A method for detecting plant health index and sclerotinia disease index

Abstract

The present invention provides a method for detecting a plant health index and a sclerotinia disease incidence index, constructs a model of changes in the physiological capacitance, physiological capacitive reactance, physiological inductive reactance, physiological resistance and physiological impedance of plant leaves with clamping force, and uses the parameters of the above model to calculate the inherent physiological capacitive reactance, inherent physiological inductive reactance, inherent physiological resistance, inherent physiological impedance of the plant leaves, as well as the nutrient transfer rate and active nutrient transport capacity of the leaves, thereby obtaining the plant leaf metabolic flow and the plant leaf metabolic rate, and finally obtaining the plant relative metabolic activity. By comparing with the relative metabolic activity of healthy plants, the health status of the plant and the sclerotinia disease infection status are judged, the health index of the plant to be tested and the sclerotinia disease incidence index are detected, and the online and real-time monitoring of plant health and the early prediction of plant sclerotinia disease are realized, providing important technical support for plant production management, green and efficient prevention and control of crop sclerotinia disease and reduced use of pesticides.

Description

Technical Field

[0001] The invention belongs to agricultural engineering, plant protection and crop information detection, and specifically relates to a method for detecting a plant health index and a sclerotinia disease incidence index by using plant electrophysiological information. Background Art

[0002] The growth and development of plants and their entire life activities, such as nutrient absorption, material and energy metabolism, and signal transduction, involve the transmission and separation of protons and dielectric charges. Various changes in plant physiological processes, such as photosynthesis, respiration, transpiration, material flow, energy metabolism, and plant growth are directly or indirectly related to electrical signals. Leaves are the most important functional organs of plants, and are most sensitive to processes such as light energy utilization and energy metabolism. They play a vital role in the growth and development of plants. Therefore, the health status of plants can be learned from the electrophysiological information from plant leaves. Rapid, real-time, online, non-destructive, and dynamic quantification of plant health status lays the foundation for plant production management.

[0003] Rapeseed is an important oil crop in my country. Its planting area accounts for more than 30% of the world's planting area and more than 40% of the country's total oil crop area. Rapeseed oil production accounts for 40-50% of vegetable oil production, ranking first in the world. Sclerotinia is the most common and serious disease in the planting of crops such as rapeseed and radish in my country, seriously affecting the yield of crops such as rapeseed and radish and the quality of rapeseed oil. Rapeseed is susceptible to sclerotinia throughout the growth period, and the disease is particularly serious after the flowering period, which can continue to harm the maturity period, mainly harming the leaves, stems, and flowers of rapeseed, and even later on the siliques. Therefore, the rapid and accurate identification of sclerotinia of crops such as rapeseed and radish is of great significance for their efficient, safe, and green prevention and control.

[0004] Traditional methods for the diagnosis and identification of sclerotinia disease mainly include Koch's postulates, agricultural disease atlases, classification retrieval, and expert diagnosis. These methods often have the disadvantages of cumbersome procedures, strong lag, and poor timeliness. In recent years, molecular biological methods have been applied in the detection of plant pathogens, such as immunological technology and nucleic acid technology, but there are few successful examples of enzyme-linked immunosorbent assay (ELISA) in the detection of plant bacterial and fungal diseases. Subsequently, technologies such as PCR and fluorescent quantitative PCR can perform specific detection during the invasion and latent stages of pathogens with high accuracy. However, this method is expensive, time-consuming, and requires high professional knowledge, and cannot be widely used for large-scale rapid monitoring of farmland. In addition, Chen Xinhuan used an unmanned aerial vehicle simulation platform equipped with a hyperspectral imager and a thermal infrared imager to achieve discriminant analysis of healthy and sclerotinia-infected rapeseed samples at the rapeseed canopy scale and leaf scale, respectively. Spectral and multispectral imaging technologies have also been used for rapid identification and early diagnosis of sclerotinia disease in rapeseed. Zhao Yanru used a variety of spectra to carry out early identification of Sclerotinia infection in rapeseed leaves, petals and stems. However, these spectral and multi-spectral detection technologies are still in the exploratory research stage in the detection of Sclerotinia, and the instruments are expensive and require high professional knowledge, so their widespread use in large-scale rapid monitoring of farmland is still limited.

[0005] Plant electrical signals originate from the early stages of plant life and accompany the entire life activities of plants. Plant electrical signals, chemical signals, and biological signals coordinately regulate plant growth and development as well as their adaptation to environmental stress. When plants are under stress, the metabolic activities of cells and tissues are unstable, which inevitably leads to changes in the electrophysiological characteristics of plants. However, there is a large time difference between the external visual symptoms of plants under stress and their internal physiological state, and intuitive symptoms often have a long lag. Many studies have shown that the electrophysiological response of plants to stress changes is faster than their intuitive symptoms, usually appearing several days earlier than the symptoms. In addition, intuitive symptoms can only reflect the previous life process of the plant, while electrophysiological information can truly and real-timely reflect the physiological state of the plant. Changes in plant electrophysiological information are considered to be the fastest response of plants to environmental changes. Capacitance, impedance, resistance, capacitive reactance, and inductive reactance are important electrophysiological information of plants, which can directly reflect the growth physiological information of plants. Therefore, plant electrophysiological information can be used as a new indicator for early identification and diagnosis of plant diseases and insect pests. Summary of the invention

[0006] In view of the shortcomings in the prior art, the present invention provides a method for detecting plant health index and sclerotinia disease index using plant electrophysiological information, filling the gap in using biophysical indicators to identify and predict plant diseases, and can quickly, in real time, online, non-destructively and dynamically monitor the metabolic activity and health status of plants.

[0007] The present invention achieves the above technical objectives through the following technical means.

[0008] A method for detecting a plant health index and a sclerotinia disease index, specifically comprising:

[0009] According to the inherent physiological capacitive resistance IXC of plant leaves, the inherent physiological sensitivity IXL of plant leaves and the nutrient transfer rate STR of plant leaves, the active nutrient transport flow UAF per unit area of ​​plant leaves and the active nutrient transport capacity NAC of plant leaves are obtained;

[0010] Said The NAC=UAF×STR;

[0011] Obtain the plant leaf metabolic flow MF based on the plant leaf inherent physiological resistance IR, the plant leaf inherent physiological impedance IZ, the plant leaf inherent physiological capacitive reactance IXC and the plant leaf inherent physiological inductive reactance IXL;

[0012] Said

[0013] According to the plant leaf nutrient transfer rate STR and the plant leaf nutrient active transport capacity NAC, the plant leaf metabolic rate MR is obtained; the MR=STR×NAC;

[0014] According to the plant leaf metabolic flow MF and the plant leaf metabolic rate MR, the plant relative metabolic activity MA is obtained;

[0015] By comparing with the relative metabolic activity of healthy plants, the health index of the tested plants is obtained;

[0016] The degree of decrease in the relative metabolic activity of the tested plants is used to judge the sclerotinia infection status of the tested plants, and the sclerotinia disease index of the tested plants is obtained.

[0017] In a further technical solution, the plant leaf nutrient transfer rate STR is equal to the plant leaf intracellular water transfer rate VT, and Among them, IWHC is the relative water holding capacity of leaf cells, and IWHT is the relative water holding time of plant cells.

[0018] A further technical solution is to obtain the health index of the plant to be tested, specifically:

[0019] Defining MA R The relative metabolic activity of healthy plants is the reference, MA i is the relative metabolic activity of the plant to be tested, and the health index HI of the plant to be tested is expressed as:

[0020] In a further technical solution, the Sclerotinia disease index SI of the plant to be tested is:

[0021] Further technical solution, the IXC=y 1 +k 1 , where y 1 , k 1 is the parameter in the model of the physiological capacitive reactance of plant leaves changing with the clamping force; the IXL=y 2 +k 2 , where y 2 , k 2 is the parameter in the model of the physiological inductive reactance of plant leaves changing with the clamping force; IR = y 3 +k 3 , where y 3 , k 3 is the parameter in the model of the physiological resistance of plant leaves changing with the clamping force; IZ = y 4 +k 4 , where y 4 , k 4 are the parameters in the model of changes in the physiological impedance of plant leaves with clamping force.

[0022] A further technical solution, the ICP is the intrinsic physiological capacitance of plant leaves, and Where f is the test frequency and π is the circumference of a circle.

[0023] According to a further technical solution, IWHT=ICP×IZ.

[0024] According to a further technical solution, the models of changes in physiological capacitance, physiological inductance, physiological resistance and physiological impedance with clamping force are constructed according to the physiological capacitance, physiological resistance and physiological impedance of the blade to be tested under different clamping forces.

[0025] According to a further technical solution, the physiological capacitance, physiological resistance and physiological impedance of the leaves to be tested under different clamping forces are measured on mature leaves at the base of the plants to be tested and healthy plants as a reference.

[0026] The beneficial effects of the present invention are:

[0027] The present invention obtains the active nutrient transport flow per unit area of ​​plant leaves through the inherent physiological capacitive reactance and inherent physiological inductive reactance of plant leaves, and then obtains the active nutrient transport capacity of plant leaves in combination with the nutrient transfer rate of plant leaves, and then obtains the metabolic rate of plant leaves; the present invention also obtains the metabolic flow of plant leaves through the inherent physiological resistance, inherent physiological impedance, inherent physiological capacitive reactance and inherent physiological inductive reactance of plant leaves; obtains the relative metabolic activity of plants based on the metabolic flow of plant leaves and the metabolic rate of plant leaves; compares the relative metabolic activity of plants with the relative metabolic activity of healthy plants to obtain the health index of the plant to be tested; and The degree of decline in the relative metabolic activity of the tested plants, the infection status of Sclerotinia disease of the tested plants, and the Sclerotinia disease index of the tested plants can be determined; it can not only quickly, in real time, online, non-destructively, and dynamically quantitatively monitor the health status of plants and the incidence of plant Sclerotinia disease, but also predict the growth, yield and incidence trend of Sclerotinia disease of plants, which overcomes the defects of difficult prediction of Sclerotinia disease, poor prevention effect, and decreased yield quality in the production process of crops such as rapeseed and kudzu, and provides technical support for plant production management and green and efficient prevention and control of Sclerotinia disease of crops such as rapeseed and kudzu, as well as reduced application of pesticides. DETAILED DESCRIPTION

[0028] The present invention is further described below in conjunction with specific embodiments, but the protection scope of the present invention is not limited thereto.

[0029] The basic principle of the present invention is:

[0030] The model of the physiological capacitance C of plant leaves changing with the clamping force F is:

[0031]

[0032] Where: U is the test voltage, ΔH is the internal energy of the system (the plant leaf system composed of cells), d is the specific effective thickness of the plant leaf, and F is the clamping force; Formula (1) can be transformed into:

[0033] C=x 0 +hF (2)

[0034] Here x 0 and h are model parameters, so

[0035] The models of the physiological resistance, physiological impedance, physiological capacitance and physiological inductive reactance of plant leaves changing with the clamping force can be derived based on the existing technology and will not be repeated here. The models of the physiological resistance, physiological impedance, physiological capacitance and physiological inductive reactance of plant leaves changing with the clamping force can be expressed as:

[0036] The model of the physiological resistance of plant leaves changing with the clamping force is:

[0037]

[0038] Where: y 1 , k 1 , b 1 are all parameters of the model;

[0039] The model of the physiological impedance of plant leaves changing with the clamping force is:

[0040]

[0041] Where: y 2 , k 2 , b 2 are all parameters of the model;

[0042] The model of the physiological capacitive reactance of plant leaves changing with the clamping force is:

[0043]

[0044] Where: y 3 , k 3 , b 3 are all parameters of the model;

[0045] The model of the physiological reactance of plant leaves changing with the clamping force is:

[0046]

[0047] Where: y 4 , k 4 , b 4 are all parameters of the model.

[0048] When the clamping force F = 0, the inherent physiological resistance IR of the plant leaf can be expressed as: IR = y 1 +k 1 , the intrinsic physiological impedance IZ of plant leaves is expressed as: IZ = y 2 +k 2 , the intrinsic physiological capacitive reactance IXC of plant leaves is expressed as: IXC = y 3 +k 3 , the inherent physiological resistance of plant leaves IXL is expressed as: IXL = y 4 +k 4 .

[0049] The method for calculating the inherent physiological capacitance ICP based on the inherent physiological capacitive reactance IXC is: Wherein, f is the test frequency, π is the pi, and in this embodiment, π is 3.1416.

[0050] Since cells (organelles) are spherical structures, the growth of cells is closely related to the growth of their volume. For the same plant organ, especially leaves, the volume of cells is positively correlated with the volume of their vacuoles, and the main component of vacuoles is water; therefore, the capacitance of plant cells can be calculated using the formula of concentric spherical capacitors:

[0051]

[0052] Wherein, π is the circumference of a circle, and in this embodiment, π is 3.1416; C is the capacitance of the concentric spherical capacitor, ε is the dielectric constant of the electrolyte, and R 1 , R 2 are the radii of the outer and inner spheres respectively; in cells (organelles), R 2 -R 1 Can be used as the film thickness, R 1 ≈R 2 , for the same type of cells (organelles) in the same plant tissue and organ, the membrane thickness is constant, ε is constant, so the volume of the cell (organelle) and the capacitance C of the cell have the following relationship:

[0053]

[0054] (8) In the formula, α is a constant, and for the same type of cells (organelles) in the same plant tissue and organ, the value of α is constant. In addition, the volume of cells (organelles), especially those in unfolded leaves, is proportional to their water holding capacity, that is, the water holding capacity of cells is proportional to is proportional to, so we can use Characterizing the water holding capacity of plant leaves, the method for calculating the relative water holding capacity of plant leaf cells IWHC based on the inherent physiological capacitance ICP is:

[0055] According to Ohm's law, the current I Z =U / Z, where U is the measured voltage, I Z is the physiological current, Z is the impedance; at the same time, the current is equal to the capacitance multiplied by the differential of the voltage in time. After integral transformation, the time t is the product of the capacitance and the impedance. Therefore, according to the inherent physiological capacitance ICP and the inherent physiological impedance IZ of the plant leaves, the calculation formula for the relative water holding time IWHT of the plant intracellular water based on electrophysiological parameters is: IWHT=ICP×IZ; according to the relative water holding capacity IWHC of the leaf intracellular water and the relative water holding time IWHT of the plant intracellular water, the intracellular water transfer rate can be calculated, and its calculation formula is:

[0056] Since nutrients are soluble in water, the intracellular water transfer rate of plant leaves and the nutrient transfer rate of plant leaves are conceptually the same. The nutrient transfer rate STR of plant leaves is equal to the intracellular water transfer rate VT of plant leaves. The calculation formula is:

[0057] Protein-binding proteins (intrinsic proteins) that cause resistance in biological tissues are most closely related to the active transport of nutrients. Therefore, the active transport flow rate of nutrients per unit area of ​​plant leaves, UAF, can be defined as:

[0058] Therefore, the active nutrient transport capacity NAC of plant leaves is the active nutrient transport flow rate UAF per unit area of ​​plant leaves multiplied by the nutrient transfer rate STR of plant leaves: The method for calculating the active nutrient transport capacity NAC of plant leaves is: NAC = UAF × STR.

[0059] Metabolic capacity is related to bioelectricity. The greater the bioelectricity, the more complete the metabolism. Therefore, the bioelectricity I based on resistance is R =U / IR, bioelectric current I based on capacitive reactance Xc =U / IXC, biocurrent I based on inductive reactance XL =U / IXL, impedance-based biocurrent I IZ =U / IZ. Since the physiological capacitance, resistance and impedance of plant leaves are measured in parallel, U is the same. Therefore, the metabolic flow MF of plant leaves can be defined as:

[0060] The active transport of nutrients is related to the metabolic rate. The stronger the active transport capacity of nutrients, the greater the metabolic rate. Therefore, the metabolic rate of plant leaves MR is defined as: MR = STR × NAC.

[0061] According to the plant leaf metabolic flow MF and the plant leaf metabolic rate MR, the relative metabolic activity MA of the plant based on electrophysiological information is defined as follows:

[0062] Defining MA R The relative metabolic activity of healthy plants is the reference, MA i is the relative metabolic activity of the tested plant, therefore, the plant health index HI is: Plant Sclerotinia Disease Index SI:

[0063] Example

[0064] Test plants: Brassica napus seeds, variety Baoyouhan 12, provided by Guizhou Hemufu Seed Co., Ltd. (Guiyang, China). Seeds of Brassica rapa were collected at the old campus of the Institute of Geochemistry, Chinese Academy of Sciences. A mixture of vermiculite and perlite in a ratio of 2:1 was used as the germination and growth medium for rapeseed and Brassica rapa seeds, and an appropriate amount of 1 / 2 times Hogland nutrient solution was added regularly. When the seedlings grew 3 leaves, the seedlings with consistent growth were selected and transplanted into pots (30 cm in diameter, 24 cm in height, and 20 cm in bottom diameter) filled with soil for growth. Water was applied once every 5 days, and appropriate amounts of compound fertilizer and organic fertilizer were applied once in the seedling stage and flowering stage. When the rapeseed had grown for about 4 weeks and had 7 to 8 leaves (the plant height was about 30 to 40 cm), plants with consistent growth were selected for seedling stage inoculation of sclerotinia infection experiment. When the rapeseed and Brassica rapa grew to the flowering stage, plants with consistent growth were selected for the flowering stage inherent inoculation of sclerotinia infection experiment.

[0065] Test pathogens: The purchased Sclerotinia sclerotiorum strain was first activated and cultured on potato (PDA) medium for 7 days, and then a 9mm block of the same diameter was cut and inoculated on the center of a new PDA plate for culture, with the mycelium facing down and contacting the medium. The diameter of the culture dish was 9cm, and the PDA was a plate after 10mL of liquid culture medium solidified. The newly cultured Sclerotinia sclerotiorum blocks for 7 days were used for inoculation of sclerotia infection experiments.

[0066] Inoculation sclerotium infection experiment: Select rapeseed and sclerotium plants with the same growth. The mature leaves at the base of each plant (the 5th to 6th fully expanded leaves) are used as inoculation leaves. The leaves are healthy, free of pests and diseases, and without nicks. The equatorial parts of the left and right leaves along the middle large vein of each leaf are used as inoculation points. Sclerotinia blocks of different diameters are inoculated to the inoculation points, with the mycelium facing down and contacting the leaves. Then, cotton of the same size is soaked in water (or oxalic acid solution) and covered with the inoculum for moisturizing culture. The plants are placed in a constant temperature and light incubator for culture. The experiment has 7 treatments, namely 6mm blocks, 9mm blocks, 12mm blocks, 9mm blocks + 0.1mM oxalic acid solution, 0.1mM oxalic acid solution, 9mm PDA blocks and blank controls. Each treatment is repeated 3 times; 0.1mM oxalic acid solution, 9mm PDA blocks and blank controls are control treatments. Among them, the wet cotton used for moisturizing in the 5 treatments of 6mm bacterial block, 9mm bacterial block, 12mm bacterial block, 9mm PDA block and blank control was soaked in sterile water, and the wet cotton used for moisturizing in the 2 treatments of 9mm bacterial block + 0.1mM oxalic acid solution and 0.1mM oxalic acid solution was soaked in 0.1mM oxalic acid. The moisturizing cotton was soaked regularly every 3 days. The leaves of rapeseed in the seedling stage, rapeseed in the flowering stage and Zhugecai in the flowering stage were inoculated respectively. The inoculation experiment was carried out from 8:30 to 9:30 in the morning, and the inoculation environment temperature was 20.0±2.0℃. The light temperature and light duration were 20.0 / 19.0℃ (day / night) and 12h / 12h (day / night), respectively, and the light intensity was 500μmol / m 2 ·s. The intrinsic electrophysiological parameters, relative water holding time, intracellular water transfer rate, nutrient transfer rate, nutrient active transport flow, metabolic flux, metabolic rate, relative metabolic activity, health index and disease index of the inoculated leaves were recorded at the time of inoculation (immediate measurement after inoculation), 1, 3, 5, 7 and 9 days after inoculation. The measurement time was 8:30-9:30 am every day, and the measurement environment temperature was 20.0±2.0℃. During the measurement, the height of the shelf for placing potted plants was adjusted to facilitate the monitoring of the electrophysiological information of plant leaves at the fixed monitoring site.

[0067] The non-inoculated part of the leaf to be tested (close to the inoculated part) is clamped between the parallel electrode plates of the measuring device, and the measuring voltage and frequency are set. Different clamping forces are set by changing the mass of the iron block. The physiological capacitance, physiological resistance, and physiological impedance of the plant leaves under different clamping forces are measured in parallel mode, and the physiological capacitive reactance and physiological inductive reactance are calculated; the physiological capacitive reactance is calculated based on the physiological capacitance of the plant leaves. Where Xc is the physiological capacitive reactance of plant leaves, C is the physiological capacitance of plant leaves, f is the test frequency, π is pi, and in this embodiment, π is 3.1416; based on the physiological impedance, physiological resistance and physiological capacitive reactance of plant leaves, the physiological inductive reactance of plant leaves is calculated: Among them, Xl is the physiological inductive reactance of plant leaves, Xc is the physiological capacitive reactance of plant leaves, Z is the physiological impedance of plant leaves, and R is the physiological resistance of plant leaves; then, a model of the change of physiological capacitance, physiological capacitive reactance, physiological inductive reactance, physiological resistance, and physiological impedance of plant leaves with clamping force is constructed to obtain the various parameters of each model; Table 1-2 is a model constructed with a random sample data of rapeseed in the seedling stage, rapeseed in the flowering stage, and radish in the flowering stage; other data models are omitted.

[0068] Table 1 Model (CF) and parameters of physiological capacitance (C) of rapeseed at seedling stage, rapeseed at flowering stage and radish at flowering stage as a function of clamping force (F)

[0069]

[0070] Table 2 Physiological impedance (Z), physiological resistance (R), physiological capacitance (Xc) and physiological inductance (Xl) of rapeseed at seedling stage, rapeseed at flowering stage and radish at flowering stage as a function of clamping force (F) Models (ZF, RF, Xc-F, Xl-F) and parameters

[0071]

[0072]

[0073] According to the parameters in each model, the inherent physiological resistance IR, inherent physiological impedance IZ, inherent physiological capacitive reactance IXC and inherent physiological inductive reactance IX of rapeseed in the seedling stage, rapeseed in the flowering stage and radish in the flowering stage were obtained, as shown in Table 3-5.

[0074] Table 3 Intrinsic physiological resistance IR, intrinsic physiological impedance IZ, intrinsic physiological capacitance IXC and intrinsic physiological inductance IX of rapeseed in seedling stage after inoculation with Sclerotinia sclerotiorum

[0075]

[0076]

[0077]

[0078] Table 4 Intrinsic physiological resistance IR, intrinsic physiological impedance IZ, intrinsic physiological capacitance IXC and intrinsic physiological inductance IX of rapeseed during flowering after inoculation with Sclerotinia sclerotiorum

[0079]

[0080]

[0081] Table 5 Intrinsic physiological resistance IR, intrinsic physiological impedance IZ, intrinsic physiological capacitive reactance IXC and intrinsic physiological inductive reactance IX of chrysanthemum inflorescence after inoculation with Sclerotinia sclerotiorum

[0082]

[0083]

[0084] Based on the inherent physiological resistance IR, inherent physiological impedance IZ, inherent physiological capacitive reactance IXC and inherent physiological inductive reactance IXL, the relative water holding time IWHT of intracellular water, nutrient transfer rate STR, active nutrient transport capacity NAC, metabolic flux MF and metabolic rate MR of leaves of rapeseed in the seedling stage, rapeseed in the flowering stage and radish in the flowering stage were obtained after infection with Sclerotinia sclerotiorum, as shown in Tables 6-8.

[0085] Table 6 Intracellular water relative holding time IWHT, nutrient transfer rate STR, nutrient active transport capacity NAC, metabolic flux MF and metabolic rate MR of rapeseed seedlings after inoculation with Sclerotinia sclerotiorum

[0086]

[0087]

[0088]

[0089] Table 7 Intracellular water relative holding time IWHT, nutrient transfer rate STR, nutrient active transport capacity NAC, metabolic flux MF and metabolic rate MR of rapeseed at flowering stage after inoculation with Sclerotinia sclerotiorum

[0090]

[0091]

[0092] Table 8 Intracellular water relative water holding time IWHT, nutrient transfer rate STR, nutrient active transport capacity NAC, metabolic flux MF and metabolic rate MR of Z. rapa during flowering after inoculation with Sclerotinia sclerotiorum

[0093]

[0094]

[0095]

[0096] Based on the metabolic flux MF and metabolic rate MR, the relative metabolic activity MA of the healthy plants of rapeseed at seedling stage, rapeseed at flowering stage and leaves of radish at flowering stage after infection with Sclerotinia sclerotiorum was obtained. R , relative metabolic activity MA of the tested plants i , plant health index HI and plant sclerotinia disease index SI, as shown in Tables 9-11.

[0097] Table 9 Relative metabolic activity MA of healthy plants of rapeseed in the seedling stage after inoculation with Sclerotinia sclerotiorum R , relative metabolic activity MA of the tested plantsi , plant sclerotinia disease index SI and plant health index HI

[0098]

[0099]

[0100] Table 10 Relative metabolic activity MA of healthy plants of rapeseed in flowering after inoculation with Sclerotinia sclerotiorum R , relative metabolic activity MA of the tested plants i , plant sclerotinia disease index SI and plant health index HI

[0101]

[0102]

[0103] Table 11 Relative metabolic activity MA of healthy plants of reference in flowering period of Zygophyllum after inoculation with Sclerotinia sclerotiorum R , relative metabolic activity MA of the tested plants i , plant sclerotinia disease index SI and plant health index HI

[0104]

[0105]

[0106]

[0107] The implementation effects of the present invention are as follows:

[0108] As shown in Tables 3-5, the inherent impedance, inherent resistance, inherent capacitance and inherent inductance of the leaves of rapeseed at seedling stage, rapeseed at flowering stage and radish at flowering stage in the three treatments of 0.10mM oxalic acid, 9mm PDA block and blank control did not change much within 9 days. The inherent impedance, inherent resistance, inherent capacitance and inherent inductance of the leaves of rapeseed at seedling stage, rapeseed at flowering stage and radish at flowering stage in the four treatments of 6mm sclerotinia block, 9mm sclerotinia block, 12mm sclerotinia block and 9mm sclerotinia block + 0.10mM oxalic acid increased sharply after inoculation. In addition, with the increase of the inoculation amount of sclerotinia (6mm<9mm<12mm), the increasing trend of the inherent impedance, inherent resistance, inherent capacitance and inherent inductance of the leaves of rapeseed at seedling stage, rapeseed at flowering stage and radish at flowering stage was more obvious.

[0109] As shown in Tables 6 to 8, the relative water holding time IWHT, nutrient transfer rate STR, nutrient active transport capacity NAC, metabolic flux MF and relative metabolic activity MA of the leaves of rapeseed at seedling stage, rapeseed at flowering stage and radish at flowering stage in the three treatments of 0.10mM oxalic acid, 9mm PDA block and blank control did not change much within 9 days. The relative water holding time IWHT, nutrient transfer rate STR, nutrient active transport capacity NAC, metabolic flux MF and relative metabolic activity MA of the leaves of rapeseed at seedling stage, rapeseed at flowering stage and radish at flowering stage in the four treatments of 6mm sclerotinia block, 9mm sclerotinia block, 12mm sclerotinia block and 9mm sclerotinia block + 0.10mM oxalic acid decreased sharply after inoculation. In addition, with the increase of the inoculation amount of Sclerotinia sclerotiorum (6mm<9mm<12mm), the relative water holding time IWHT, nutrient transfer rate STR, nutrient active transport capacity NAC, metabolic flux MF and relative metabolic activity MA of leaves of rapeseed at the seedling stage, rapeseed at the flowering stage and Aralia oleracea at the flowering stage decreased more significantly.

[0110] As shown in Tables 9 to 11, the plant health index HI and plant sclerotinia disease index SI of the leaves of rapeseed at seedling stage, rapeseed at flowering stage and radish at flowering stage treated with 0.10 mM oxalic acid, 9 mm PDA block and blank control did not change much within 9 days. The plant health index HI of rapeseed at seedling stage, rapeseed at flowering stage and radish at flowering stage treated with 6 mm sclerotinia block, 9 mm sclerotinia block, 12 mm sclerotinia block and 9 mm sclerotinia block + 0.10 mM oxalic acid decreased sharply after inoculation, and the plant sclerotinia disease index SI increased sharply after inoculation. In addition, with the increase of the inoculation amount of sclerotinia (6 mm < 9 mm < 12 mm), the sclerotinia disease index SI of rapeseed at seedling stage, rapeseed at flowering stage and radish at flowering stage was more obviously increased, and the health index HI decreased more significantly. It can also be seen from Tables 9 to 11 that the immediate inoculation of bacterial blocks and oxalic acid or PDA blocks has a great impact on the metabolic activity of the leaves, and the 12mm bacterial blocks have a stimulating effect on the metabolic activity of rapeseed seedlings. Oxalic acid alone not only did not reduce the metabolic activity, but also had a great stimulation on the metabolic activity of 1-5 days after inoculation, which shows that 1-5 days after inoculation, 1-5 days after inoculation, is not a pathogenic factor in itself, and only in the presence of sclerotinia disease can it affect the metabolic activity and health of the plant. These results not only well characterize the occurrence and development of sclerotinia disease, but also well characterize the degree of influence of oxalic acid or the interaction between oxalic acid and sclerotinia disease on plant pathogenicity and health, and realize the real-time, online, and rapid monitoring and prediction of plant health and sclerotinia disease, providing basic data and support for better analysis of the pathogenic process and mechanism of sclerotinia disease.

[0111] The embodiments are preferred implementations of the present invention, but the present invention is not limited to the above-mentioned implementations. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essential content of the present invention belong to the protection scope of the present invention.

Claims

1. A method for detecting a plant health index and a sclerotinia disease index, characterized in that: According to the inherent physiological capacitive resistance IXC of plant leaves, the inherent physiological sensitivity IXL of plant leaves and the nutrient transfer rate STR of plant leaves, the active nutrient transport flow UAF per unit area of ​​plant leaves and the active nutrient transport capacity NAC of plant leaves are obtained; Said The NAC=UAF×STR; Obtain the plant leaf metabolic flow MF based on the plant leaf inherent physiological resistance IR, the plant leaf inherent physiological impedance IZ, the plant leaf inherent physiological capacitive reactance IXC and the plant leaf inherent physiological inductive reactance IXL; Said According to the plant leaf nutrient transfer rate STR and the plant leaf nutrient active transport capacity NAC, the plant leaf metabolic rate MR is obtained; the MR=STR×NAC; According to the plant leaf metabolic flow MF and the plant leaf metabolic rate MR, the plant relative metabolic activity MA is obtained; By comparing with the relative metabolic activity of healthy plants, the health index of the tested plants is obtained; According to the decrease degree of relative metabolic activity of the tested plants, the infection status of sclerotinia sclerotinia of the tested plants is judged, and the disease index of sclerotinia sclerotinia of the tested plants is obtained; The health index of the plant to be tested is obtained as follows: Defining MA R The relative metabolic activity of healthy plants is the reference, MA i is the relative metabolic activity of the plant to be tested, and the health index HI of the plant to be tested is expressed as: The Sclerotinia disease index SI of the plant to be tested is:

2. The method for detecting plant health index and sclerotinia disease index according to claim 1, characterized in that: The plant leaf nutrient transfer rate STR is equal to the plant leaf intracellular water transfer rate VT, and Among them, IWHC is the relative water holding capacity of leaf cells, and IWHT is the relative water holding time of plant cells.

3. The method for detecting plant health index and sclerotinia disease index according to claim 1, characterized in that: The IXC=y1+k1, wherein y1 and k1 are parameters in the model of the change of the physiological capacitive reactance of the plant leaves with the clamping force; the IXL=y2+k2, wherein y2 and k2 are parameters in the model of the change of the physiological inductive reactance of the plant leaves with the clamping force; the IR=y3+k3, wherein y3 and k3 are parameters in the model of the change of the physiological resistance of the plant leaves with the clamping force; the IZ=y4+k4, wherein y4 and k4 are parameters in the model of the change of the physiological impedance of the plant leaves with the clamping force.

4. The method for detecting plant health index and sclerotinia disease index according to claim 2, characterized in that: Said ICP is the intrinsic physiological capacitance of plant leaves, and Where f is the test frequency and π is the circumference of a circle.

5. The method for detecting plant health index and sclerotinia disease index according to claim 4, characterized in that: The IWHT=ICP×IZ.

6. The method for detecting plant health index and sclerotinia disease index according to claim 3, characterized in that: The models of physiological capacitance, physiological inductance, physiological resistance and physiological impedance changing with clamping force are constructed according to the physiological capacitance, physiological resistance and physiological impedance of the leaf to be tested under different clamping forces.

7. The method for detecting plant health index and sclerotinia disease index according to claim 6, characterized in that: The physiological capacitance, physiological resistance and physiological impedance of the leaves to be tested under different clamping forces are measured on mature leaves at the base of the plants to be tested and the healthy plants as a reference.

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