Greenhouse crop disease and pest comprehensive monitoring method and management system based on big data

Abstract

The invention relates to the technical field of data analysis and monitoring, in particular to a greenhouse crop disease and pest comprehensive monitoring method and management system based on big data, and the method specifically comprises the steps: modeling a digital twinborn model of a greenhouse, and measuring to obtain a stomatal conductance coefficient and a fluorescence parameter of crops; calculating to obtain a first leaf health threat value and a second leaf health threat value, and comprehensively judging the pest influence degree of the pest monitoring area according to the second leaf health threat value; and the first leaf health threat value and the second leaf health threat value of each pest monitoring area in the greenhouse are visualized to the greenhouse digital twinborn model, so that the problems that greenhouse crop pest monitoring is not accurate enough, the real-time performance is insufficient and the information integration is not comprehensive in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and monitoring, and is a method and management system for integrated monitoring of greenhouse crop diseases and insect pests based on big data. Background Art

[0002] With the continuous development of agricultural technology, greenhouse cultivation has become an important means to improve crop yield and quality. However, pests and diseases in greenhouse environments are still one of the main threats to crop growth. Traditional greenhouse pest and disease monitoring methods mainly rely on manual inspections and empirical judgments, which makes it difficult to achieve early warning and accurate monitoring of pests and diseases.

[0003] In terms of the collection of pest and disease monitoring parameters, existing technologies mainly rely on single sensors or image recognition technologies, such as identifying leaf spots only through visual images or performing environmental monitoring through simple temperature and humidity sensors. This single technical means is difficult to provide sufficient basis for the comprehensive assessment of pests and diseases, lacking the ability to conduct multi-dimensional and multi-parameter comprehensive analysis. In addition, existing technologies have problems with the real-time and poor accuracy of data analysis, making it difficult to timely discover and predict the outbreak trend of pests and diseases. Summary of the invention

[0004] The technical problem to be solved by the present invention is that the greenhouse crop disease and insect pest monitoring in the prior art is not accurate enough, lacks real-time performance and has incomplete information integration. A comprehensive greenhouse crop disease and insect pest monitoring method and management system based on big data are proposed.

[0005] In order to achieve the above-mentioned object, the technical solution of the greenhouse crop pest and disease integrated monitoring method based on big data of the present invention comprises the following steps:

[0006] S1: Build a digital twin model of the greenhouse and divide the greenhouse into pest monitoring areas according to the growth rate of each crop in the greenhouse;

[0007] S2: Conduct laser scattering tests on crops in the pest monitoring area, measure the stomatal conductance coefficient of crops, and simultaneously measure the fluorescence parameters of crops in the pest monitoring area;

[0008] S3: According to S2, the first leaf health threat value is calculated and obtained, and the first leaf health threat value of each pest monitoring area in the greenhouse is visualized in the greenhouse digital twin model;

[0009] S4: Real-time collection of leaf surface growth images and leaf surface temperature images of crops in each pest monitoring area;

[0010] S5: Calculate the second leaf health threat value according to the leaf surface growth image and the leaf surface temperature image, make a comprehensive judgment on the degree of impact of pests in the pest monitoring area according to the second leaf health threat value, and visualize the second leaf health threat values ​​of each pest monitoring area in the greenhouse into the greenhouse digital twin model.

[0011] Specifically, in S2, the laser scattering test includes:

[0012] S21: raising the plastic isolation cloths on both sides of a single pest monitoring area to construct an airflow channel, and collecting height data of crop leaf growth in the pest monitoring area, and dividing the crop leaf growth area into N horizontal height layers according to the height data;

[0013] S22: installing the laser transmitter on the left side of the pest monitoring area and performing horizontal adjustment of the laser transmitter to ensure that the laser beam can evenly pass through the gas environment around the crop leaves, wherein the horizontal adjustment includes: before each laser scattering test, adjusting the laser transmitter to be consistent with the horizontal height layer where the growth height layer corresponding to the crop leaves is located;

[0014] S23: Place a laser signal detector on the right side of the pest monitoring area, adjust the horizontal height of the receiver to be consistent with the horizontal height layer of the laser transmitter, and receive the scattered signal through the receiver;

[0015] S24: Start the laser transmitter, perform laser scattering tests at N horizontal height layers respectively, record the scattering signals, and calculate the stomatal conductance coefficient of the crops according to the scattering signals.

[0016] Specifically, the calculation strategy of the stomatal conductance coefficient of the crop is as follows:

[0017]

[0018] Among them, gd n It represents the stomatal conductance coefficient of crops in the nth horizontal layer;

[0019] η n is the air velocity coefficient in the nth horizontal altitude layer;

[0020] A is the standard surface stomatal area of ​​crops in the pest monitoring area;

[0021] C n is the gas concentration value in the nth horizontal altitude layer, C=k×Q, k is the conversion ratio coefficient, and Q is the scattered light intensity received by the receiver;

[0022] It represents the rate of change of gas concentration in the nth horizontal altitude layer within a monitoring period t.

[0023] Specifically, in S2, measuring the fluorescence parameters of crops in the pest monitoring area includes the following specific steps:

[0024] S25: selecting three leaves with the largest leaf areas at each horizontal height layer of each crop plant to form a set of leaves to be tested for each crop plant;

[0025] S26: Simultaneously, using a pulse amplitude modulation fluorescence instrument, measuring fluorescence parameters of the set of leaves to be tested of each crop, wherein the fluorescence parameters include: an initial fluorescence parameter F0 and a maximum fluorescence parameter Fm;

[0026] S27: Calculate the photosynthetic efficiency coefficient of crops based on the initial fluorescence parameters and the maximum fluorescence parameters, specifically: Among them, i n is a subscript, indicating the i-th level in the n-th horizontal altitude layer n crops; there are a total of I n crops;

[0027] Indicates that in the nth horizontal layer, the i-th n The maximum value of the maximum fluorescence parameter in the set of leaves to be tested of the crop;

[0028] It represents the average value of the maximum fluorescence parameters of crops in the nth horizontal altitude layer.

[0029] Specifically, S3 includes the following specific steps:

[0030] S31: Extract the stomatal conductance coefficient and photosynthetic efficiency coefficient of crops in the pest monitoring area;

[0031] S32: Importing the stomatal conductance coefficient and the photosynthetic efficiency coefficient into a first leaf health threat value calculation strategy to calculate and obtain a first leaf health threat value, wherein the first leaf health threat value calculation strategy is specifically:

[0032]

[0033] Among them, wx1 is the health threat value of the first leaf;

[0034] S33: Preset a first leaf health threshold and conduct a preliminary assessment of the degree of impact of insect pests on crops in the insect pest monitoring area;

[0035] When the first leaf health threat value in the pest monitoring area is greater than the first leaf health threshold, it is assessed that the crops in the pest monitoring area are in a healthy state;

[0036] When the first leaf health threat value of the pest monitoring area is less than or equal to the first leaf health threshold, the first leaf health threat value of each pest monitoring area in the greenhouse is visualized in the greenhouse digital twin model, a suspected pest warning is issued to the greenhouse management platform, and step S4 is executed synchronously.

[0037] Specifically, S4 includes the following specific steps:

[0038] S41: collecting the leaf surface growth images of crops in each pest monitoring area in real time, wherein the leaf surface growth images are collected by a camera device;

[0039] S42: Importing the leaf surface growth image into the insect population density factor calculation strategy, the insect population density factor calculation strategy is as follows:

[0040]

[0041] Among them, e 1,n Indicates the insect population density factor at the nth horizontal altitude layer;

[0042] r is a subscript, indicating the i-th level in the n-th horizontal altitude layer n On the crop plant, the rth leaf; Indicates the i n Total number of leaves per crop plant;

[0043] It represents the actual distance between the rth leaf and the bottom of the crop root;

[0044] H represents the i n Total root height of each crop plant;

[0045] represents the number of pixel blocks in the insect mouth area on the leaf surface growth image of the rth leaf;

[0046] NU r Represents the total number of pixel blocks in the leaf area on the leaf surface growth image of the rth leaf;

[0047] Indicates the i n Crops, The average number of pixel blocks per leaf.

[0048] S43: collecting leaf surface temperature images of crops in each pest monitoring area in real time, wherein the leaf surface temperature images are collected by infrared equipment;

[0049] S44: Importing the blade surface temperature image into a temperature distortion factor calculation strategy, wherein the temperature distortion factor calculation strategy is as follows:

[0050]

[0051] Among them, e 2,n represents the temperature distortion factor of the nth horizontal layer;

[0052] It represents the maximum value of pixels in the temperature anomaly area on the leaf surface temperature image of the rth leaf;

[0053] represents the minimum pixel value of the temperature anomaly area on the leaf surface temperature image of the rth leaf;

[0054] Indicates the i n Crops, R in The pixel average value of the temperature abnormal area of ​​each leaf.

[0055] Specifically, S5 includes the following specific steps:

[0056] S51: Calculate a second leaf health threat value according to the leaf surface growth image and the leaf surface temperature image, and the specific calculation formula is:

[0057]

[0058] Wherein, wx2 is the health threat value of the second leaf.

[0059] Specifically, S5 also includes the following specific steps:

[0060] S52: Preset a second leaf health threshold, and make a comprehensive judgment on the degree of pest impact in the pest monitoring area according to the second leaf health threat value, including:

[0061] When the second leaf health threat value is less than or equal to the second leaf health threshold, repeat steps S2-S3, recalculate and obtain the first leaf health threat value, and perform a preliminary assessment of the degree of impact of insect pests on crops in the insect pest monitoring area again;

[0062] When the second blade health threat value is greater than the second blade health threshold, executing step S53;

[0063] S53: Visualize the first leaf health threat value and the second leaf health threat value of each pest monitoring area in the greenhouse into the greenhouse digital twin model.

[0064] Preferably, a visualization strategy is as follows: the visualization includes: arranging the second leaf health threat values ​​of each pest monitoring area in descending order from large to small, recording the first category threat area as a red area, the second category threat area as a yellow area, the third category threat area as a green area, the fourth category threat area as a blue area... the sth category working area as gray... the Sth category working area as white;

[0065] The grayscale value of the threat area decreases as the type ordinal number increases, the grayscale value of the first type of working area is the largest, and the grayscale value of the S type of working area is the smallest.

[0066] The threat area division strategy includes: in the sequence, the division starts from the initial second leaf health threat value, and every two second leaf health threat values ​​constitute a type of threat area.

[0067] In addition, the greenhouse crop pest and disease integrated management system based on big data of the present invention includes the following modules:

[0068] Monitoring area division module, laser scattering measurement module, fluorescence parameter measurement module, first leaf health threat assessment module, second leaf health threat assessment module, pest comprehensive judgment module and visualization module;

[0069] The monitoring area division module is used to model the digital twin model of the greenhouse and divide the greenhouse into pest monitoring areas according to the growth rate of each crop in the greenhouse;

[0070] The laser scattering measurement module is used to perform laser scattering tests on crops in the pest monitoring area to measure and obtain the stomatal conductance coefficient of the crops.

[0071] The fluorescence parameter measurement module is used to measure the fluorescence parameters of crops in the pest monitoring area;

[0072] The first blade health threat assessment module is used to calculate and obtain a first blade health threat value;

[0073] The second leaf health threat assessment module is used to collect leaf surface growth images and leaf surface temperature images of crops in each pest monitoring area in real time, and calculate the second leaf health threat value according to the leaf surface growth images and the leaf surface temperature images;

[0074] The pest comprehensive judgment module makes a comprehensive judgment on the pest impact degree of the pest monitoring area according to the second leaf health threat value;

[0075] The visualization module is used to visualize the first leaf health threat value and the second leaf health threat value of each pest monitoring area in the greenhouse into the greenhouse digital twin model.

[0076] A storage medium stores instructions, and when a computer reads the instructions, the computer executes the integrated monitoring method for greenhouse crop diseases and insect pests based on big data.

[0077] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned integrated monitoring method for greenhouse crop diseases and insect pests based on big data is implemented.

[0078] Compared with the prior art, the technical effects of the present invention are as follows:

[0079] 1. The present invention simultaneously adopts the methods of laser scattering test and fluorescence parameter measurement to comprehensively obtain the stomatal conductance coefficient and fluorescence characteristics of crops, which effectively improves the multidimensionality and accuracy of the data and provides a reliable basis for subsequent crop health threat assessment.

[0080] 2. The present invention collects leaf surface growth images and temperature images in real time and analyzes the images, so that the monitoring information of pests and diseases can be updated in time, ensuring a dynamic grasp of the health status of crops and improving the response speed of the early warning mechanism.

[0081] 3. By calculating the health threat value of the first leaf and the second leaf, the present invention can not only realize timely judgment of the impact of pests, but also actively issue an alarm after discovering potential threats, take prevention and control measures in time, and enhance the scientificity and effectiveness of agricultural production. In addition, the present invention also visualizes the monitoring results into the digital twin model, so that managers can more intuitively understand the health threat situation of each pest monitoring area, facilitate quick management decisions, and reduce the impact of pests and diseases on crops. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0083] in:

[0084] Figure 1 It is a schematic diagram of the process of the greenhouse crop pest and disease integrated monitoring method based on big data of the present invention;

[0085] Figure 2 It is a structural schematic diagram of the greenhouse crop disease and insect pest integrated management system based on big data of the present invention. DETAILED DESCRIPTION

[0086] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0087] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0088] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0089] Embodiment 1:

[0090] like Figure 1 As shown, the greenhouse crop pest and disease integrated monitoring method based on big data in the embodiment of the present invention is as follows: Figure 1 As shown, the specific steps include:

[0091] S1: Build a digital twin model of the greenhouse and divide the greenhouse into pest monitoring areas according to the growth rate of each crop in the greenhouse;

[0092] S2: Conduct laser scattering tests on crops in the pest monitoring area, measure the stomatal conductance coefficient of crops, and simultaneously measure the fluorescence parameters of crops in the pest monitoring area;

[0093] In S2, the laser scattering test includes:

[0094] S21: raising the plastic isolation cloths on both sides of a single pest monitoring area to construct an airflow channel, and collecting height data of crop leaf growth in the pest monitoring area, and dividing the crop leaf growth area into N horizontal height layers according to the height data;

[0095] S22: installing the laser transmitter on the left side of the pest monitoring area and performing horizontal adjustment of the laser transmitter to ensure that the laser beam can evenly pass through the gas environment around the crop leaves, wherein the horizontal adjustment includes: before each laser scattering test, adjusting the laser transmitter to be consistent with the horizontal height layer where the growth height layer corresponding to the crop leaves is located;

[0096] S23: Place a laser signal detector on the right side of the pest monitoring area, adjust the horizontal height of the receiver to be consistent with the horizontal height layer of the laser transmitter, and receive the scattered signal through the receiver;

[0097] S24: Start the laser transmitter, perform laser scattering tests at N horizontal height layers respectively, record the scattering signals, and calculate the stomatal conductance coefficient of the crops according to the scattering signals.

[0098] The calculation strategy of the stomatal conductance coefficient of the crops is as follows:

[0099]

[0100] Among them, gd n It represents the stomatal conductance coefficient of crops in the nth horizontal layer;

[0101] η n is the air velocity coefficient in the nth horizontal altitude layer;

[0102] A is the standard surface stomatal area of ​​crops in the pest monitoring area;

[0103] C n is the gas concentration value in the nth horizontal altitude layer, C=k×Q, k is the conversion ratio coefficient, and Q is the scattered light intensity received by the receiver;

[0104] It represents the rate of change of gas concentration in the nth horizontal altitude layer within a monitoring period t.

[0105] Exemplarily, in this embodiment, the standard surface stomatal surface area of ​​crops in the pest monitoring area is obtained by those skilled in the art according to the growth properties of the crops;

[0106] Furthermore, in this embodiment, a calculation formula for the airflow velocity coefficient is also provided, specifically: λ is the emission wavelength of the laser transmitter, f0 is the laser emission frequency of the laser transmitter; Δf n is the Doppler frequency shift; θ n is the laser scattering angle.

[0107] In S2, measuring the fluorescence parameters of crops in the pest monitoring area includes the following specific steps:

[0108] S25: selecting three leaves with the largest leaf areas at each horizontal height layer of each crop plant to form a set of leaves to be tested for each crop plant;

[0109] S26: Simultaneously, using a pulse amplitude modulation fluorescence instrument, measuring fluorescence parameters of the set of leaves to be tested of each crop, wherein the fluorescence parameters include: an initial fluorescence parameter F0 and a maximum fluorescence parameter Fm;

[0110] S27: Calculate the photosynthetic efficiency coefficient of crops based on the initial fluorescence parameters and the maximum fluorescence parameters, specifically: Among them, i n is a subscript, indicating the i-th level in the n-th horizontal altitude layer n crops; there are a total of I n crops;

[0111] Indicates that in the nth horizontal layer, the i-th n The maximum value of the maximum fluorescence parameter in the set of leaves to be tested of the crop;

[0112] It represents the average value of the maximum fluorescence parameters of crops in the nth horizontal altitude layer.

[0113] S3: According to S2, the first leaf health threat value is calculated and obtained, and the first leaf health threat value of each pest monitoring area in the greenhouse is visualized in the greenhouse digital twin model;

[0114] S3 includes the following specific steps:

[0115] S31: Extract the stomatal conductance coefficient and photosynthetic efficiency coefficient of crops in the pest monitoring area;

[0116] S32: Importing the stomatal conductance coefficient and the photosynthetic efficiency coefficient into a first leaf health threat value calculation strategy to calculate and obtain a first leaf health threat value, wherein the first leaf health threat value calculation strategy is specifically:

[0117]

[0118] Among them, wx1 is the health threat value of the first leaf;

[0119] S33: Preset a first leaf health threshold and conduct a preliminary assessment of the degree of impact of insect pests on crops in the insect pest monitoring area;

[0120] When the first leaf health threat value in the pest monitoring area is greater than the first leaf health threshold, it is assessed that the crops in the pest monitoring area are in a healthy state;

[0121] When the first leaf health threat value of the pest monitoring area is less than or equal to the first leaf health threshold, the first leaf health threat value of each pest monitoring area in the greenhouse is visualized in the greenhouse digital twin model, a suspected pest warning is issued to the greenhouse management platform, and step S4 is executed synchronously.

[0122] S4: Real-time collection of leaf surface growth images and leaf surface temperature images of crops in each pest monitoring area;

[0123] S4 includes the following specific steps:

[0124] S41: collecting the leaf surface growth images of crops in each pest monitoring area in real time, wherein the leaf surface growth images are collected by a camera device;

[0125] S42: Importing the leaf surface growth image into the insect population density factor calculation strategy, the insect population density factor calculation strategy is as follows:

[0126]

[0127] Among them, e 1,n Indicates the insect population density factor at the nth horizontal altitude layer;

[0128] r is a subscript, indicating the i-th level in the n-th horizontal altitude layer n On the crop plant, the rth leaf; Indicates the i n Total number of leaves per crop plant;

[0129] It represents the actual distance between the rth leaf and the bottom of the crop root;

[0130] H represents the i n Total root height of each crop plant;

[0131] represents the number of pixel blocks in the insect mouth area on the leaf surface growth image of the rth leaf;

[0132] NU r Represents the total number of pixel blocks in the leaf area on the leaf surface growth image of the rth leaf;

[0133] Indicates the i n Crops, The average number of pixel blocks per leaf.

[0134] S43: collecting leaf surface temperature images of crops in each pest monitoring area in real time, wherein the leaf surface temperature images are collected by infrared equipment;

[0135] S44: Importing the blade surface temperature image into a temperature distortion factor calculation strategy, wherein the temperature distortion factor calculation strategy is as follows:

[0136]

[0137] Among them, e 2,n represents the temperature distortion factor of the nth horizontal layer;

[0138] It represents the maximum value of pixels in the temperature anomaly area on the leaf surface temperature image of the rth leaf;

[0139] represents the minimum pixel value of the temperature anomaly area on the leaf surface temperature image of the rth leaf;

[0140] Indicates the i n Crops, The pixel average value of the temperature abnormal area of ​​each leaf.

[0141] S5: Calculate the second leaf health threat value according to the leaf surface growth image and the leaf surface temperature image, make a comprehensive judgment on the degree of impact of pests in the pest monitoring area according to the second leaf health threat value, and visualize the second leaf health threat values ​​of each pest monitoring area in the greenhouse into the greenhouse digital twin model.

[0142] S5 includes the following specific steps:

[0143] S51: Calculate a second leaf health threat value according to the leaf surface growth image and the leaf surface temperature image, and the specific calculation formula is:

[0144]

[0145] Wherein, wx2 is the health threat value of the second leaf.

[0146] S5 also includes the following specific steps:

[0147] S52: Preset a second leaf health threshold, and make a comprehensive judgment on the degree of impact of insect pests in the insect pest monitoring area according to the second leaf health threat value, including:

[0148] When the second leaf health threat value is less than or equal to the second leaf health threshold, repeat steps S2-S3, recalculate and obtain the first leaf health threat value, and perform a preliminary assessment of the degree of impact of insect pests on crops in the insect pest monitoring area again;

[0149] When the second blade health threat value is greater than the second blade health threshold, executing step S53;

[0150] S53: Visualize the first leaf health threat value and the second leaf health threat value of each pest monitoring area in the greenhouse into the greenhouse digital twin model.

[0151] Preferably, a visualization strategy is as follows: the visualization includes: arranging the second leaf health threat values ​​of each pest monitoring area in descending order from large to small, recording the first category threat area as a red area, the second category threat area as a yellow area, the third category threat area as a green area, the fourth category threat area as a blue area... the sth category working area as gray... the Sth category working area as white;

[0152] The grayscale value of the threat area decreases as the type ordinal number increases, the grayscale value of the first type of working area is the largest, and the grayscale value of the S type of working area is the smallest.

[0153] The threat area division strategy includes: in the sequence, the division starts from the initial second leaf health threat value, and every two second leaf health threat values ​​constitute a type of threat area.

[0154] Embodiment 2:

[0155] like Figure 2 As shown, the greenhouse crop pest and disease integrated management system based on big data in the embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0156] Monitoring area division module, laser scattering measurement module, fluorescence parameter measurement module, first leaf health threat assessment module, second leaf health threat assessment module, pest comprehensive judgment module and visualization module;

[0157] The monitoring area division module is used to model the digital twin model of the greenhouse and divide the greenhouse into pest monitoring areas according to the growth rate of each crop in the greenhouse;

[0158] The laser scattering measurement module is used to perform laser scattering tests on crops in the pest monitoring area to measure and obtain the stomatal conductance coefficient of the crops.

[0159] The fluorescence parameter measurement module is used to measure the fluorescence parameters of crops in the pest monitoring area;

[0160] The first blade health threat assessment module is used to calculate and obtain a first blade health threat value;

[0161] The second leaf health threat assessment module is used to collect leaf surface growth images and leaf surface temperature images of crops in each pest monitoring area in real time, and calculate the second leaf health threat value according to the leaf surface growth images and the leaf surface temperature images;

[0162] The pest comprehensive judgment module makes a comprehensive judgment on the pest impact degree of the pest monitoring area according to the second leaf health threat value;

[0163] The visualization module is used to visualize the first leaf health threat value and the second leaf health threat value of each pest monitoring area in the greenhouse into the greenhouse digital twin model.

[0164] Embodiment three:

[0165] 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;

[0166] The processor executes the above-mentioned big data-based integrated monitoring method for greenhouse crop diseases and pests by calling the computer program stored in the memory.

[0167] 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 at least one computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the greenhouse crop pest and disease integrated monitoring method based on big data provided by the above method embodiment. The electronic device may also include other components for implementing 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, so as to input and output data. This embodiment will not be described in detail here.

[0168] Embodiment 4:

[0169] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0170] When the computer program runs on a computer device, the computer device executes the above-mentioned integrated monitoring method for greenhouse crop diseases and insect pests based on big data.

[0171] 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.

[0172] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0173] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0174] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0175] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0176] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0177] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0178] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0179] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0180] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means 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 representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0181] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0182] 1. The present invention simultaneously adopts the methods of laser scattering test and fluorescence parameter measurement to comprehensively obtain the stomatal conductance coefficient and fluorescence characteristics of crops, which effectively improves the multidimensionality and accuracy of the data and provides a reliable basis for subsequent crop health threat assessment.

[0183] 2. The present invention collects leaf surface growth images and temperature images in real time and analyzes the images, so that the monitoring information of pests and diseases can be updated in time, ensuring a dynamic grasp of the health status of crops and improving the response speed of the early warning mechanism.

[0184] 3. By calculating the health threat value of the first leaf and the second leaf, the present invention can not only realize timely judgment of the impact of pests, but also actively issue an alarm after discovering potential threats, take prevention and control measures in time, and enhance the scientificity and effectiveness of agricultural production. In addition, the present invention also visualizes the monitoring results into the digital twin model, so that managers can more intuitively understand the health threat situation of each pest monitoring area, facilitate quick management decisions, and reduce the impact of pests and diseases on crops.

[0185] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A comprehensive monitoring method for greenhouse crop pests and diseases based on big data, characterized by: The method comprises the following specific steps: S1: Model the digital twin of the greenhouse and divide the greenhouse into pest monitoring areas according to the growth rate of each crop in the greenhouse; S2: Conduct laser scattering tests on crops in the pest monitoring area, measure the stomatal conductance coefficient of crops, and simultaneously measure the fluorescence parameters of crops in the pest monitoring area; S3: According to S2, the first leaf health threat value is calculated and obtained, and the first leaf health threat value of each pest monitoring area in the greenhouse is visualized into the greenhouse digital twin model; S4: Real-time collection of leaf surface growth images and leaf surface temperature images of crops in each pest monitoring area; S5: Calculate the second leaf health threat value according to the leaf surface growth image and the leaf surface temperature image, make a comprehensive judgment on the degree of impact of pests in the pest monitoring area according to the second leaf health threat value, and visualize the second leaf health threat values ​​of each pest monitoring area in the greenhouse into the greenhouse digital twin model.

2. The method for comprehensive monitoring of greenhouse crop diseases and insect pests based on big data according to claim 1, characterized in that: In S2, the laser scattering test includes: S21: raising the plastic isolation cloths on both sides of a single pest monitoring area to construct an airflow channel, and collecting height data of crop leaf growth in the pest monitoring area, and dividing the crop leaf growth area into N horizontal height layers according to the height data; S22: installing the laser transmitter on the left side of the pest monitoring area and performing horizontal adjustment of the laser transmitter to ensure that the laser beam can evenly pass through the gas environment around the crop leaves, wherein the horizontal adjustment includes: before each laser scattering test, adjusting the laser transmitter to be consistent with the horizontal height layer where the growth height layer corresponding to the crop leaves is located; S23: Place a laser signal detector on the right side of the pest monitoring area, adjust the horizontal height of the receiver to be consistent with the horizontal height layer of the laser transmitter, and receive the scattered signal through the receiver; S24: Start the laser transmitter, perform laser scattering tests at N horizontal height layers respectively, record the scattering signals, and calculate the stomatal conductance coefficient of the crops according to the scattering signals.

3. The method for comprehensive monitoring of greenhouse crop diseases and insect pests based on big data according to claim 2, characterized in that: The calculation strategy of the stomatal conductance coefficient of the crops is as follows: Among them, gd n It represents the stomatal conductance coefficient of crops in the nth horizontal layer; η n is the air velocity coefficient in the nth horizontal altitude layer; A is the standard surface stomatal area of ​​crops in the pest monitoring area; C n is the gas concentration value in the nth horizontal altitude layer, C=k×Q, k is the conversion ratio coefficient, and Q is the scattered light intensity received by the receiver; It represents the rate of change of gas concentration in the nth horizontal altitude layer within a monitoring period t.

4. The method for comprehensive monitoring of greenhouse crop diseases and insect pests based on big data according to claim 3, characterized in that: In S2, measuring the fluorescence parameters of crops in the pest monitoring area includes the following specific steps: S25: selecting three leaves with the largest leaf areas at each horizontal height layer of each crop plant to form a set of leaves to be tested for each crop plant; S26: Simultaneously, using a pulse amplitude modulation fluorescence instrument, measuring fluorescence parameters of the set of leaves to be tested of each crop, wherein the fluorescence parameters include: an initial fluorescence parameter F0 and a maximum fluorescence parameter Fm; S27: Calculate the photosynthetic efficiency coefficient of crops based on the initial fluorescence parameters and the maximum fluorescence parameters, specifically: Among them, i n is a subscript, indicating the i-th level in the n-th horizontal altitude layer n crops; there are a total of I n Plants of crops; Indicates that in the nth horizontal layer, the i-th n The maximum value of the maximum fluorescence parameter in the set of leaves to be tested of the crop; It represents the average value of the maximum fluorescence parameters of crops in the nth horizontal altitude layer.

5. The method for comprehensive monitoring of greenhouse crop diseases and insect pests based on big data according to claim 4, characterized in that: S3 includes the following specific steps: S31: Extract the stomatal conductance coefficient and photosynthetic efficiency coefficient of crops in the pest monitoring area; S32: Importing the stomatal conductance coefficient and the photosynthetic efficiency coefficient into a first leaf health threat value calculation strategy to calculate and obtain a first leaf health threat value, wherein the first leaf health threat value calculation strategy is specifically: Among them, wx1 is the health threat value of the first leaf; S33: Preset a first leaf health threshold and conduct a preliminary assessment of the degree of impact of insect pests on crops in the insect pest monitoring area; When the first leaf health threat value in the pest monitoring area is greater than the first leaf health threshold, it is assessed that the crops in the pest monitoring area are in a healthy state; When the first leaf health threat value of the pest monitoring area is less than or equal to the first leaf health threshold, the first leaf health threat value of each pest monitoring area in the greenhouse is visualized in the greenhouse digital twin model, a suspected pest warning is issued to the greenhouse management platform, and step S4 is executed synchronously.

6. The method for comprehensive monitoring of greenhouse crop diseases and insect pests based on big data according to claim 5, characterized in that: S4 includes the following specific steps: S41: collecting the leaf surface growth images of crops in each pest monitoring area in real time, wherein the leaf surface growth images are collected by a camera device; S42: Importing the leaf surface growth image into the insect population density factor calculation strategy, the insect population density factor calculation strategy is as follows: Among them, e 1,n Indicates the insect population density factor at the nth horizontal altitude layer; r is a subscript, indicating the i-th level in the n-th horizontal altitude layer n On the crop plant, the rth leaf; Indicates the i n Total number of leaves per crop plant; It represents the actual distance between the rth leaf and the bottom of the crop root; H represents the i n Total root height of each crop plant; represents the number of pixel blocks in the insect mouth area on the leaf surface growth image of the rth leaf; NU r Represents the total number of pixel blocks in the leaf area on the leaf surface growth image of the rth leaf; Indicates the i n Crops, The average number of pixel blocks per leaf; S43: collecting leaf surface temperature images of crops in each pest monitoring area in real time, wherein the leaf surface temperature images are collected by infrared equipment; S44: Importing the blade surface temperature image into a temperature distortion factor calculation strategy, wherein the temperature distortion factor calculation strategy is as follows: Among them, e 2,n represents the temperature distortion factor of the nth horizontal layer; It represents the maximum value of pixels in the temperature anomaly area on the leaf surface temperature image of the rth leaf; represents the minimum pixel value of the temperature anomaly area on the leaf surface temperature image of the rth leaf; Indicates the i n Crops, The pixel average value of the temperature abnormal area of ​​each leaf.

7. The method for comprehensive monitoring of greenhouse crop diseases and insect pests based on big data according to claim 6, characterized in that: S5 includes the following specific steps: S51: Calculate a second leaf health threat value according to the leaf surface growth image and the leaf surface temperature image, and the specific calculation formula is: Wherein, wx2 is the health threat value of the second leaf.

8. The method for comprehensive monitoring of greenhouse crop diseases and insect pests based on big data according to claim 7, characterized in that: S5 also includes the following specific steps: S52: Preset a second leaf health threshold, and make a comprehensive judgment on the degree of impact of insect pests in the insect pest monitoring area according to the second leaf health threat value, including: When the second leaf health threat value is less than or equal to the second leaf health threshold, repeat steps S2-S3, recalculate and obtain the first leaf health threat value, and perform a preliminary assessment of the degree of pest impact on crops in the pest monitoring area again; When the second blade health threat value is greater than the second blade health threshold, executing step S53; S53: Visualize the first leaf health threat value and the second leaf health threat value of each pest monitoring area in the greenhouse into the greenhouse digital twin model.

9. A greenhouse crop pest and disease integrated management system based on big data, which is implemented based on the greenhouse crop pest and disease integrated monitoring method based on big data as claimed in any one of claims 1 to 8, characterized in that: The system includes the following modules: Monitoring area division module, laser scattering measurement module, fluorescence parameter measurement module, first leaf health threat assessment module, second leaf health threat assessment module, pest comprehensive judgment module and visualization module; The monitoring area division module is used to model the digital twin model of the greenhouse and divide the greenhouse into pest monitoring areas according to the growth rate of each crop in the greenhouse; The laser scattering measurement module is used to perform laser scattering tests on crops in the pest monitoring area to measure and obtain the stomatal conductance coefficient of the crops. The fluorescence parameter measurement module is used to measure the fluorescence parameters of crops in the pest monitoring area; The first blade health threat assessment module is used to calculate and obtain a first blade health threat value; The second leaf health threat assessment module is used to collect leaf surface growth images and leaf surface temperature images of crops in each pest monitoring area in real time, and calculate the second leaf health threat value according to the leaf surface growth images and the leaf surface temperature images; The pest comprehensive judgment module makes a comprehensive judgment on the pest impact degree of the pest monitoring area according to the second leaf health threat value; The visualization module is used to visualize the first leaf health threat value and the second leaf health threat value of each pest monitoring area in the greenhouse into the greenhouse digital twin model.

Images