A method for monitoring tomato brown rugose fruit virus in combination with a sensor network node

Abstract

The application discloses a kind of tomato brown wrinkle fruit virus monitoring method combined with sensor network node, it is related to tomato virus monitoring technical field, to solve the problem of the accuracy reduction of virus infection risk identification, by the continuous collection of the electrical conductivity of the leaves of vegetable base plant and the calculation variation trend, judge whether to enter regional evaluation mechanism, vegetable base is divided into multiple regions, obtain the abnormal evaluation characteristics of the butyric acid vapor content and the fruit surface roughness of each region, screening target area, target area plant obtains fruit tissue by aseptic sampling, determines the ion leakage of epidermis, and after grinding, centrifugation to the remaining tissue, determines the virus protein concentration using fluorescently labeled immunoassay method, calculates the cell membrane damage index based on ion leakage, combined with virus protein concentration generates virus infection coefficient, quantitatively evaluates the virus infection state, realizes multidimensional monitoring from physiological indicators to virus accumulation, improves the accuracy of virus infection risk identification.

Description

Technical Field

[0001] This invention relates to the field of tomato virus monitoring technology, and more specifically, to a method for monitoring tomato brown wrinkled fruit virus by combining sensor network nodes. Background Technology

[0002] Tomato brown wrinkled fruit virus is a novel and devastating viral pathogen. It spreads rapidly and has a strong infectivity, causing deformed leaves and brown wrinkled patches on the surface of fruits, which seriously affects the marketability and yield of fruits. Existing technologies such as enzyme-linked immunosorbent assay (ELISA) and real-time quantitative PCR are used to detect the virus. In recent years, sensor technology and the Internet of Things have been gradually applied to agricultural disease monitoring. Some studies have attempted to use conductivity sensing, gas sensing or image acquisition technology to analyze the physiological state and appearance characteristics of plants.

[0003] The existing technology has the following shortcomings:

[0004] Currently, existing technologies mainly rely on single physiological or molecular detection indicators for virus monitoring, lacking multi-dimensional integrated monitoring methods that integrate plant physiological signals, volatile substance indicators, and tissue molecular levels. This makes it impossible to achieve early, regional, and dynamic virus infection assessment in large-scale vegetable bases, resulting in reduced accuracy in identifying virus infection risks and insufficient scientific basis for prevention and control decisions. Therefore, a method for monitoring tomato brown wrinkled fruit virus by combining sensor network nodes is proposed.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for monitoring tomato brown wrinkled fruit virus by combining sensor network nodes. This method utilizes a multi-dimensional fusion algorithm based on continuous acquisition of leaf conductivity, detection of butyric acid vapor and fruit surface roughness by regional division, and analysis of tissue sample ion leakage and viral protein concentration to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring tomato brown wrinkled fruit virus by combining sensor network nodes, comprising the following steps:

[0008] Step S1: Test the plants in the vegetable base to be tested, detect the leaf conductivity of the plants, calculate the conductivity change trend based on the leaf conductivity, and determine whether to enter the regional evaluation mechanism based on the conductivity change trend.

[0009] Step S2: When entering the regional assessment mechanism, the vegetable base to be tested is divided into regions, and the butyric acid vapor content and fruit surface roughness of each region are obtained. The abnormal assessment characteristics of each region are generated by combining the butyric acid vapor content and fruit surface roughness.

[0010] Step S3: Based on the abnormal assessment characteristics, the target area is obtained by screening and dividing the region. Tissue samples are taken from the plants in the target area to detect the amount of plant epidermal ion leakage and viral protein concentration in the tissue samples.

[0011] Step S4: Calculate the cell membrane damage index based on the amount of ion leakage from the plant epidermis, generate the virus infection coefficient of the target area by combining it with the viral protein concentration, and assess the virus infection status of the target area based on the virus infection coefficient.

[0012] In a preferred embodiment, in step S1, plants are randomly selected from the vegetable base to be tested as plant samples.

[0013] Leaf conductivity of plant samples was collected using a conductivity acquisition device.

[0014] A preset collection period is used to collect leaf conductivity data from the same plant sample at the beginning and end of the preset collection period. The difference between the leaf conductivity data at the end of the preset collection period and the beginning of the preset collection period is used to obtain the change in leaf conductivity.

[0015] The ratio of the change in blade conductivity to the preset acquisition period is used as the trend of conductivity change.

[0016] In a preferred embodiment, in step S1, the range of blade conductivity variation trend is retrieved from the historical database and compared with the conductivity variation trend;

[0017] The total number of plant samples whose electrical conductivity trend is higher than the upper limit of the electrical conductivity trend range is considered as the number of abnormal plant samples.

[0018] The ratio of the number of abnormal plant samples to the total number of plant samples is used as the abnormal sample ratio.

[0019] If the proportion of abnormal samples is greater than the preset abnormal proportion threshold, the region assessment mechanism will be activated.

[0020] Conversely, if the condition is not met, the region will not be included in the regional assessment mechanism.

[0021] In a preferred embodiment, in step S2, when entering the regional assessment mechanism, the vegetable base to be tested is divided into regions;

[0022] Multiple detection nodes are evenly distributed within the defined area, and the preset detection time period is divided into multiple detection times.

[0023] At each detection time, the air at the detection node is monitored by a butyric acid selective gas sensor deployed at the detection node, and the butyric acid vapor concentration signal is output. The digital processing unit processes the butyric acid vapor concentration signal to obtain the butyric acid vapor concentration.

[0024] The butyric acid vapor concentration at each detection time is accumulated within the preset detection period and divided by the total number of detection times to obtain the butyric acid vapor content at the detection node.

[0025] In a preferred embodiment, in step S2, the surface roughness of the fruit at the detection node is obtained by the image acquisition device of the detection node;

[0026] The butyric acid vapor content and fruit surface roughness at the detection nodes were standardized to obtain the butyric acid vapor content coefficient and the fruit surface roughness coefficient, respectively.

[0027] The product of butyric acid vapor content coefficient and fruit surface roughness coefficient is used as the node evaluation index;

[0028] Detection nodes whose node evaluation index is greater than the preset node evaluation threshold are designated as high-value detection nodes.

[0029] The ratio of the total number of high-value detection nodes to the total number of detection nodes within the statistically defined area is used as the regional anomaly assessment index.

[0030] In a preferred embodiment, in step S3, when the anomaly assessment feature is greater than or equal to a preset anomaly assessment threshold, the divided region is marked as the target region.

[0031] Conversely, the divided regions are not marked;

[0032] Sap samples were taken from plants within the target area, and local tissue samples of the fruit were cut using a sterile blade.

[0033] A fixed volume of tissue sample is placed in a known volume of deionized water buffer solution and immersed for a specified time under constant temperature conditions, so that ions in the epidermal cells are released into the solution.

[0034] The conductivity of the solution is measured using a conductivity sensor to determine the amount of ion leakage from the plant epidermis.

[0035] In a preferred embodiment, in step S3, the remaining tissue sample is placed in a grinding tube under sterile conditions, a buffer solution to maintain the pH stability of the tissue fluid is added, the tissue is broken up by mechanical grinding and clarified, and the supernatant is collected as the juice sample to be tested.

[0036] The juice sample to be tested was placed in a reaction vessel, and the viral protein concentration of the juice sample was determined by fluorescent labeling immunoassay.

[0037] In a preferred embodiment, in step S4, the average value of plant epidermal ion leakage in all target areas is calculated as the baseline ion leakage.

[0038] The conductivity value corresponding to all ions released by the plant when the cell membrane is completely damaged is taken as the maximum ion leakage.

[0039] By combining baseline and maximum ion leakage, the cell membrane damage index of the target area is calculated by normalizing the ion leakage of plant epidermis in the target area.

[0040] In a preferred embodiment, in step S4, the viral protein concentration is standardized to obtain a viral protein concentration factor, which is then combined with the cell membrane damage index to generate the viral infection coefficient of the target area.

[0041] When the virus infection coefficient is greater than or equal to the preset status judgment threshold, the virus infection status of the target area is judged to be severe infection.

[0042] When the viral infection coefficient is less than the preset status determination threshold, the viral infection status of the target area is determined to be mild infection.

[0043] The technical effects and advantages of this invention are as follows:

[0044] This invention continuously collects and calculates the leaf conductivity trends of plants in a test vegetable base to determine whether they should enter a regional assessment mechanism. The mechanism divides the vegetable base into multiple regions and obtains abnormal assessment characteristics by acquiring butyric acid vapor content and fruit surface roughness in each region to screen target areas. Within the target area, fruit tissue samples are obtained through aseptic sampling to determine epidermal ion leakage. Simultaneously, the remaining tissue is ground, centrifuged, and viral protein concentration is determined using a fluorescently labeled immunoassay. Based on the plant epidermal ion leakage, a cell membrane damage index is calculated, and combined with the viral protein concentration factor, a viral infection coefficient is generated to quantitatively assess the viral infection status of the target area. This achieves multi-dimensional monitoring from plant physiological indicators to the degree of viral accumulation, improving the accuracy of viral infection risk identification, objectively and scientifically identifying the viral infection risk of target areas, and providing a reliable basis for prevention and control. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the implementation of a tomato brown wrinkled fruit virus monitoring method combining sensor network nodes according to the present invention.

[0046] Figure 2 This is a schematic diagram illustrating the steps of a tomato brown wrinkled fruit virus monitoring method combining sensor network nodes according to the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] This invention continuously collects and calculates the leaf conductivity trends of plants in a test vegetable base to determine whether they should enter a regional assessment mechanism. The mechanism divides the vegetable base into multiple regions and obtains abnormal assessment characteristics by acquiring butyric acid vapor content and fruit surface roughness in each region to screen target areas. Within the target area, fruit tissue samples are obtained through aseptic sampling to determine epidermal ion leakage. Simultaneously, the remaining tissue is ground, centrifuged, and viral protein concentration is determined using a fluorescently labeled immunoassay. Based on the plant epidermal ion leakage, a cell membrane damage index is calculated, and combined with the viral protein concentration factor, a viral infection coefficient is generated to quantitatively assess the viral infection status of the target area, achieving multi-dimensional monitoring from plant physiological indicators to the degree of viral accumulation.

[0049] Example 1

[0050] Please see Figures 1 to 2 A method for monitoring tomato brown wrinkled fruit virus by combining sensor network nodes includes the following steps:

[0051] Step S1: Test the plants in the vegetable base to be tested, detect the leaf conductivity of the plants, calculate the conductivity change trend based on the leaf conductivity, and determine whether to enter the regional evaluation mechanism based on the conductivity change trend.

[0052] Step S2: When entering the regional assessment mechanism, the vegetable base to be tested is divided into regions, and the butyric acid vapor content and fruit surface roughness of each region are obtained. The abnormal assessment characteristics of each region are generated by combining the butyric acid vapor content and fruit surface roughness.

[0053] Step S3: Based on the abnormal assessment characteristics, the target area is obtained by screening and dividing the region. Tissue samples are taken from the plants in the target area to detect the amount of plant epidermal ion leakage and viral protein concentration in the tissue samples.

[0054] Step S4: Calculate the cell membrane damage index based on the amount of ion leakage from the plant epidermis, generate the virus infection coefficient of the target area by combining it with the viral protein concentration, and assess the virus infection status of the target area based on the virus infection coefficient.

[0055] The specific implementation is as follows:

[0056] In step S1, plants are randomly selected from the vegetable base to be tested as plant samples.

[0057] Among them, plant samples are ensured to be evenly distributed in the vegetable base to be tested;

[0058] The conductivity of plant samples was collected using a conductivity acquisition device. During the detection process, functional leaves of the plant samples were selected as the detection objects, preferably leaves that were close to the upper part of the stem, of moderate age, and undamaged. The two ends of the electrode clamping assembly were clamped on both sides of the leaf to ensure that the electrodes were in full contact with the leaf surface and that the contact area was consistent, so as to reduce the contact resistance deviation. After clamping, the constant voltage signal generation circuit applied a constant voltage signal to both ends of the leaf. The conductivity detection circuit collected the current response signal passing through the leaf tissue in real time, and processed the current response signal to obtain a digital current value. The leaf conductivity was obtained by comparing the digital current value with the applied constant voltage signal.

[0059] Leaf conductivity refers to the ability of leaf tissue to conduct current under the influence of an electric field. The value of leaf conductivity is closely related to the permeability of leaf cell membrane and the distribution of ions inside and outside the cell. Under normal physiological conditions, the leaf cell membrane structure of plant samples is intact, the membrane has a strong barrier effect on ions, and the leaf conductivity is at a stable baseline level. When the plant is infected by a virus, the permeability of the cell membrane changes and the leaf conductivity increases. When the leaf conductivity shows an increasing trend, it indicates that there may be cell membrane damage.

[0060] A preset collection period is used to collect leaf conductivity data from the same plant sample at the beginning and end of the preset collection period. The difference between the leaf conductivity data at the end of the preset collection period and the beginning of the preset collection period is used to obtain the change in leaf conductivity.

[0061] The ratio of the change in blade conductivity to the preset acquisition period is used as the trend of conductivity change.

[0062] Retrieve the leaf conductivity change trend range from the historical database that matches the current environmental conditions, crop varieties, and phenological stages of the vegetable base to be tested. Count the total number of plant samples whose conductivity change trend is higher than the upper limit of the conductivity change trend range as the number of abnormal plant samples.

[0063] The ratio of the number of abnormal plant samples to the total number of plant samples is used as the abnormal sample ratio.

[0064] A preset anomaly rate threshold is compared with the anomaly sample rate to determine whether to proceed with the regional assessment mechanism.

[0065] If the proportion of abnormal samples is greater than the preset abnormal proportion threshold, the region assessment mechanism will be activated.

[0066] Conversely, it is determined that the region will not be included in the regional assessment mechanism;

[0067] When the proportion of abnormal samples exceeds the preset abnormality threshold, it indicates that there may be regional viral infection in the vegetable base to be tested, and further evaluation is needed through butyric acid vapor content and fruit surface roughness, etc.

[0068] It should be explained that the conductivity acquisition device is a device used to detect the leaf conductivity of plant samples, including an electrode clamping assembly, a constant voltage signal generation circuit, and a conductivity detection circuit; the preset acquisition period is the time period used to collect leaf conductivity, which can be set according to the daily variation pattern of crops and the time distribution of transpiration peaks; the historical database is a collection of long-term monitoring data of the vegetable base to be tested, in which the conductivity change trend range is obtained by statistically analyzing the conductivity change trend distribution of historical normal plant samples; the preset abnormality ratio threshold is a critical value used to determine whether there is an abnormality in the overall physiological state of the vegetable base to be tested, which can be set according to historical data statistics, for example, by selecting the mean and standard deviation of the abnormal sample ratios under multiple past normal states to obtain the preset abnormality ratio threshold.

[0069] In step S2, when entering the regional assessment mechanism, the vegetable base to be tested is divided into regions. Multiple regions can be divided into regular grids and natural boundaries based on the geographical location of the vegetable base to be tested.

[0070] For example, when the vegetable base to be tested is a regular rectangular field, it can be divided into several rectangular grids at equal intervals in the east-west and north-south directions, with each grid serving as a division area;

[0071] Multiple detection nodes are evenly distributed within the defined area. The detection period is preset and divided into multiple detection times. At each detection time, the air at the detection node is monitored by the butyric acid selective gas sensor and the butyric acid vapor concentration signal is output.

[0072] The digital processing unit processes the butyric acid vapor concentration signal to obtain the butyric acid vapor concentration. Within a preset detection period, the butyric acid vapor concentration at each detection time is accumulated and divided by the total number of detection times to obtain the butyric acid vapor content at the detection node.

[0073] The image acquisition device at the detection node projects grating stripes onto the plant surface. The image acquisition unit of the image acquisition device acquires the intensity distribution and phase shift information of the reflected light, forming a two-dimensional image and depth information of the plant fruit. The two-dimensional image of the plant fruit generates a height point cloud on the surface of the plant fruit through the three-dimensional reconstruction algorithm module and calculates the surface roughness of the fruit at the detection node.

[0074] It should be noted that the butyric acid selective gas sensor is a gas sensing device used to detect the concentration of butyric acid vapor in the air, including a gas sampling unit, a signal conditioning circuit, and a digital processing unit; the preset detection period is the time period for collecting butyric acid vapor concentration and fruit surface image data, which can be set according to ambient temperature, sunshine duration, etc.; the image acquisition device is a detection device used to acquire three-dimensional morphological information of the plant fruit surface, including a structured light sensor, an image acquisition unit, and a three-dimensional reconstruction algorithm module, used to collect fruit surface roughness.

[0075] The butyric acid vapor content and fruit surface roughness at the detection nodes were standardized to obtain the butyric acid vapor content coefficient and the fruit surface roughness coefficient, respectively.

[0076] The product of butyric acid vapor content coefficient and fruit surface roughness coefficient is used as the node evaluation index;

[0077] The preset node evaluation threshold is compared with the node evaluation index to determine whether the detected node is abnormal. Detected nodes whose node evaluation index is greater than the preset node evaluation threshold are regarded as high-value detected nodes.

[0078] The ratio of the total number of high-value detection nodes to the total number of detection nodes within the statistically defined area is used as the regional anomaly assessment index.

[0079] The higher the butyric acid vapor content, the greater the total amount of volatile organic acids released by the fruits and leaves of the plants in the designated area, reflecting that the plants are in a state of enhanced cell membrane permeability. The higher the fruit surface roughness coefficient, the greater the microscopic height fluctuation of the fruit epidermis, the more uneven the surface, and the existence of disordered epidermal cell arrangement.

[0080] It should be noted that standardization refers to the process of mapping raw data of different physical quantities or different dimensions to a uniform dimension, uniform numerical range or uniform statistical distribution through specific mathematical transformations. Standardization methods include, but are not limited to, standard linear transformation based on interval scaling, Z-Score standardization based on statistics or normalization based on nonlinear mapping functions. The application methods of standardization will not be elaborated here. The preset node evaluation threshold is a critical value used to identify whether a single detection node has an abnormal state. It can be set according to historical data. For example, the node evaluation index distribution of plants of the same variety and the same phenological stage in a healthy state can be retrieved from the historical database, and the 90th percentile of the distribution can be taken as the preset node evaluation threshold.

[0081] In step S3, the segmented regions are filtered based on anomaly assessment features, and the anomaly assessment features are compared with preset anomaly assessment thresholds:

[0082] When the anomaly assessment feature is greater than or equal to the preset anomaly assessment threshold, the divided region is marked as the target region.

[0083] Conversely, the divided regions are not marked.

[0084] It should be noted that the anomaly assessment threshold is a reference value used to distinguish whether there are potential viral anomalies in the divided areas. By measuring the butyric acid vapor content and fruit surface roughness of multiple vegetable bases at different times and different growth stages, the anomaly assessment characteristics of each area are calculated. Statistical analysis is used to determine the quantiles, mean, and standard deviation of the characteristic values. Based on this, the characteristic values ​​that can effectively distinguish between high-risk and low-risk areas are selected as the anomaly assessment threshold.

[0085] Tissue sampling was performed on plants within the target area. Local tissue samples of the fruit were cut using a sterile blade. The selection of sampling sites followed the principle of uniformity, that is, plants within the same target area were sampled at the same growth stage and at the same location, and the quality of the cut tissue was kept within the specified range to eliminate data deviations caused by differences in location.

[0086] A fixed volume of tissue sample is placed in a known volume of deionized water buffer solution and immersed for a specified time under constant temperature conditions, so that ions in the epidermal cells are released into the solution.

[0087] As the viral infection progresses, the cell membrane structure is damaged, leading to more ions leaking into the external solution, which in turn increases the solution conductivity. During the detection process, the conductivity of the solution is measured using a conductivity sensor, and the result is taken as the amount of ion leakage from the plant epidermis.

[0088] It should be noted that a conductivity sensor is a sensing device used to measure the concentration of electrolyte ions in a liquid solution. Its basic principle is to utilize the correspondence between the conductivity of a solution and the concentration of ions contained therein, and to realize the real-time detection of the conductivity of the solution by means of changes in current or voltage between electrodes.

[0089] The remaining tissue sample was placed in a sterile grinding tube, and a buffer solution to maintain the pH stability of the tissue fluid was added. The tissue was broken up by mechanical grinding, so that the cell contents were released and fully dissolved in the buffer solution to form a homogeneous juice sample.

[0090] Subsequently, the juice sample obtained from grinding was clarified by centrifuging the suspension in a centrifuge to separate cell debris and impurities, and the supernatant was collected as the juice sample to be tested.

[0091] The juice sample to be tested was placed in a reaction vessel, and an antibody that specifically binds to the tomato brown wrinkled fruit virus was introduced into the vessel, so that the viral protein in the juice sample could specifically bind to the antibody. Unbound components were removed by washing, and an enzyme-labeled secondary antibody that could bind to the viral protein complex was further introduced. The concentration of viral protein in the juice sample was determined by fluorescent labeling immunoassay.

[0092] It should be noted that a centrifuge is an experimental device that uses the centrifugal force generated by high-speed rotation to separate components of different densities or particle sizes in a mixture; enzyme-labeled secondary antibody refers to an antibody molecule that is used as a secondary antibody in immunoassay methods and is modified by enzyme labeling. By labeling enzyme molecules on the secondary antibody molecule, a colorimetric or luminescent reaction occurs when the corresponding substrate is added, thereby amplifying the signal and realizing the indirect detection of the target antigen; fluorescently labeled immunoassay is an immunological detection method that uses fluorescently labeled antibodies to specifically bind to the target antigen and quantifies the antigen concentration by measuring the intensity of the fluorescence signal.

[0093] In step S4, the degree of cell membrane damage in the target area is quantitatively evaluated using the plant epidermal ion leakage rate. The average value of plant epidermal ion leakage rates in all target areas is calculated as the baseline ion leakage rate. Combined with the maximum ion leakage rate, the plant epidermal ion leakage rate in the target area is normalized to obtain the cell membrane damage index of the target area. The specific calculation formula is as follows:

[0094] ;

[0095] in, The cell membrane damage index. This represents the amount of ion leakage from the plant epidermis. This represents the baseline ion leakage rate. This represents the maximum ion leakage rate.

[0096] The cell membrane damage index ranges from 0 to 1. The closer the value is to 1, the more severe the cell membrane damage, indicating that the viral infection has a more significant destructive effect on the plant cell membrane; a value close to 0 indicates that the plant cell membrane is intact.

[0097] It should be noted that the maximum ion leakage rate refers to the conductivity measurement value corresponding to all ions released by the plant under conditions of complete cell membrane rupture. It reflects the upper limit of ion release from the plant epidermal tissue under extreme damage conditions. By placing a sample of epidermal tissue from a healthy plant in a high-temperature water bath or adding a cell membrane disruptor (such as a surfactant or organic solvent), the structure of the plant epidermal cell membrane is completely ruptured, and the ions in the cell contents are completely released into the external solution. The conductivity of the solution is then measured using a conductivity sensor, and the result is taken as the maximum ion leakage rate.

[0098] The viral protein concentration was standardized to obtain a viral protein concentration factor, which, combined with the cell membrane damage index, generated the viral infection coefficient of the target region. The calculation formula is as follows:

[0099] ;

[0100] in, The viral infection coefficient, The viral protein concentration factor. The cell membrane damage index. and These are the weighting coefficients.

[0101] It should be noted that the virus infection coefficient reflects the degree of virus accumulation in the target area and the degree of damage to the plant cell membrane. The larger the value, the more severe the virus infection in the target area; the smaller the value, the milder the virus infection in the target area. The weighting coefficient is obtained by fitting historical experimental data and satisfies the constraint that the sum is 1.

[0102] Compare the virus infection coefficient with a preset state determination threshold:

[0103] When the virus infection coefficient is greater than or equal to the preset status judgment threshold, the virus infection status of the target area is judged to be severe infection.

[0104] When the viral infection coefficient is less than the preset status determination threshold, the viral infection status of the target area is determined to be mild infection.

[0105] It should be noted that the state determination threshold is a reference value used to judge the severity of viral infection in the target area. By measuring the viral protein concentration and the amount of ion leakage from the plant epidermis in plants in the target area with different infection levels, and combining the calculated viral infection coefficient, the distribution range of the viral infection coefficient is analyzed to determine the critical value that can effectively distinguish between severe and mild infection as the state determination threshold.

[0106] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0107] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0108] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0109] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0110] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring tomato brown wrinkled fruit virus by combining sensor network nodes, characterized in that: The method comprises the following steps: Step S1: detecting the plants in the vegetable base to be tested, detecting the leaf conductivity of the plants, calculating the conductivity change trend according to the leaf conductivity, and judging whether to enter the regional evaluation mechanism based on the conductivity change trend; Step S2: when entering the regional evaluation mechanism, the vegetable base to be tested is divided into regions, the butyric acid vapor content and the fruit surface roughness of each divided region are obtained, and the abnormal evaluation features of each divided region are generated by comprehensively considering the butyric acid vapor content and the fruit surface roughness; Step S3: according to the abnormal evaluation features, the divided region is screened to obtain a target region, and the plants in the target region are sampled, and the plant epidermis ion leakage amount and the virus protein concentration of the tissue sample are detected; In step S3, when the abnormal evaluation features are greater than or equal to a preset abnormal evaluation threshold, the divided region is marked as the target region; Otherwise, the divided region is not marked; The plants in the target region are sampled, and the local tissue of the fruit is cut as a tissue sample using a sterile blade; The tissue sample is taken in a fixed volume and placed in a known volume of deionized water buffer, and is soaked under constant temperature conditions for a specified time, so that the ions in the epidermal cells are released into the solution; The conductivity of the solution is measured by a conductivity sensor as the plant epidermis ion leakage amount; In step S3, the remaining tissue sample is placed in a grinding tube under sterile conditions, a buffer solution for maintaining the pH stability of the tissue fluid is added, the tissue is broken by mechanical grinding and clarification treatment, and the supernatant is collected as a juice sample to be tested; The juice sample to be tested is placed in a reaction container, and the fluorescence labeled immune detection method is used to determine the virus protein concentration of the juice sample to be tested; Step S4: based on the plant epidermis ion leakage amount, the cell membrane damage index is calculated, the virus infection coefficient of the target region is generated by combining the virus protein concentration, and the virus infection state of the target region is evaluated according to the virus infection coefficient; In step S4, the average value of the plant epidermis ion leakage amount of all target regions is calculated as the baseline ion leakage amount; The conductivity measurement value corresponding to the total ion released by the plant in the complete cell membrane damage state is taken as the maximum ion leakage amount; The plant epidermis ion leakage amount of the target region is normalized to obtain the cell membrane damage index of the target region by combining the baseline ion leakage amount and the maximum ion leakage amount.

2. The method according to claim 1, wherein: in step S1, a plant sample is randomly selected from the plants in the vegetable base to be tested; The leaf conductivity of the plant sample is collected by a conductivity collection device; A preset collection period is set, the leaf conductivity of the same plant sample is collected at the beginning and end of the preset collection period, and the difference between the leaf conductivities at the beginning and end of the preset collection period is taken as the leaf conductivity change amount; The ratio of the leaf conductivity change amount to the preset collection period is taken as the conductivity change trend.

3. The method according to claim 2, wherein: ​ In step S1, the leaf conductivity change trend interval in the historical database is compared with the conductivity change trend; The total number of plant samples with conductivity change trend higher than the upper limit of the conductivity change trend interval is counted as the number of abnormal plant samples; The ratio of the number of abnormal plant samples to the total number of plant samples is taken as the abnormal sample proportion; If the abnormal sample proportion is greater than the preset abnormal proportion threshold, it is determined to enter the regional evaluation mechanism; Otherwise, it is determined not to enter the regional evaluation mechanism.

4. The tomato brown rugose fruit virus monitoring method according to claim 1, characterized in that: In step S2, when the regional evaluation mechanism is entered, the vegetable base to be tested is divided into regions; Multiple detection nodes are uniformly arranged in the divided regions, and a preset detection period is divided into multiple detection time points; At each detection time point, the air around the detection node is monitored by the butyric acid selective gas sensor arranged on the detection node, and a butyric acid vapor concentration signal is output. The digital processing unit processes the butyric acid vapor concentration signal to obtain the butyric acid vapor concentration; The butyric acid vapor concentrations at each detection time point are accumulated within the preset detection period and divided by the total number of detection time points to obtain the butyric acid vapor content of the detection node.

5. The tomato brown rugose fruit virus monitoring method according to claim 4, characterized in that: In step S2, the fruit surface roughness of the detection node is obtained by the image acquisition device of the detection node; The butyric acid vapor content and the fruit surface roughness of the detection node are respectively standardized to obtain the butyric acid vapor content coefficient and the fruit surface roughness coefficient; The product of the butyric acid vapor content coefficient and the fruit surface roughness coefficient is taken as the node evaluation index; The detection nodes with node evaluation index greater than the preset node evaluation threshold are taken as high-value detection nodes; The ratio of the total number of high-value detection nodes in the planned regional area to the total number of detection nodes is taken as the regional abnormal evaluation index.

6. The tomato brown rugose fruit virus monitoring method according to claim 1, characterized in that: In step S4, the virus protein concentration is standardized to obtain a virus protein concentration factor, and the cell membrane damage index is combined to generate a virus infection coefficient of the target region; When the virus infection coefficient is greater than or equal to the preset state judgment threshold, it is determined that the virus infection state of the target region is severe infection; When the virus infection coefficient is less than the preset state judgment threshold, it is determined that the virus infection state of the target region is mild infection.

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