​​farmland that needs to be sprayed with pesticides.

[0010] An agricultural technology remote monitoring system based on the Internet of Things, used to implement the above-mentioned agricultural technology remote monitoring method based on the Internet of Things, including a control module, a data acquisition module, an image acquisition module, an image processing module, a growth status assessment module, a pest infection analysis module, a health status comprehensive assessment module and a drug dosage analysis module; Furthermore, the data acquisition module is used to collect plant height, leaf area and chlorophyll content of crops; the picture acquisition module is used to collect pictures of pests and diseases; the picture processing module is used to pre-process the pictures of pests and diseases to obtain the number of pest and disease areas, the area of ​​pest and disease areas and the distance between two pest and disease areas; the growth status evaluation module is used to import the collected crop growth indicators into the crop growth status evaluation model to evaluate the growth status of crops; the pest and disease infection analysis module is used to import the collected pest and disease pictures into the pest and disease infection analysis model to analyze the severity of crop pest and disease infection; the health status comprehensive evaluation module is used to import the growth status of crops and the severity of crop pest and disease infection into the crop health status comprehensive evaluation model to evaluate the health of crops; the dosage analysis module is used to import the health of crops into the dosage analysis model to obtain the spraying amount of pesticides; Furthermore, the control module is used to control the operation of the data acquisition module, the image acquisition module, the image processing module, the growth status assessment module, the pest infection analysis module, the health status comprehensive assessment module and the drug dosage analysis module.

[0011] An electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes the above-mentioned agricultural technology remote monitoring method based on the Internet of Things by calling the computer program stored in the memory.

[0012] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned agricultural technology remote monitoring method based on the Internet of Things.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention collects growth indicators of crops, and evaluates the growth status of crops according to a crop growth status evaluation model; collects pest and disease pictures, and analyzes the severity of crop pest and disease infection according to a pest and disease infection analysis model; introduces the growth status of crops and the severity of crop pest and disease infection into a comprehensive crop health status evaluation model to evaluate the health of crops; introduces the health of crops into a dosage analysis model to obtain the spraying amount of pesticides; the present invention collects growth indicators and pest and disease information of crops, combines the growth status of crops with the pest and disease infection situation to jointly analyze the health status of crops, and analyzes the pesticide spraying amount according to the crop health status, so as to accurately determine the dosage of pesticides in farmland, enhance the farmland management effect, and thus improve the prevention and control efficiency of pests and diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 This is a flow chart of a remote monitoring method for agricultural science and technology based on the Internet of Things of the present invention; Figure 2 This is a schematic diagram of the overall framework of an agricultural technology remote monitoring system based on the Internet of Things of the present invention; Figure 3 Schematic diagram of a leaf during the image analysis process of the present invention. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] Example 1 See also Figure 1 and Figure 3 , an embodiment provided by the present invention: a remote monitoring method of agricultural technology based on the Internet of Things, comprising the following specific steps: Collecting crop growth indicators and evaluating crop growth status according to crop growth status evaluation model; In this embodiment, collecting the growth indicators of crops and evaluating the growth status of crops according to the crop growth status evaluation model includes the following specific steps: S11. Collecting growth indicators of crops, including plant height, leaf area and chlorophyll content; In this embodiment, the specific steps of measuring the plant height of crops are: selecting a crop area whose plant height needs to be measured, selecting a plant height measurement sensor (such as a laser rangefinder or a depth camera), and installing the sensor on a suitable platform (such as a drone or a bracket); setting the sensor's measurement range, resolution, sampling frequency and other parameters according to the type and growth stage of the crop to be measured, starting the sensor, and the sensor uses laser or hyperspectral imaging technology to collect three-dimensional morphological data and height information of the crop in real time, transmitting the data collected by the sensor to a data processing system through a wireless network or a wired connection, filtering, denoising and calibrating the collected data to ensure the accuracy of the data, and using data processing software or a custom algorithm to analyze the data collected by the sensor to obtain the plant height of the crop; The specific steps for measuring the leaf area of ​​crops are as follows: determine the farmland area where the leaf area needs to be measured, install the leaf area measurement sensor (such as hyperspectral imaging sensor, multispectral camera, etc.) on the drone or fixed bracket, set the measurement parameters (such as resolution, scanning speed, scanning frequency, exposure time, measurement range, etc.) in the sensor operation interface, formulate a measurement plan according to the measurement area, time and weather conditions, design the flight path of the drone to ensure that the entire measurement area is covered with a moderate overlap, fly the drone to a predetermined height and position, and fly according to the pre-planned route. The sensor automatically collects crop image data during the flight, transmits the collected image data to the data processing system, performs pre-processing steps such as denoising, correction and geocoding, uses special processing software or algorithms to analyze the processed images, extract leaf features, calculate the leaf area of ​​a single leaf or the entire crop, and calculate indicators such as leaf area index (LAI) as needed; The specific steps for measuring the chlorophyll content of crops are: install the chlorophyll meter on a mobile device or a fixed frame to automatically scan the crops, set the measurement parameters of the chlorophyll meter (such as measurement time, measurement interval, data storage, etc.), and calibrate the chlorophyll meter with a standard chlorophyll sample or a leaf with a known chlorophyll content before use. During measurement, the chlorophyll meter automatically identifies and locates the leaf, emits light of a specific wavelength to the leaf, and analyzes and calculates the chlorophyll content based on the changes in the light after passing through the leaf, according to the absorption characteristics of chlorophyll to light. Using a chlorophyll meter to measure chlorophyll content is an existing technology and will not be described in detail here. For details, please refer to the paper "Design and Performance Testing of a Portable Chlorophyll Meter" or related materials.

[0018] S12, importing the collected crop growth indicators into a crop growth status assessment model to assess the growth status of the crops, the crop growth status assessment model includes a growth status abnormality assessment value calculation formula, the growth status abnormality assessment value calculation formula is: ; In the formula, is the coefficient of abnormal plant height, is the leaf area abnormality coefficient, is the abnormal proportion coefficient of chlorophyll content, , is the plant height of the i-th crop collected, is the number of crops in the field to be harvested, is the standard plant height of crops corresponding to the growth cycle, is the leaf area of ​​the xth leaf of the ith crop collected, is the number of leaves for each crop, is the standard leaf area of ​​crops corresponding to the growth period, is the chlorophyll content of the xth leaf of the ith crop collected, It is the standard chlorophyll content of crops in the corresponding growth cycle.

[0019] In this embodiment, the abnormal growth status evaluation value is calculated by plant height, leaf area and chlorophyll content data. The larger the abnormal growth status evaluation value, the worse the growth status of the crop, and the smaller the abnormal growth status evaluation value, the better the growth status of the crop. In this embodiment, the specific steps of obtaining standard data of crops in different growth periods are: Select target crop varieties and select representative plants as samples in each growth cycle (such as sowing period, seedling period, tillering period, jointing period, heading period, grain filling period and maturity period, etc.); Plant height: Place the measuring tool (tape measure or plant height meter) vertically next to the plant and measure the distance from the ground to the highest point of the plant. Measure multiple samples in each growth cycle to obtain the average value as the standard plant height. Record the results of each measurement, and record information such as the measurement date and weather conditions. Leaf area: In each growth cycle, select healthy leaves without pests and diseases, use a leaf area meter or graph paper to measure, measure multiple leaves in each growth cycle, and obtain the average value as the standard leaf area; Chlorophyll content: Select leaf samples, place the leaves in the measuring fixture according to the operating instructions of the chlorophyll meter, start the measurement, and measure multiple leaves in each growth cycle to obtain the average value as the standard chlorophyll content.

[0020] Collect pest and disease pictures and analyze the severity of crop pest and disease infection based on the pest and disease infection analysis model; In this embodiment, collecting pest pictures and analyzing the severity of crop pest infection according to the pest infection analysis model includes the following specific steps: S21, collecting pest and disease images, pre-processing the pest and disease images, and obtaining pest and disease area information, the pest and disease area information including the number of pest and disease areas, the area of ​​the pest and disease areas, and the distance between two pest and disease areas; In this embodiment, the specific steps of image preprocessing are: deploying equipment with image acquisition function in the farmland (such as a high-definition camera installed in the field or an image collector carried by a drone), the equipment automatically captures crop images according to the set acquisition frequency and time or trigger conditions (such as signs of pest activity), the collected image data is transmitted to the data processing center through the network, a filter is used to remove noise in the image (such as median filtering), the image quality is improved by means of histogram equalization, contrast enhancement, grayscale processing, etc., so that the characteristics of pests and diseases are more obvious, and threshold segmentation, edge detection and other methods are used to separate pest and disease areas; The specific steps to obtain pest information are: determine the boundaries of pests and diseases through contour detection, use the 'findContours' function in OpenCV to extract the contours in the image, select the appropriate contour retrieval mode (for example, 'RetrievalMode.EXTERNAL' only detects external contours, or 'RetrievalMode.ALL' detects all contours), and select the contour approximation method (for example, 'ApproximationMethod.NO' only stores the corner points of the contour to reduce the amount of data), analyze the extracted contours, and filter out possible pest contours based on the shape, size and other characteristics of the pests and diseases (can be filtered by the area, perimeter, shape factor and other attributes of the contours). For the filtered contours, use 'approxPolyDP' and other functions to approximate the contours, further simplify the contours, determine the boundaries of pests and diseases (can be done by calculating the point set of the contour or by the convex hull of the contour, the minimum circumscribed rectangle and other methods), verify and correct the extracted pest contours, mark the location of the pests and diseases, calculate the area within the pest contours, and convert it into the actual area in pixels through calibration; Determine the coordinates of the center point of each pest area, and use the Euclidean distance formula to calculate the distance between two pest center points. The Euclidean distance is a commonly used distance definition, which refers to the real distance between two points in m-dimensional space, or the natural length of a vector (that is, the distance from the point to the origin). The Euclidean distance in two-dimensional and three-dimensional space is the actual distance between two points. The formula for two-dimensional space is , For point With point The Euclidean distance between them, the coordinate system is constructed as follows: the lower left corner of the collected pest and disease picture is taken as the coordinate origin, the length is the horizontal coordinate, the width is the vertical coordinate, and the coordinate unit is pixel.

[0021] S22. The number of pest and disease areas, the area of ​​the pest and disease areas, and the distance between two pest and disease areas are introduced into a pest and disease infection analysis model to analyze the severity of crop pest and disease infection. The pest and disease infection analysis model includes a pest and disease infection severity value calculation formula, which is: ; In the formula, is the number of pest and disease pictures collected, is the area of ​​the vth pest-infected area in the uth image collected, is the maximum threshold of the preset safe area of ​​the pest infection area, is the number of areas infected by pests and diseases, is the distance between the vth pest-infected area and its nearest pest-infected area in the uth image collected, It is the minimum threshold of the safe distance between two preset pest and disease infection areas.

[0022] In this embodiment, the severity value of the pest infection is calculated by the number of pest infection areas, the area of ​​the pest infection areas and the distance between two pest infection areas. The larger the severity value of the pest infection is, the more serious the infection of the crops is.

[0023] The growth status of crops and the severity of crop pests and diseases are introduced into the comprehensive assessment model of crop health status to evaluate the health of crops; In this embodiment, the growth status of crops and the severity of crop pests and diseases infection are introduced into the comprehensive assessment model of crop health status to assess the health status of crops, including the following specific steps: The calculated abnormal growth status assessment value and the pest and disease infection severity value are obtained, and the abnormal growth status assessment value and the pest and disease infection severity value are introduced into the comprehensive assessment model of crop health status to evaluate the health of crops. The comprehensive assessment model of crop health status includes a crop health assessment value calculation formula, and the crop health assessment value calculation formula is: , where is the growth status abnormal assessment value ratio coefficient, is the coefficient of the severity of pests and diseases infection, .

[0024] In this embodiment, the crop health assessment value is obtained by comprehensive assessment of the abnormal growth state assessment value and the pest and disease infection severity value. The smaller the crop health assessment value, the better the health of the crop, and the larger the crop health assessment value, the worse the health of the crop. In this embodiment, the plant height abnormality ratio , Leaf area abnormality ratio coefficient , Abnormal chlorophyll content ratio coefficient , the preset maximum threshold of the safe area of ​​the pest infection area , the minimum threshold of the safe distance between two pest-infected areas , Growth status abnormal assessment value ratio coefficient and the coefficient of the proportion of the severity of pests and diseases infection The value-taking method is as follows: select one acre of farmland containing healthy crops and crops with pests and diseases as the experimental sample, collect crop growth indicators and pest and disease pictures, import the growth indicators into the growth status abnormality evaluation value calculation formula to calculate the crop growth status abnormality evaluation value; import the pest and disease pictures into the pest and disease infection severity value calculation formula to calculate the pest and disease infection severity value; import the growth status abnormality evaluation value and the pest and disease infection severity value into the crop health evaluation value calculation formula to calculate the crop health evaluation value; import the crop health evaluation value into the dosage calculation formula to obtain the pesticide spraying amount, and then invite 10 agricultural experts to evaluate the health of the crops, and analyze the appropriate pesticide spraying amount based on the evaluation data, and import the calculated crop health status, pesticide spraying amount, and the crop health status and pesticide spraying amount analyzed by agricultural experts into professional fitting software to match a set of data with the highest similarity, and obtain the corresponding plant height abnormality ratio coefficient. , Leaf area abnormality ratio coefficient , Abnormal chlorophyll content ratio coefficient , the preset maximum threshold of the safe area of ​​the pest infection area , the minimum threshold of the safe distance between two pest-infected areas , Growth status abnormal assessment value ratio coefficient and the coefficient of the proportion of the severity of pests and diseases infection The value of .

[0025] Import the health status of crops into the dosage analysis model to obtain the amount of pesticide sprayed; In this embodiment, the health status of crops is introduced into the pesticide dosage analysis model to obtain the spraying amount of pesticides, including the following specific steps: The calculated crop health assessment value is obtained, and the crop health assessment value is imported into the dosage analysis model to obtain the spraying amount of the pesticide. The dosage analysis model includes a crop dosage calculation formula, and the crop dosage calculation formula is: , where is the preset threshold value for crop health assessment. is the standard dosage per unit area of ​​farmland. The area of ​​farmland that needs to be sprayed with pesticides.

[0026] In this embodiment, the specific steps of obtaining the standard dosage of pesticide per unit area of ​​crops are: determining the type of crops to be applied with pesticides, and determining the standard dosage per unit area (usually in grams per mu or milliliters per square meter) according to the pesticide instructions and labels or by consulting agricultural related technical institutions; The steps to obtain the area of ​​farmland are as follows: take pictures of the area where the farmland is located by using a drone equipped with a camera, import the pictures into image processing software or GIS software, mark the boundaries of the farmland according to the images, and determine the areas where pesticides need to be sprayed. For irregularly shaped farmland, use the measurement tools of the GIS software to directly calculate the area of ​​the plot (import the farmland picture into the GIS software, set the coordinate system and scale, create a new vector layer, and use the drawing tool to draw the boundaries of the farmland. Select the drawn polygon, and the software will automatically display the area information); for regularly shaped farmland, measure the side length and use the corresponding geometric formula (such as length*width) to calculate the area; Here is a sample step-by-step guide to calculating medication dosage: To treat pests and diseases in one acre of wheat field, according to the instructions on the pesticide label, the recommended dosage is 70 grams of pesticide per acre. The area of ​​the wheat field is 5 acres. Under standard circumstances, the total amount of pesticide required is: 70 grams / acre × 5 acres = 350 grams.

[0027] Example 2 See also Figure 2 , an agricultural technology remote monitoring system based on the Internet of Things, used to implement the above-mentioned agricultural technology remote monitoring method based on the Internet of Things, including a control module, a data acquisition module, an image acquisition module, an image processing module, a growth status assessment module, a pest infection analysis module, a health status comprehensive assessment module and a drug dosage analysis module; In this embodiment, the data acquisition module is used to collect plant height, leaf area and chlorophyll content of crops; the picture acquisition module is used to collect pictures of pests and diseases; the picture processing module is used to pre-process the pictures of pests and diseases to obtain the number of pest and disease areas, the area of ​​pest and disease areas and the distance between two pest and disease areas; the growth status evaluation module is used to import the collected crop growth indicators into the crop growth status evaluation model to evaluate the growth status of crops; the pest and disease infection analysis module is used to import the collected pest and disease images into the pest and disease infection analysis model to analyze the severity of crop pest and disease infection; the health status comprehensive evaluation module is used to import the growth status of crops and the severity of crop pest and disease infection into the crop health status comprehensive evaluation model to evaluate the health of crops; the dosage analysis module is used to import the health of crops into the dosage analysis model to obtain the spraying amount of pesticides; In this embodiment, the control module is used to control the operation of the data acquisition module, the image acquisition module, the image processing module, the growth status assessment module, the pest infection analysis module, the health status comprehensive assessment module and the drug dosage analysis module.

[0028] Example 3 This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes the above-mentioned agricultural technology remote monitoring method based on the Internet of Things by calling the computer program stored in the memory; The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a remote monitoring method of agricultural technology based on the Internet of Things provided by the above-mentioned method embodiment. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data, which will not be described in detail in this embodiment.

[0029] Example 4 This embodiment provides a computer-readable storage medium storing instructions, which, when a computer program is executed on a computer device, enables the computer device to execute the above-mentioned agricultural technology remote monitoring method based on the Internet of Things; For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0030] In the description of this specification, the descriptions with reference to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0031] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can well understand and use the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A remote monitoring method for agricultural technology based on the Internet of Things, characterized in that: The specific steps include: Collecting crop growth indicators and evaluating crop growth status according to crop growth status evaluation model; Collect pest and disease pictures and analyze the severity of crop pest and disease infection based on the pest and disease infection analysis model; The growth status of crops and the severity of crop pests and diseases are introduced into the comprehensive assessment model of crop health status to evaluate the health of crops; Import the health status of crops into the dosage analysis model to obtain the amount of pesticide sprayed.

2. The method for remote monitoring of agricultural technology based on the Internet of Things as claimed in claim 1, characterized in that: The collecting of crop growth indicators and evaluating the crop growth status according to the crop growth status evaluation model comprises the following specific steps: S11, collecting growth indicators of crops, wherein the growth indicators of crops include plant height, leaf area and chlorophyll content; S12, importing the collected crop growth indicators into a crop growth status assessment model to assess the growth status of the crops, wherein the crop growth status assessment model includes a growth status abnormality assessment value calculation formula, and the growth status abnormality assessment value calculation formula is: ； In the formula, is the coefficient of abnormal plant height, is the leaf area abnormality coefficient, is the chlorophyll content abnormality coefficient, , is the plant height of the i-th crop collected, is the number of crops in the field to be harvested, is the standard plant height of crops corresponding to the growth cycle, is the leaf area of ​​the xth leaf of the ith crop collected, is the number of leaves for each crop, is the standard leaf area of ​​crops corresponding to the growth period, is the chlorophyll content of the xth leaf of the ith crop collected, It is the standard chlorophyll content of crops in the corresponding growth cycle.

3. The method for remote monitoring of agricultural technology based on the Internet of Things as claimed in claim 2, characterized in that: The collecting of pest and disease images and analyzing the severity of crop pest and disease infection according to the pest and disease infection analysis model includes the following specific steps: S21, collecting pest and disease pictures, pre-processing the pest and disease pictures, and obtaining pest and disease area information, wherein the pest and disease area information includes the number of pest and disease areas, the area of ​​the pest and disease areas, and the distance between two pest and disease areas; S22, the number of pest and disease areas, the area of ​​the pest and disease areas, and the distance between two pest and disease areas are introduced into a pest and disease infection analysis model to analyze the severity of crop pest and disease infection, wherein the pest and disease infection analysis model includes a pest and disease infection severity value calculation formula, and the pest and disease infection severity value calculation formula is: ； In the formula, is the number of pest and disease pictures collected, is the area of ​​the vth pest-infected area in the uth image collected, is the maximum threshold of the preset safe area of ​​the pest infection area, is the number of areas infected by pests and diseases, is the distance between the vth pest-infected area and its nearest pest-infected area in the uth image collected, It is the minimum threshold of the safe distance between two preset pest and disease infection areas.

4. The method for remote monitoring of agricultural technology based on the Internet of Things as claimed in claim 3, characterized in that: The method of introducing the growth status of crops and the severity of crop pests and diseases into the comprehensive assessment model of crop health status to assess the health status of crops includes the following specific steps: The calculated abnormal growth status evaluation value and the pest and disease infection severity value are obtained, and the abnormal growth status evaluation value and the pest and disease infection severity value are introduced into a comprehensive crop health status evaluation model to evaluate the health of the crops. The comprehensive crop health status evaluation model includes a crop health evaluation value calculation formula, which is: , where is the growth status abnormal assessment value ratio coefficient, is the coefficient of the severity of pests and diseases infection, .

5. The method for remote monitoring of agricultural technology based on the Internet of Things as claimed in claim 4, characterized in that: The method of importing the health status of crops into the pesticide dosage analysis model to obtain the pesticide spraying amount includes the following specific steps: The calculated crop health assessment value is obtained, and the crop health assessment value is introduced into a dosage analysis model to obtain the spraying amount of the pesticide. The dosage analysis model includes a crop dosage calculation formula, and the crop dosage calculation formula is: , where is the preset threshold value for crop health assessment. is the standard dosage per unit area of ​​farmland. The area of ​​farmland that needs to be sprayed with pesticides.

6. An agricultural technology remote monitoring system based on the Internet of Things, used to implement the agricultural technology remote monitoring method based on the Internet of Things as claimed in any one of claims 1 to 5, characterized in that: It includes a control module, a data acquisition module, an image acquisition module, an image processing module, a growth status assessment module, a pest infection analysis module, a health status comprehensive assessment module and a drug dosage analysis module. The data acquisition module is used to collect plant height, leaf area and chlorophyll content of crops; The picture collection module is used to collect pictures of pests and diseases; The image processing module is used to pre-process the pest image to obtain the number of pest areas, the area of ​​the pest area and the distance between two pest areas; The growth status assessment module is used to import the collected crop growth indicators into the crop growth status assessment model to assess the growth status of the crops; The pest infection analysis module is used to import the collected pest images into the pest infection analysis model to analyze the severity of crop pest infection.

7. The agricultural technology remote monitoring system based on the Internet of Things as claimed in claim 6, characterized in that: The health status comprehensive assessment module is used to import the growth status of crops and the severity of crop pests and diseases into the crop health status comprehensive assessment model to assess the health of crops; The pesticide dosage analysis module is used to import the health status of crops into the pesticide dosage analysis model to obtain the spraying amount of pesticides; The control module is used to control the operation of the data acquisition module, the image acquisition module, the image processing module, the growth status assessment module, the pest infection analysis module, the health status comprehensive assessment module and the drug dosage analysis module.

8. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program that can be called by the processor, and the processor executes the agricultural technology remote monitoring method based on the Internet of Things as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes an agricultural technology remote monitoring method based on the Internet of Things as described in any one of claims 1-5.

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