A method and system for dynamically evaluating the efficacy of a herbicide

Abstract

This application belongs to the field of agricultural technology, and discloses a method and system for dynamically evaluating the efficacy of herbicides. The efficacy is evaluated by two methods, namely, the colorimetric method and the plant fluorescence spectroscopy method. The weights of the two evaluation results are dynamically adjusted according to environmental parameters, and the final efficacy evaluation result is obtained by comprehensively combining the two evaluation results according to the weights, which can effectively overcome environmental interference and improve the accuracy and reliability of herbicide efficacy evaluation.

Description

Technical Field

[0001] This application relates to the field of agricultural technologies, and more particularly, to a method and system for dynamically evaluating the efficacy of herbicides. Background Art

[0002] In the context of the booming development of organic agriculture, the application of environmentally friendly herbicides (such as biogenic herbicides, plant essential oil herbicides, natural organic acid herbicides, amino acid herbicides, polysaccharide herbicides, etc.) has become increasingly popular. However, compared with traditional herbicides, the efficacy of environmentally friendly herbicides is more easily affected by environmental factors, especially factors such as rainfall and irrigation, which often lead to rapid attenuation of efficacy and unstable weed control effects, thereby affecting the yield and quality of organic crops. Currently, for the evaluation of herbicidal effects, traditional methods mainly rely on manual visual inspection, but this method is highly subjective and lacks sufficient evaluation accuracy, making it difficult to meet the requirements of fine management in modern agriculture. Although laboratory testing methods can provide relatively accurate efficacy data, their operation process is complex, time-consuming, and laborious, and they cannot meet the actual application scenarios of rapid field evaluation.

[0003] Especially in complex and variable environmental conditions, a single efficacy evaluation method often fails to comprehensively and accurately reflect the true effects of herbicides, easily leading to misjudgment and delaying the best remedial opportunity, ultimately having an adverse impact on the yield and quality of organic crops. For example, in an environment with high soil humidity, excessive moisture in the soil may interfere with the effective extraction of herbicides, thereby affecting the accuracy of the efficacy evaluation results based on soil sampling; while under conditions of high light intensity, the photosynthesis of plant leaves is active, and the subtle changes in their physiological states may be masked, thus reducing the sensitivity of the efficacy evaluation method based on plant physiological indicators. Therefore, in the complex and variable application scenarios of organic agriculture, there is an urgent need for a field evaluation technology that can effectively overcome environmental interference and achieve rapid and accurate evaluation of the efficacy of environmentally friendly herbicides, so as to timely guide farmers to use drugs scientifically and reasonably, thereby effectively ensuring the stability and sustainability of organic agricultural production.

[0004] In view of the above problems, the existing technologies urgently need to be improved. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for dynamically evaluating the efficacy of herbicides, which has the advantages of improving the accuracy and reliability of herbicide efficacy evaluation.

[0006] In a first aspect, this application provides a method for dynamically evaluating the efficacy of herbicides, which is used to evaluate the efficacy of environmentally friendly herbicides after the application of environmentally friendly herbicides. The steps of this method include:

[0007] A1. Collect field soil samples, extract herbicides from the field soil samples, add a color developer for a color reaction to obtain a color solution;

[0008] A2. Obtain the color characteristics of the color solution to match with the standard color patches of a preset color comparison card, and obtain the evaluation result of the herbicidal efficacy by colorimetry;

[0009] A3. Use a plant fluorescence spectrometer to obtain the chlorophyll fluorescence spectrum of weed leaves, analyze the fluorescence spectrum characteristic parameters of the chlorophyll fluorescence spectrum, and determine the evaluation result of the herbicidal efficacy by the plant fluorescence spectrometry according to the fluorescence spectrum characteristic parameters;

[0010] A4. Collect environmental parameters in real time, and dynamically adjust the reference weights of the evaluation result of the herbicidal efficacy by colorimetry and the evaluation result of the herbicidal efficacy by the plant fluorescence spectrometry according to the current environmental parameters; the environmental parameters include soil humidity and light intensity;

[0011] A5. According to the adjusted reference weights, comprehensively combine the evaluation result of the herbicidal efficacy by colorimetry and the evaluation result of the herbicidal efficacy by the plant fluorescence spectrometry to obtain the comprehensive evaluation result of the herbicidal efficacy.

[0012] Preferably, step A1 includes:

[0013] A101. Collect multi-point field soil samples, mix them evenly according to the quartering method to obtain a mixed soil sample;

[0014] A102. Weigh a preset mass of the mixed soil sample, add a pretreatment solution, and extract the herbicide by ultrasonic oscillation to obtain a soil extract; the pretreatment solution contains acetonitrile, water and formic acid, and the volume ratio is 4:5:0.1;

[0015] A103. Purify the soil extract using a solid-phase extraction column to remove pigments and humus to obtain a purified extract;

[0016] A104. Concentrate the purified extract by nitrogen blowing under nitrogen protection to obtain a concentrated extract;

[0017] A105. Dissolve the concentrated extract in a color developer solution, and react in the dark for a preset time to obtain a color solution.

[0018] Preferably, step A101 includes:

[0019] Based on the field terrain data, divide the target field into multiple micro-topographic units with an elevation difference less than a preset threshold; the field terrain data includes elevation data and slope data;

[0020] In each micro-topographic unit, randomly collect multiple soil samples;

[0021] Mix the soil samples within the same micro-topographic unit evenly to obtain the representative soil sample corresponding to the micro-topographic unit;

[0022] Using the quartering method, weighted mixing of the representative soil samples of each micro-topographic unit is carried out according to the area proportion of each micro-topographic unit to obtain a mixed soil sample.

[0023] Preferably, step A2 includes:

[0024] A201. Use an image acquisition device to acquire an image of the chromogenic solution, and use an adaptive threshold segmentation algorithm to segment the chromogenic region from the chromogenic solution image;

[0025] A202. For the segmented chromogenic region, calculate the mean and standard deviation of the R, G, and B channels in the RGB color space, convert the RGB color space to the HSV color space, and calculate the mean and standard deviation of the H, S, and V channels to obtain 12 color feature parameters, which form a color feature vector;

[0026] A203. Input the color feature vector into a pre-trained color matching model to obtain the standard color block matching result output by the color matching model, so as to determine the colorimetric method drug efficacy evaluation result.

[0027] Preferably, after step A202 and before step A203, the following steps are further included:

[0028] A204. Real-time collect the light intensity in the field and calculate the average light intensity within the first preset time window;

[0029] A205. According to the average light intensity, use a polynomial regression model to perform light correction on the color feature vector.

[0030] Preferably, step A3 includes:

[0031] A301. Use a plant fluorescence spectrometer to obtain the chlorophyll fluorescence spectrum data generated by weed leaves under the excitation spectrum irradiation to obtain the original fluorescence spectrum;

[0032] A302. Preprocess the original fluorescence spectrum to obtain the preprocessed fluorescence spectrum; the preprocessing includes noise removal, background fluorescence correction, and smoothing processing;

[0033] A303. Extract multiple fluorescence spectrum feature parameters from the preprocessed fluorescence spectrum; the fluorescence spectrum feature parameters include the F685 / F730 ratio, the maximum photochemical efficiency of photosystem II, and the actual photochemical efficiency of photosystem II;

[0034] A304. Input the extracted fluorescence spectral characteristic parameters into the pre-established drug efficacy evaluation model to obtain the drug efficacy evaluation result of plant fluorescence spectrometry output by the drug efficacy evaluation model.

[0035] Preferably, step A301 includes:

[0036] Determine the weed species and growth stage information of the measured weeds based on image recognition technology;

[0037] According to the weed species and growth stage information, adjust the spectral parameters of the excitation spectrum;

[0038] Use a plant fluorescence spectrometer to irradiate the weed leaves based on the adjusted excitation spectrum, and obtain the chlorophyll fluorescence spectral data generated by the weed leaves under the irradiation of the excitation spectrum to obtain the original fluorescence spectrum.

[0039] Preferably, step A4 includes:

[0040] A401. Collect the field soil humidity and light intensity in real time, and calculate the average soil humidity and average light intensity within the second preset time window;

[0041] A402. According to the average soil humidity and average light intensity, based on the mapping relationship between environmental parameters and reference weight adjustment coefficients, calculate the weight adjustment coefficient of the colorimetric method drug efficacy evaluation result and the weight adjustment coefficient of the plant fluorescence spectrometry drug efficacy evaluation result;

[0042] A403. According to the weight adjustment coefficient of the colorimetric method drug efficacy evaluation result and the weight adjustment coefficient of the plant fluorescence spectrometry drug efficacy evaluation result, respectively adjust the reference weight of the colorimetric method drug efficacy evaluation result and the reference weight of the plant fluorescence spectrometry drug efficacy evaluation result.

[0043] Preferably, step A5 includes:

[0044] A501. Calculate the difference between the colorimetric method drug efficacy evaluation result and the plant fluorescence spectrometry drug efficacy evaluation result, compare the difference with the preset deviation threshold, and judge whether the evaluation results conflict;

[0045] A502. If the evaluation results conflict, then based on the historical drug efficacy evaluation data, determine the drug efficacy evaluation correction rule corresponding to the current evaluation result, and according to the determined drug efficacy evaluation correction rule, respectively correct the colorimetric method drug efficacy evaluation result and the plant fluorescence spectrometry drug efficacy evaluation result to obtain the final colorimetric method drug efficacy evaluation result and the final plant fluorescence spectrometry drug efficacy evaluation result;

[0046] A503. If the evaluation results do not conflict, then use the original colorimetric method drug efficacy evaluation result and the original plant fluorescence spectrometry drug efficacy evaluation result as the final colorimetric method drug efficacy evaluation result and the final plant fluorescence spectrometry drug efficacy evaluation result;

[0047] A504. Based on the adjusted reference weights, the final colorimetric efficacy evaluation result and the final plant fluorescence spectrometry efficacy evaluation result are weighted and integrated to obtain the comprehensive efficacy evaluation result.

[0048] In a second aspect, the present application provides a dynamic evaluation system for herbicide efficacy, which is used to evaluate the efficacy of an environmentally friendly herbicide after the environmentally friendly herbicide is applied. The system includes:

[0049] An image acquisition device for acquiring an image of a color-developing solution; the color-developing solution is obtained by collecting a field soil sample, extracting the herbicide in the field soil sample, and adding a color-developing agent for a color reaction;

[0050] A plant fluorescence spectrometer for measuring the chlorophyll fluorescence spectrum of weed leaves;

[0051] An environmental parameter acquisition device for real-time acquisition of environmental parameters; the environmental parameters include soil humidity and light intensity;

[0052] An upper computer for performing the following steps:

[0053] Extracting the color characteristics of the color-developing solution from the color-developing solution image for matching with the standard color patches of a preset colorimetric card to obtain the colorimetric efficacy evaluation result;

[0054] Analyzing the fluorescence spectrum characteristic parameters of the chlorophyll fluorescence spectrum, and determining the plant fluorescence spectrometry efficacy evaluation result according to the fluorescence spectrum characteristic parameters;

[0055] Dynamically adjusting the reference weights of the colorimetric efficacy evaluation result and the plant fluorescence spectrometry efficacy evaluation result according to the current environmental parameters;

[0056] Based on the adjusted reference weights, integrating the colorimetric efficacy evaluation result and the plant fluorescence spectrometry efficacy evaluation result to obtain the comprehensive efficacy evaluation result.

[0057] Beneficial effects: A method and system for dynamically evaluating herbicide efficacy provided by the present application evaluate the efficacy through two methods, namely colorimetry and plant fluorescence spectrometry, and dynamically adjust the weights of the two evaluation results according to environmental parameters, and comprehensively obtain the efficacy evaluation result, which can effectively overcome environmental interference and improve the accuracy and reliability of herbicide efficacy evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flowchart of the method for dynamically evaluating herbicide efficacy provided by an embodiment of the present application.

[0059] Figure 2 It is a schematic structural diagram of the system for dynamically evaluating herbicide efficacy provided by an embodiment of the present application.

[0060] Description of reference numerals: 1. Image acquisition device; 2. Plant fluorescence spectrometer; 3. Environmental parameter acquisition device; 4. Host computer. Specific embodiments

[0061] The technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the present application described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0062] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first" and "second" are only used for differential description and cannot be understood as indicating or implying relative importance.

[0063] Refer to Figure 1 , the present application proposes a method for dynamically evaluating the efficacy of herbicides, which is used to evaluate the efficacy of environmentally friendly herbicides after the application of environmentally friendly herbicides. The steps of this method include:

[0064] A1. Collect field soil samples, extract the herbicides in the field soil samples, add a color developer for a color reaction to obtain a color solution;

[0065] A2. Obtain the color characteristics of the color solution to match with the standard color patches of a preset color comparison card to obtain the efficacy evaluation result by colorimetry;

[0066] A3. Use a plant fluorescence spectrometer to obtain the chlorophyll fluorescence spectrum of weed leaves, analyze the fluorescence spectrum characteristic parameters of the chlorophyll fluorescence spectrum, and determine the efficacy evaluation result by the plant fluorescence spectrometry method according to the fluorescence spectrum characteristic parameters;

[0067] A4. Collect environmental parameters in real time, and dynamically adjust the reference weights of the efficacy evaluation result by colorimetry and the efficacy evaluation result by the plant fluorescence spectrometry method according to the current environmental parameters; the environmental parameters include soil humidity and light intensity;

[0068] A5. According to the adjusted reference weights, comprehensively combine the efficacy evaluation result by colorimetry and the efficacy evaluation result by the plant fluorescence spectrometry method to obtain the comprehensive efficacy evaluation result.

[0069] Among them, step A1 can be implemented by the following methods: First, select a representative area in the field for soil sampling. For example, random sampling, grid sampling, or stratified sampling can be used to collect a certain amount of soil samples. Then, pre-treat the collected soil samples, such as drying, grinding, sieving, etc., to remove impurities and large particulate matter in the soil. Next, extract the herbicide components from the pre-treated soil samples. The extraction methods can include solvent extraction, ultrasonic extraction, or solid-phase extraction, etc. During the extraction process, select appropriate extraction solvents and extraction conditions according to the properties of the herbicide to be measured to improve the extraction efficiency and selectivity. The extracted herbicide extract needs to undergo a color reaction for subsequent colorimetric analysis. The color reaction refers to the process in which the herbicide reacts with the color reagent to form a colored substance after adding the color reagent. The selection of the color reagent depends on the type and properties of the herbicide to be measured. Commonly used color reagents include enzymes, dyes, or metal ions, etc. By controlling the conditions of the color reaction, such as reaction time, temperature, and pH value, etc., a colorimetric solution with a color depth related to the herbicide concentration can be obtained.

[0070] Among them, in step A2, the acquisition of color characteristics can be achieved in various ways. For example, the color of the colorimetric solution can be visually observed and compared with a standard colorimetric card to determine which standard color block the color of the colorimetric solution is closest to, thereby obtaining a semi-quantitative colorimetric result. To improve the objectivity and accuracy of colorimetric analysis, an image acquisition device, such as a digital camera or a scanner, etc., can be used to collect the image of the colorimetric solution, and then image processing software is used to analyze the color information of the image and extract color characteristic parameters. The color characteristic parameters can include numerical values in color spaces such as RGB values, HSV values, Lab values, etc., or numerical values of color attributes such as chromaticity, brightness, and saturation. The preset colorimetric card refers to a series of pre-made standard color blocks, and each standard color block represents a known herbicide concentration or efficacy level. The colorimetric card can be made by methods such as standard solution preparation and color measurement instrument calibration to ensure the accuracy and reliability of the colorimetric card. By matching the color characteristics of the colorimetric solution with the standard color blocks of the preset colorimetric card, the herbicide concentration or efficacy level corresponding to the colorimetric solution can be determined, thereby obtaining the colorimetric method efficacy evaluation result. The matching methods can include visual comparison, color characteristic vector similarity calculation, or color matching models.

[0071] Among them, in step A3, the plant fluorescence spectrometer is an instrument used to measure the chlorophyll fluorescence spectrum of plant leaves. Its working principle is to irradiate plant leaves with excitation light of a specific wavelength. After chlorophyll molecules absorb light energy, they will emit fluorescence, and the fluorescence spectrometer can measure and record the wavelength and intensity distribution of the fluorescence. The chlorophyll fluorescence spectrum can reflect the photosynthetic physiological state of plants. Herbicides will affect the photosynthetic system of plants, thus causing changes in the chlorophyll fluorescence spectrum. By analyzing the fluorescence spectrum characteristic parameters of the chlorophyll fluorescence spectrum, the efficacy of herbicides on plants can be evaluated. The fluorescence spectrum characteristic parameters can include at least one of fluorescence peak position, fluorescence peak intensity, fluorescence ratio, etc. For example, the F685 / F730 ratio is a commonly used chlorophyll fluorescence spectrum parameter that can reflect the degree of damage to the plant photosynthetic apparatus. Parameters such as the maximum photochemical efficiency of photosystem II and the actual photochemical efficiency of photosystem II can reflect the efficiency of plant photosynthesis. According to the relationship between the fluorescence spectrum characteristic parameters and the efficacy, an efficacy evaluation model can be established, such as a linear regression model, a neural network model or a support vector machine model, etc. Inputting the extracted fluorescence spectrum characteristic parameters into the efficacy evaluation model can obtain the efficacy evaluation result of the plant fluorescence spectrometry method.

[0072] Among them, in step A4, the environmental parameters include soil humidity and light intensity. Soil humidity can be measured using a soil humidity sensor, and light intensity can be measured using a light intensity sensor. According to the current environmental parameters, dynamically adjust the reference weights of the colorimetric method efficacy evaluation result and the plant fluorescence spectrometry method efficacy evaluation result. The principle of weight adjustment is that when the environmental conditions are not conducive to a certain evaluation method, reduce the weight of this method and increase the weight of the other method, and vice versa. For example, when the soil humidity is high, the soil sampling method may be interfered by moisture. At this time, the weight of the colorimetric method evaluation result can be appropriately reduced, and the weight of the plant fluorescence spectrometry method evaluation result can be increased. When the light intensity is too high or too low, the plant fluorescence spectrometry method may be affected by the light conditions. At this time, the weight of the plant fluorescence spectrometry method evaluation result can be appropriately reduced, and the weight of the colorimetric method evaluation result can be increased. The specific method of weight adjustment can adopt a preset weight adjustment coefficient table, a weight adjustment function or an expert system, etc.

[0073] Among them, in step A5, after obtaining the colorimetric method efficacy evaluation result and the plant fluorescence spectroscopy method efficacy evaluation result, according to the reference weight determined in step A4, the two evaluation results are weighted averaged or weighted summed to obtain the comprehensive efficacy evaluation result. For example, the weight of the colorimetric method efficacy evaluation result can be set as w1, and the weight of the plant fluorescence spectroscopy method efficacy evaluation result can be set as w2, and w1 + w2 = 1. The comprehensive efficacy evaluation result = w1 * colorimetric method efficacy evaluation result + w2 * plant fluorescence spectroscopy method efficacy evaluation result. By integrating the results of the two evaluation methods, the respective advantages can be fully utilized, the deficiencies of each other can be compensated, and the accuracy and reliability of the efficacy evaluation can be improved. In addition, before integrating the evaluation results, a consistency test can be performed on the colorimetric method efficacy evaluation result and the plant fluorescence spectroscopy method efficacy evaluation result to determine whether the two evaluation results are consistent or have a large deviation. If the two evaluation results have a large deviation, the reasons can be further analyzed, such as checking the experimental operations, instrument equipment, or environmental conditions, etc., to exclude abnormal situations and ensure the accuracy of the evaluation results.

[0074] Specifically, when the method of the present application conducts a dynamic evaluation of the herbicide efficacy, first, in step A1, field soil samples are collected and pretreated to obtain a color-developing solution that can reflect the herbicide residue amount in the soil, laying a foundation for the subsequent colorimetric method efficacy evaluation. In step A2, by obtaining the color characteristics of the color-developing solution and matching it with a preset colorimetric card, the colorimetric method efficacy evaluation based on the herbicide residue amount is realized, and the operation is simple and fast. At the same time, in step A3, the plant fluorescence spectroscopy technology is introduced to obtain the chlorophyll fluorescence spectrum of the weed leaves, and the efficacy evaluation result of the plant fluorescence spectroscopy method is determined by analyzing the spectral characteristic parameters, reflecting the influence of the herbicide on the plant physiological activity, so as to realize the efficacy evaluation from two levels of soil residue and plant physiology. More importantly, in step A4, a method for dynamically adjusting the evaluation result weight is proposed, the environmental parameters are collected in real time, and the reference weights of the colorimetric method and the plant fluorescence spectroscopy method evaluation results are dynamically adjusted according to the environmental parameters, so that the evaluation results can better adapt to different environmental conditions and improve the accuracy and reliability of the evaluation. Finally, in step A5, according to the adjusted reference weight, the evaluation results of the colorimetric method and the plant fluorescence spectroscopy method are integrated to obtain the final comprehensive efficacy evaluation result, realizing a comprehensive, accurate and dynamic field evaluation of the efficacy of the environmentally friendly herbicide. Thus, the herbicide efficacy dynamic evaluation method provided by the present application comprehensively considers the soil herbicide residue amount and the plant physiological state, and can dynamically adjust the evaluation strategy according to the environmental conditions, and can effectively overcome environmental interference and realize a rapid and accurate evaluation of the efficacy of the environmentally friendly herbicide.

[0075] Through the above technical solution, the present application can effectively overcome environmental interference, realize a rapid and accurate evaluation of the efficacy of the environmentally friendly herbicide, and provide technical support for the field application of the environmentally friendly herbicide.

[0076] In some possible embodiments, step A1 includes:

[0077] A101. Collect soil samples at multiple points in the field, mix them evenly according to the quartering method to obtain a mixed soil sample;

[0078] A102. Weigh a preset mass of the mixed soil sample, add a pretreatment solution, and extract the herbicide by ultrasonic oscillation to obtain a soil extract; the pretreatment solution contains acetonitrile, water, and formic acid, with a volume ratio of 4:5:0.1;

[0079] A103. Purify the soil extract using a solid-phase extraction column to remove pigments and humus and obtain a purified extract;

[0080] A104. Concentrate the purified extract by nitrogen blowing under nitrogen protection to obtain a concentrated extract;

[0081] A105. Dissolve the concentrated extract in a color reagent solution, react in the dark for a preset time to obtain a color solution.

[0082] Among them, in step A101, the collection of soil samples at multiple points in the field can cover different areas of the field and reduce the influence of local differences on the representativeness of the samples. The operation of mixing evenly according to the quartering method can ensure the uniformity and representativeness of the mixed soil sample.

[0083] Among them, in step A102, a preset mass of the mixed soil sample is weighed to ensure the basis for quantitative analysis of the experiment. Acetonitrile in the pretreatment solution is used to increase the solubility of the herbicide, water is used to assist in wetting the soil, and formic acid is used to adjust the pH value and improve the extraction efficiency. The volume ratio of 4:5:0.1 achieves an optimized extraction effect. Ultrasonic oscillation uses the energy of sound waves to accelerate the release of the herbicide from soil particles and improve the extraction speed and efficiency. Among them, the power, frequency, and oscillation time of ultrasonic oscillation can be set according to actual needs, for example, determined in advance through experiments according to the soil type and herbicide type.

[0084] Among them, in step A103, the solid-phase extraction column selectively adsorbs the target herbicide using the stationary phase material while removing impurities such as pigments and humus to achieve a purification effect.

[0085] Among them, in step A104, nitrogen protection avoids the oxidation of the sample during the concentration process. Nitrogen blowing concentration uses nitrogen to accelerate the volatilization of the solvent, enrich the herbicide, and improve the detection sensitivity.

[0086] Among them, in step A105, the concentrated extract is dissolved in the color reagent solution to provide reactants for the subsequent color reaction. Reacting in the dark for a preset time ensures the stable progress of the color reaction and reduces the interference of light. The color reagent can be selected according to the type of herbicide, and the preset time can be set according to actual needs, for example, determined in advance through experiments according to the type of herbicide.

[0087] Specifically, for the problem of representative field soil sample collection, this method adopts a mixed strategy of multi-point sampling and quartering method. In actual operation, the field is divided into multiple regions, and multiple soil samples are collected from each region. Soil samples from different regions are mixed, and then the quartering method is used, that is, the mixed sample is flattened into a square, divided into four parts, two diagonal parts are removed, and the remaining two parts are mixed. This process is repeated until the sample volume meets the experimental requirements, thus obtaining a representative mixed soil sample. To efficiently extract herbicides from the soil, a mixed solution containing acetonitrile, water, and formic acid is used as the pretreatment solution. The volume ratio of acetonitrile, water, and formic acid is precisely controlled at 4:5:0.1. This ratio has been optimized to effectively improve the extraction efficiency of the target herbicide. In the ultrasonic oscillation-assisted extraction process, the ultrasonic generator generates high-frequency vibrations, accelerating the penetration of the solvent into the soil matrix and promoting the rapid dissolution of the herbicide into the pretreatment solution. In the solid-phase extraction column purification step, a commonly used C18 solid-phase extraction column is utilized. By virtue of its adsorption capacity for non-polar or weakly polar organic compounds, it effectively removes interfering substances such as pigments and humus in the soil extract. In the nitrogen blowing concentration process, the extract is heated in a nitrogen atmosphere. Nitrogen accelerates the volatilization of the solvent and simultaneously inhibits the oxidation of the sample. Eventually, the volume of the extract is reduced, and the concentration of the herbicide is enriched. Before the color reaction, the concentrated extract is dissolved in a specific color reagent solution. For example, for sulfonylurea herbicides, sulfanilamide can be used as the color reagent, and the light-shielded reaction time is set to, for example, 30 minutes to ensure the full completion of the color reaction.

[0088] In some preferred embodiments, step A101 includes:

[0089] Based on the field terrain data, the target field is divided into multiple micro-topography units with an elevation difference less than a preset threshold; the field terrain data includes elevation data and slope data;

[0090] Within each micro-topography unit, multiple soil samples are randomly collected;

[0091] The soil samples within the same micro-topography unit are mixed evenly to obtain a representative soil sample for the corresponding micro-topography unit;

[0092] Using the quartering method, according to the area ratio of each micro-topography unit, the representative soil samples of each micro-topography unit are weighted and mixed to obtain a mixed soil sample.

[0093] Among them, in order to more accurately reflect the differences in soil under different terrain conditions, it is first necessary to obtain the terrain data of the field plots. The terrain data can include elevation data and slope data. The elevation data reflects the altitude of different positions in the field, and the slope data describes the degree of surface inclination. These data can be obtained through various methods such as lidar scanning carried by drones and measurement by RTK positioning equipment. Then, based on the obtained terrain data, the entire target field is divided into several small regional units. The basis for division is to ensure that the elevation difference within each micro-topography unit is controlled within a preset threshold range. The preset threshold can be flexibly set according to the actual application scenario and the complexity of the field terrain. For example, it can be set to 0.5 meters, 1 meter or other suitable values. Through this division method, it can be ensured that the terrain conditions within the same micro-topography unit are relatively consistent.

[0094] Among them, randomly collecting multiple soil samples within each micro-topography unit means collecting soil samples within each divided micro-topography unit. In order to ensure that the collected soil samples can represent the soil characteristics of the micro-topography unit, it is necessary to randomly select multiple sampling points within each micro-topography unit for sampling. The purpose of random sampling is to avoid sampling bias caused by human factors and improve the representativeness of the samples. The number of samples can be adjusted according to the size of the micro-topography unit and the uniformity of the soil. Usually, 3-5 soil samples can be collected for each micro-topography unit, or more. Sampling tools can be commonly used tools such as soil sampling drills and shovels. The sampling depth and method should be kept consistent to reduce the errors introduced during the sampling process.

[0095] Among them, mixing the soil samples within the same micro-topography unit evenly to obtain the representative soil sample of the corresponding micro-topography unit means fully mixing the multiple soil samples collected within the same micro-topography unit. The purpose of mixing is to eliminate the local differences in the soil within the micro-topography unit and obtain a sample that can represent the average soil condition of the entire micro-topography unit. The mixing method can use the quartering method, that is, pouring all the samples together, spreading them flat, then dividing them into four parts, taking the two diagonal parts for mixing, and repeating this step multiple times until the samples are evenly mixed. Professional equipment such as soil mixers can also be used for mixing. The mixed soil sample is the representative soil sample of the corresponding micro-topography unit and can reflect the soil characteristics of the micro-topography unit.

[0096] Among them, the quartering method is adopted to weight and mix the representative soil samples of each microtopographic unit according to the area proportion of each microtopographic unit, and the mixed soil sample is obtained. This means that after obtaining the representative soil samples of each microtopographic unit, these samples need to be mixed again to obtain the final mixed soil sample. This mixing is not a simple equal - quantity mixing, but rather takes into account the area proportion of each microtopographic unit in the entire field. For a microtopographic unit with a larger area proportion, the weight of its representative soil sample in the final mixed sample should be greater, and vice versa. The quartering method can be used for weighted mixing. The specific steps are as follows: First, calculate the proportion of the area of each microtopographic unit in the total area of the entire field, and then according to this proportion, take out the corresponding proportion of soil from the representative soil samples of each microtopographic unit. For example, if a microtopographic unit has an area proportion of 20%, then take out 20% of the amount from the representative soil sample of this unit. Mix the soil samples taken out from all microtopographic units together and use the quartering method to mix them evenly again to obtain the final mixed soil sample. This way of weighted mixing can ensure that the final mixed soil sample can more accurately reflect the soil conditions of the entire field.

[0097] Specifically, for the method for dynamically evaluating the herbicide efficacy proposed in this application, in the step of collecting field soil samples, in order to solve the problem that the traditional quartering mixing method cannot fully consider the topographic differences of the field plots, a specific improvement plan for step A101 is proposed. This plan first divides the target field into multiple micro-topographic units based on the field topographic data. When dividing, the elevation difference within the micro-topographic unit is less than the preset threshold as the standard, ensuring the relative consistency of the terrain within each micro-topographic unit. The advantage of doing this is that the field can be effectively managed in regions according to the terrain characteristics. Considering the influence of the terrain on the distribution of soil moisture and nutrients, the soil samples collected within each micro-topographic unit are more representative and can reflect the true soil conditions of this area. After the division of the micro-topographic units is completed, within each micro-topographic unit, multiple soil samples are randomly collected, and the soil samples within the same micro-topographic unit are mixed evenly to obtain the representative soil sample of this micro-topographic unit. Through random sampling and mixing within the unit, the influence of local soil differences on the representativeness of the samples is further reduced, and the sample quality is improved. Finally, in order to obtain a mixed soil sample that can represent the soil conditions of the entire field, the representative soil samples of each micro-topographic unit also need to be mixed. When mixing, the quartering method is used, and weighted mixing is carried out according to the area ratio of each micro-topographic unit. For the micro-topographic unit with a large area ratio, the proportion of its soil sample in the final mixed sample also increases accordingly. In this way, it is ensured that the final mixed soil sample can comprehensively reflect the soil characteristics of different topographic regions and more comprehensively and accurately represent the soil conditions of the entire field. Through this refined sub-unit sampling and weighted mixing method, the representativeness of soil sample collection can be effectively improved, laying a foundation for the accuracy of subsequent efficacy evaluation, and further improving the reliability of herbicide efficacy evaluation.

[0098] Through the above technical solution, this application can more accurately collect representative mixed soil samples, effectively overcoming the sampling deviation problem caused by ignoring the field topographic differences in the traditional sampling method, enabling the collected soil samples to more truly reflect the soil conditions of the entire field, and thus providing a more accurate sample basis for subsequent herbicide efficacy evaluation and improving the accuracy and reliability of the efficacy evaluation results.

[0099] In some embodiments, step A2 includes:

[0100] A201. Use an image acquisition device to acquire the image of the chromogenic solution, and use an adaptive threshold segmentation algorithm to segment the chromogenic region from the image of the chromogenic solution;

[0101] A202. For the segmented color development region, calculate the mean and standard deviation of the R, G, and B channels in the RGB color space, convert the RGB color space to the HSV color space, calculate the mean and standard deviation of the H, S, and V channels, obtain 12 color feature parameters, and form a color feature vector.

[0102] A203. Input the color feature vector into a pre-trained color matching model to obtain the standard color patch matching result output by the color matching model, which is used to determine the colorimetric method drug efficacy evaluation result.

[0103] Among them, in step A201, an image acquisition device is used to obtain an image of the color development solution, and the color development region is accurately segmented from the image through an adaptive threshold segmentation algorithm, which can avoid the interference of the background on the color feature extraction and ensure the accuracy of subsequent color feature extraction.

[0104] Among them, in step A202, for the segmented color development region, calculate the mean and standard deviation of the R, G, and B channels in the RGB color space, and also calculate the mean and standard deviation of the H, S, and V channels in the HSV color space, and extract a total of 12 color feature parameters to form a color feature vector. By extracting multi-dimensional color features in the RGB and HSV color spaces, the color information of the color development solution can be described more comprehensively and robustly, reducing the interference of factors such as light changes on color feature extraction and improving the discrimination ability of color features.

[0105] Among them, in step A203, the extracted color feature vector is input into a pre-trained color matching model, and the model is used for standard color patch matching. The drug efficacy evaluation value or evaluation level corresponding to the matched standard color patch is used as the colorimetric method drug efficacy evaluation result. By using a pre-trained color matching model, an accurate mapping between color features and drug efficacy evaluation results can be achieved, improving the objectivity and accuracy of colorimetric method drug efficacy evaluation. Among them, the color matching model can be trained in the following way: Select linear regression, polynomial regression, support vector machine, or neural network, etc. as the color matching model, collect a data set containing multiple samples, where the samples include color feature vectors and corresponding standard color patch labels, and use the data set to train the selected color matching model to make the prediction error reach below the preset error threshold to obtain the trained color matching model.

[0106] Specifically, aiming at the problem of deviation in the colorimetric efficacy evaluation results caused by inaccurate extraction of color features, this solution accurately segments the color development area using the adaptive threshold segmentation algorithm in step A201 to reduce background interference and ensure the pertinence and accuracy of color feature extraction; extracts multi-dimensional color features such as mean and standard deviation in the RGB and HSV color spaces in step A202 to comprehensively and robustly describe the color information of the color development solution, reduce the interference of environmental factors such as uneven illumination, and improve the discrimination ability of color features; uses a pre-trained color matching model to perform standard color block matching in step A203 to achieve an accurate mapping from color features to the efficacy evaluation results and improve the objectivity and accuracy of colorimetric efficacy evaluation. Thus, through steps such as image segmentation, multi-dimensional color feature extraction, and model matching, the accurate acquisition of the color features of the color development solution and the reliable determination of the colorimetric efficacy evaluation results are realized, effectively solving the problem of deviation in the colorimetric efficacy evaluation results caused by inaccurate extraction of color features.

[0107] In some specific embodiments, the adaptive threshold segmentation algorithm is configured as the OTSU algorithm, and the color matching model is configured as a support vector machine model. After collecting the color development solution image, the OTSU algorithm is used to process the color development solution image, automatically calculate the optimal threshold, and segment the color development area from the background based on the optimal threshold. For the segmented color development area, the mean of the R channel, the mean of the G channel, the mean of the B channel, the standard deviation of the R channel, the standard deviation of the G channel, the standard deviation of the B channel in the RGB color space, and the mean of the H channel, the mean of the S channel, the mean of the V channel, the standard deviation of the H channel, the standard deviation of the S channel, and the standard deviation of the V channel in the HSV color space are calculated respectively, a total of 12 color feature parameters, which constitute the color feature vector. The color feature vector is input into the pre-trained support vector machine model, and the support vector machine model outputs the standard color block matching result, which is used to determine the colorimetric efficacy evaluation result. Using the OTSU algorithm can adaptively segment the color development area, reduce manual intervention, and improve the segmentation accuracy and efficiency. Using the support vector machine model as the color matching model can effectively process high-dimensional color feature vectors, realize the non-linear mapping between color features and efficacy evaluation results, and improve the accuracy and robustness of color matching.

[0108] In some preferred embodiments, after step A202 and before step A203, the following steps are further included:

[0109] A204. Real-time collect the light intensity in the field and calculate the average light intensity within the first preset time window;

[0110] A205. According to the average light intensity, perform light correction on the color feature vector using a polynomial regression model.

[0111] Among them, step A204 can be specifically implemented in the following way: Use a light intensity sensor to monitor the light intensity of the field environment in real time. The light intensity sensor can be selected as a silicon photocell or a photodiode, etc. The arrangement position of the light intensity sensor can be close to the color solution image acquisition area to ensure that the collected light intensity data is as consistent as possible with the light conditions during the color solution image acquisition. The first preset time window can be set to 1 minute, 5 minutes, 10 minutes, etc. The length of the time window can be adjusted according to the light change frequency of the actual application scenario. The average light intensity can be obtained by calculating the arithmetic mean of the light intensity data collected within the preset time window. This can effectively filter out the influence of instantaneous light fluctuations and obtain a relatively stable light intensity representation value, providing a reliable data basis for subsequent light correction.

[0112] Among them, step A205 can be specifically implemented in the following way: First, it is necessary to pre-establish a polynomial regression model. The input of the polynomial regression model is the average light intensity and the initial color feature vector, and the output is the corrected color feature vector. The order of the polynomial regression model can be determined according to the complexity of the nonlinear relationship between the light intensity and the color feature vector. For example, a quadratic or cubic polynomial regression model can be selected. The parameters of the polynomial regression model can be obtained through experimental calibration. The calibration experiment can collect color solution images under different light intensity conditions, extract the color feature vector, and then use the collected data for model training to complete the determination of the model parameters. In actual application, according to the average light intensity calculated in step A204 and the color feature vector calculated in step A202, substitute them into the pre-established polynomial regression model, and the corrected color feature vector can be calculated. Through the polynomial regression model for light correction, the interference of light intensity changes on the color feature vector can be effectively eliminated, improving the robustness and accuracy of the color feature. Specifically, the polynomial regression model can be expressed as:

[0113] C1_i = a0_i + a1_i * I + a2_i * I^2 +... + an_i * I^n + b1_i * C0_i;

[0114] i = 1, 2,..., 12;

[0115] Among them, C0_i is the i-th color feature parameter in the initial color feature vector, C1_i is the i-th color feature parameter in the corrected color feature vector, I is the average light intensity, a0_i to an_i and b1_i are the model parameters corresponding to the i-th color feature parameter, n is the order of the polynomial regression model. For example, for a quadratic polynomial regression model, n = 2, and for a cubic polynomial regression model, n = 3.

[0116] Thus, through the synergistic effect of step A204 and step A205, under complex and variable field lighting conditions, effective lighting correction of the color feature vector can be achieved, reducing the interference of lighting condition changes on the efficacy evaluation results, and improving the accuracy and reliability of the colorimetric method for efficacy evaluation.

[0117] Specifically, aiming at the technical problem that the lighting conditions in the field environment are complex and variable, and the unstable lighting intensity will interfere with the extraction of color features of the color solution image, thereby affecting the accuracy of the efficacy evaluation results. Before performing the color matching model after extracting the color feature vector, this application adds a lighting correction step. First, through step A204, the lighting intensity in the field is collected in real time, and the average lighting intensity within the first preset time window is calculated. This can obtain the average lighting level during color feature extraction, providing a data basis for subsequent lighting correction. Then, in step A205, according to the average lighting intensity, the polynomial regression model is used to perform lighting correction on the color feature vector. The polynomial regression model can better fit the non-linear relationship between the lighting intensity and the color features, thereby effectively eliminating the influence of lighting intensity changes on the color feature vector, improving the robustness and accuracy of the color features, and further enhancing the reliability of the colorimetric method for efficacy evaluation.

[0118] Through the above technical solution, this application can effectively eliminate the interference of lighting intensity changes on the color feature vector under complex and variable field lighting conditions, improve the robustness and accuracy of the color features, and further enhance the reliability of the colorimetric method for efficacy evaluation, enabling the efficacy evaluation results to more accurately reflect the true efficacy level of the herbicide, and providing more reliable data support for subsequent comprehensive efficacy evaluation and drug use decision-making.

[0119] In some embodiments, step A3 includes:

[0120] A301. Using a plant fluorescence spectrometer to obtain the chlorophyll fluorescence spectrum data generated by weed leaves under the excitation spectrum irradiation to obtain the original fluorescence spectrum;

[0121] A302. Preprocessing the original fluorescence spectrum to obtain the preprocessed fluorescence spectrum; the preprocessing includes noise removal, background fluorescence correction, and smoothing processing;

[0122] A303. Extracting multiple fluorescence spectrum characteristic parameters from the preprocessed fluorescence spectrum; the fluorescence spectrum characteristic parameters include the F685 / F730 ratio, the maximum photochemical efficiency of photosystem II, and the actual photochemical efficiency of photosystem II;

[0123] A304. Inputting the extracted fluorescence spectrum characteristic parameters into a pre-established efficacy evaluation model to obtain the plant fluorescence spectrometry efficacy evaluation result output by the efficacy evaluation model.

[0124] Among them, in step A301, a plant fluorescence spectrometer is used to obtain the chlorophyll fluorescence spectral data generated by weed leaves under the excitation spectrum irradiation, and the original fluorescence spectrum is obtained. This is the basis and premise for subsequent spectral analysis and efficacy evaluation. Specifically, it can be achieved in the following ways: Use a portable plant fluorescence spectrometer to collect spectral data in the field to ensure the real-time and on-site nature of the spectral data. In terms of the parameter settings of the spectrometer, the spectral parameters of the excitation spectrum can be adjusted according to the weed species and growth stage to achieve the self-adaptability of spectral collection. During the spectral data collection process, the probe of the spectrometer can be aligned with the weed leaves to ensure the collection of high-quality chlorophyll fluorescence spectral data.

[0125] Among them, in step A302, pretreatment can effectively improve the quality of the fluorescence spectral data, reduce the interference of environmental factors and instrument errors on the evaluation results, and thus make the subsequent feature parameter extraction and efficacy evaluation more accurate and reliable. Specifically, it can be achieved in the following ways: Digital filtering algorithms such as median filtering, moving average filtering or wavelet denoising can be used to remove noise and filter out the random noise in the spectral data. Baseline correction algorithms such as polynomial fitting baseline correction, differential spectroscopy or standard sample method can be used for background fluorescence correction to eliminate the influence of background fluorescence signals on the analysis results. Spectral smoothing algorithms such as Savitzky-Golay smoothing, Gaussian smoothing or wavelet threshold smoothing can be used for smoothing processing to reduce the high-frequency noise in the spectral data and improve the signal-to-noise ratio of the spectrum. Further, the pretreatment operations can be carried out in sequence according to the order of removing noise, background fluorescence correction and smoothing processing to ensure the effect of pretreatment.

[0126] Among them, the multiple fluorescence spectral characteristic parameters extracted in step A303 are all key indicators characterizing the physiological state of plants and the photosynthesis efficiency, which can effectively reflect the impact degree of herbicides on weeds and provide a scientific basis for the efficacy evaluation. The following methods can be specifically used to achieve this: The F685 / F730 ratio refers to the intensity ratio of the fluorescence spectrum at wavelengths of 685 nm and 730 nm, which can reflect the chlorophyll content of plants and the health status of the photosynthetic apparatus. A decrease in the ratio usually indicates that the plant is under stress or damaged. The maximum photochemical efficiency of photosystem II (Fv / Fm) refers to the maximum photochemical efficiency that photosystem II can achieve under dark adaptation conditions of the plant, reflecting the potential activity of photosystem II. A decrease in the value indicates that photosystem II is inhibited. The actual photochemical efficiency of photosystem II (ΦPSII) refers to the actual photochemical efficiency of photosystem II under light conditions of the plant, reflecting the working state of photosystem II in the actual environment. A decrease in the value indicates a decrease in the light energy conversion efficiency of photosystem II. Further, during the extraction process of the characteristic parameters, a program can be written using spectral analysis software or programming languages (such as Matlab, Python, etc.) to achieve the automatic extraction and calculation of the characteristic parameters, improving the efficiency and accuracy.

[0127] Among them, in step A304, through the efficacy evaluation model, the quantitative evaluation of the efficacy can be achieved, improving the objectivity and accuracy of the evaluation results. The following methods can be specifically used to achieve this: The efficacy evaluation model can adopt a multiple linear regression model, a support vector machine model, a neural network model or other machine learning models. The input of the model is the extracted fluorescence spectral characteristic parameters, and the output is the efficacy evaluation result. The establishment of the efficacy evaluation model can be based on a large amount of herbicide efficacy test data. Through model training and parameter optimization, an efficacy evaluation model with good performance can be obtained. The output result of the efficacy evaluation model can be an efficacy grade, an efficacy percentage or other quantitative indicators that can characterize the efficacy degree.

[0128] Thus, through steps A301 to A304, the accurate acquisition of the efficacy evaluation result of the plant fluorescence spectrometry can be achieved, providing a reliable basis for the subsequent comprehensive efficacy evaluation. Moreover, step A302 preprocesses the original fluorescence spectrum, step A303 defines the fluorescence spectral characteristic parameters, and step A304 constructs the efficacy evaluation model, which are closely linked, making the efficacy evaluation process more standardized and refined, and the evaluation results more accurate and reliable.

[0129] Specifically, the present application defines the process of obtaining the efficacy evaluation results by plant fluorescence spectrometry. First, a chlorophyll fluorescence spectral data of weed leaves is obtained by using a plant fluorescence spectrometer to obtain an original fluorescence spectrum, which is the basic data for efficacy evaluation. Then, the original fluorescence spectrum is preprocessed, including noise removal, background fluorescence correction and smoothing processing, to improve the quality of spectral data and reduce interference factors. Next, multiple characteristic parameters such as the F685 / F730 ratio, the maximum photochemical efficiency of photosystem II, and the actual photochemical efficiency of photosystem II are extracted from the preprocessed spectrum. These parameters can reflect the effects of herbicides on the physiological state and photosynthesis of weeds. Finally, the extracted characteristic parameters are input into a pre-established efficacy evaluation model, and the model outputs the efficacy evaluation results by plant fluorescence spectrometry, realizing the quantitative evaluation of efficacy. Through the above steps, the efficacy evaluation process by plant fluorescence spectrometry is standardized and refined, the accuracy and reliability of the evaluation results are improved, the subjectivity of the traditional manual visual inspection method and the time-consuming and laborious deficiencies of the laboratory detection method are overcome, and the requirements for rapid and accurate evaluation of efficacy in the field are met.

[0130] Through the above technical solutions, the present application defines the specific steps of the efficacy evaluation by plant fluorescence spectrometry, realizing the standardization and refinement of the efficacy evaluation process, and improving the accuracy and reliability of the efficacy evaluation results. By preprocessing the original fluorescence spectrum, the quality of spectral data is effectively improved, and the interference of environmental factors and instrument errors is reduced. By extracting multiple key fluorescence spectral characteristic parameters, the effects of herbicides on the physiological state of weeds are comprehensively reflected. By constructing an efficacy evaluation model, the quantitative evaluation of efficacy is realized, and the objectivity of the evaluation results is improved. Therefore, the efficacy evaluation method by plant fluorescence spectrometry provided by the present application can overcome the deficiencies of traditional methods and provide an effective technical means for the rapid and accurate evaluation of the field efficacy of environmentally friendly herbicides.

[0131] Preferably, step A301 includes:

[0132] Determining the weed species and growth stage information of the weed to be measured based on image recognition technology;

[0133] Adjusting the spectral parameters of the excitation spectrum according to the weed species and growth stage information;

[0134] Using a plant fluorescence spectrometer, irradiating the weed leaves based on the adjusted excitation spectrum, and obtaining the chlorophyll fluorescence spectral data generated by the weed leaves under the irradiation of the excitation spectrum to obtain the original fluorescence spectrum.

[0135] Among them, image recognition technology is used to automatically and quickly identify the types and growth stages of weeds in the field. Specifically, after the image acquisition device obtains the weed leaf images, the system analyzes the image texture, color, and shape features to determine the weed types, such as broad-leaved weeds or gramineous weeds. At the same time, the system evaluates growth indicators such as the number of weed leaves and plant height to determine the growth stage of the weeds, such as the seedling stage, growth stage, or maturity stage. Thus, the information on weed types and growth stages is accurately identified. Specifically, existing image recognition technology can be used to identify the information on weed types and growth stages, and no limitation is imposed here.

[0136] Furthermore, the system pre-stores the excitation spectral parameters corresponding to different weed types and growth stages. These parameters can include the spectral range and peak wavelength, and can also include light intensity. For example, for the seedling stage of broad-leaved weeds, the preset excitation spectral parameters are a spectral range of 400 - 700 nm and a peak wavelength of 450 nm; for the maturity stage of gramineous weeds, the preset excitation spectral parameters are a spectral range of 350 - 650 nm and a peak wavelength of 400 nm. After obtaining the information on weed types and growth stages, the spectral parameter adjustment module automatically calls the matching excitation spectral parameters and sends the parameters to the plant fluorescence spectrometer. Thus, the excitation light source of the plant fluorescence spectrometer is adjusted to a state adapted to the current weed characteristics.

[0137] Specifically, after receiving the adjusted excitation spectral parameters, the plant fluorescence spectrometer adjusts its light source output. For example, if the adjusted excitation spectral parameters are a spectral range of 400 - 700 nm and a peak wavelength of 450 nm, then the light source emission spectral range of the plant fluorescence spectrometer is 400 - 700 nm, and the light intensity is the largest at the wavelength of 450 nm. Subsequently, the adjusted excitation spectrum irradiates the surface of the weed leaves. After the chlorophyll molecules in the weed leaves absorb light energy, they will emit fluorescence. The spectral sensor of the plant fluorescence spectrometer receives and records the fluorescence signal emitted by the weed leaves to generate the original fluorescence spectral data. Since the excitation spectral parameters have been optimized and adjusted according to the weed types and growth stages, the collected original fluorescence spectral data can more accurately and sensitively reflect the physiological state of the weeds, providing a high-quality data basis for subsequent drug efficacy evaluation.

[0138] In some embodiments, step A4 includes:

[0139] A401. Real-time collect the soil humidity and light intensity in the field, and calculate the average soil humidity and average light intensity within the second preset time window;

[0140] A402. Calculate the weight adjustment coefficient for the colorimetric method's efficacy evaluation result and the weight adjustment coefficient for the plant fluorescence spectroscopy method's efficacy evaluation result based on the mean soil humidity and mean light intensity, according to the mapping relationship between environmental parameters and the reference weight adjustment coefficient.

[0141] A403. Adjust the reference weight of the colorimetric method's efficacy evaluation result and the reference weight of the plant fluorescence spectroscopy method's efficacy evaluation result respectively according to the weight adjustment coefficient for the colorimetric method's efficacy evaluation result and the weight adjustment coefficient for the plant fluorescence spectroscopy method's efficacy evaluation result.

[0142] Among them, in step A401, real-time collection means continuously collecting environmental parameter data during the efficacy evaluation process. Field soil humidity refers to the water content of the field soil, and light intensity refers to the light intensity level in the field. The second preset time window refers to a preset time period used to calculate the mean of environmental parameters, which can be set according to actual needs. For example, it can be set to 10 minutes, 20 minutes, or 30 minutes. Calculating the mean is to obtain representative environmental parameter values within the time window and reduce the interference of instantaneous environmental fluctuations on subsequent weight adjustment.

[0143] Among them, in step A402, the mapping relationship between environmental parameters and the reference weight adjustment coefficient is a pre-established correspondence between environmental parameter values and weight adjustment coefficients (which can be determined based on experimental data). This mapping relationship can be a functional relationship, a tabular relationship, or a rule relationship. Through the mapping relationship, corresponding weight adjustment coefficients can be determined according to different combinations of mean soil humidity and mean light intensity. The weight adjustment coefficient is used to adjust the reference weights of the evaluation results of the colorimetric method and the plant fluorescence spectroscopy method.

[0144] Among them, in step A403, adjusting the reference weight means dynamically adjusting the proportions of the evaluation results of the colorimetric method and the plant fluorescence spectroscopy method in the comprehensive efficacy evaluation. The adjustment can be to increase or decrease the reference weight (for example, increase or decrease based on a preset benchmark reference weight), and the adjustment range is determined by the weight adjustment coefficient. By adjusting the reference weight, the comprehensive efficacy evaluation result can more reasonably reflect the influence of environmental parameter changes.

[0145] Specifically, this solution aims to dynamically adjust the reference weights of the colorimetric method and the plant fluorescence spectroscopy method for the evaluation results of drug efficacy according to environmental parameters. First, the average values of soil humidity and light intensity are collected and calculated in real time through step A401 to obtain representative values of the current environmental parameters. Then, in step A402, using the pre-set mapping relationship between environmental parameters and reference weight adjustment coefficients, the weight adjustment coefficients of the two evaluation methods are calculated based on the average environmental parameters. The design of this mapping relationship takes into account the differential impacts that environmental parameters may have on different drug efficacy evaluation methods. For example, when the soil humidity is relatively high, the evaluation results of the colorimetric method for soil sampling may be interfered, and at this time, the weight adjustment coefficient of the colorimetric method can be reduced through the mapping relationship. Finally, in step A403, according to the weight adjustment coefficients, the reference weights of the two evaluation methods are adjusted respectively, so that the reference weights can vary dynamically according to real-time environmental parameters. Thus, the comprehensive evaluation result of drug efficacy can better adapt to environmental changes, and the accuracy and reliability of the evaluation result are improved.

[0146] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0147] The environmental parameter acquisition device includes a soil humidity sensor and a light intensity sensor, which are used to collect the soil humidity and light intensity in the field in real time. The second preset time window is set to 30 minutes. In step A402, the mapping relationship between environmental parameters and reference weight adjustment coefficients is pre-set as a two-dimensional lookup table. The row index of the lookup table is the average soil humidity, the column index is the average light intensity, and the values in the table are the weight adjustment coefficients of the colorimetric method and the weight adjustment coefficients of the plant fluorescence spectroscopy method. For example, when the average soil humidity is at a high level (such as higher than 80%) and the average light intensity is at a low level (such as lower than 20000 Lux), in the corresponding table entry of the lookup table, the weight adjustment coefficient of the colorimetric method is set to 0.7, and the weight adjustment coefficient of the plant fluorescence spectroscopy method is set to 1.3. In step A403, the reference weights are adjusted by multiplication, that is, the original reference weights of the colorimetric method and the plant fluorescence spectroscopy method are multiplied by the corresponding weight adjustment coefficients respectively to obtain the adjusted reference weights.

[0148] Through the above technical solution, this application can dynamically adjust the reference weights of the evaluation results of the colorimetric method for drug efficacy and the plant fluorescence spectroscopy method for drug efficacy according to environmental parameters such as soil humidity and light intensity collected in real time, so that the comprehensive evaluation result of drug efficacy can more accurately reflect the true drug efficacy of environmentally friendly herbicides, reduce the interference of environmental factors on the evaluation results of the colorimetric method and the plant fluorescence spectroscopy method for drug efficacy, and improve the accuracy and reliability of drug efficacy evaluation.

[0149] In some embodiments, step A5 includes:

[0150] A501. Calculate the difference (absolute value difference) between the evaluation result of the colorimetric method for drug efficacy and the evaluation result of the plant fluorescence spectroscopy method for drug efficacy, compare this difference with the preset deviation threshold, and determine whether the evaluation results conflict;

[0151] A502. If the evaluation results conflict, then based on the historical drug efficacy evaluation data, determine the drug efficacy evaluation correction rule corresponding to the current evaluation result, and according to the determined drug efficacy evaluation correction rule, correct the evaluation result of the colorimetric method for drug efficacy and the evaluation result of the plant fluorescence spectroscopy method for drug efficacy respectively, to obtain the final evaluation result of the colorimetric method for drug efficacy and the final evaluation result of the plant fluorescence spectroscopy method for drug efficacy;

[0152] A503. If the evaluation results do not conflict, then use the original evaluation result of the colorimetric method for drug efficacy and the original evaluation result of the plant fluorescence spectroscopy method for drug efficacy as the final evaluation result of the colorimetric method for drug efficacy and the final evaluation result of the plant fluorescence spectroscopy method for drug efficacy;

[0153] A504. According to the adjusted reference weight, perform weighted synthesis on the final evaluation result of the colorimetric method for drug efficacy and the final evaluation result of the plant fluorescence spectroscopy method for drug efficacy, to obtain the comprehensive evaluation result of drug efficacy.

[0154] Among them, in step A501, the preset deviation threshold can be set according to the actual application scenario and the evaluation accuracy requirement. For example, the preset deviation threshold can be set to several times the standard deviation of the evaluation result of the colorimetric method for drug efficacy or the evaluation result of the plant fluorescence spectroscopy method for drug efficacy. By setting the deviation threshold, it is possible to effectively identify the situation where the results of the two evaluation methods differ greatly, lay a foundation for subsequent conflict handling, and avoid deviation in the final evaluation caused by directly synthesizing conflicting evaluation results.

[0155] Among them, step A502 refers to the corrective measures taken when it is determined in step A501 that the evaluation results are in conflict. Specifically, a historical drug efficacy evaluation database can be established, which stores historical data of multiple past drug efficacy evaluations, including colorimetric method drug efficacy evaluation results, plant fluorescence spectroscopy drug efficacy evaluation results, as well as corresponding environmental parameters, drug types, and other information. When the evaluation results are in conflict, the historical drug efficacy evaluation database can be retrieved to find historical evaluation data similar to the current evaluation results (e.g., current environmental parameters, drug types, etc.), and based on these historical data, the deviation rules between the colorimetric method drug efficacy evaluation results and the plant fluorescence spectroscopy drug efficacy evaluation results can be analyzed, thereby formulating corresponding drug efficacy evaluation correction rules. The drug efficacy evaluation correction rules can be in various forms. For example, they can be correction formulas based on regression models or correction strategies based on expert experience. After determining the drug efficacy evaluation correction rules, according to these rules, the original colorimetric method drug efficacy evaluation results and the plant fluorescence spectroscopy drug efficacy evaluation results are respectively corrected to obtain the final evaluation results. Introducing historical drug efficacy evaluation data can provide a reference basis for correcting the evaluation results, making the correction process more scientific and reasonable. Through correction, the deviation of the evaluation results can be effectively reduced or eliminated, improving the accuracy of the evaluation results.

[0156] Among them, step A503 refers to the processing method adopted when it is determined in step A501 that the evaluation results are not in conflict. If the difference between the two evaluation results is less than the preset deviation threshold, it indicates that there is no obvious conflict between the two evaluation results, and the credibility of the evaluation results themselves is relatively high. At this time, the original colorimetric method drug efficacy evaluation results and the original plant fluorescence spectroscopy drug efficacy evaluation results can be directly used as the final evaluation results, ensuring that no additional modification is made to the evaluation results when the credibility of the evaluation results themselves is relatively high, and ensuring the objectivity of the evaluation results.

[0157] Among them, in step A504, according to the adjusted reference weights in step A4, the final colorimetric method drug efficacy evaluation results and the final plant fluorescence spectroscopy drug efficacy evaluation results are weighted and synthesized to obtain the comprehensive drug efficacy evaluation results. By adopting the method of weighted synthesis, the roles and contributions of different evaluation methods in drug efficacy evaluation can be fully considered, making the final comprehensive drug efficacy evaluation results more comprehensive and accurate.

[0158] Specifically, in view of the possible conflict between the evaluation results of the colorimetric method for drug efficacy and the evaluation results of the plant fluorescence spectroscopy method for drug efficacy, this solution proposes specific solutions to ensure the accuracy and reliability of the comprehensive evaluation results of drug efficacy. First, in step A501, by calculating the difference between the two evaluation results and comparing it with a preset deviation threshold, it is determined whether the evaluation results conflict. If the evaluation results conflict, then step A502 is entered, and based on historical drug efficacy evaluation data, a drug efficacy evaluation correction rule is determined, and the evaluation results are corrected. If the evaluation results do not conflict, then step A503 is entered, and the original evaluation results are directly adopted. Finally, in step A504, according to the adjusted reference weights, the final evaluation results are weighted and synthesized to obtain the comprehensive evaluation results of drug efficacy. Through the above steps, this solution can effectively handle the possible conflict situations between the evaluation results of the colorimetric method for drug efficacy and the evaluation results of the plant fluorescence spectroscopy method for drug efficacy, avoid the deviation of the comprehensive evaluation results of drug efficacy caused by directly synthesizing conflicting evaluation results, and ensure the accuracy and reliability of the comprehensive evaluation results of drug efficacy.

[0159] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0160] Set the preset deviation threshold to 0.2. In step A501, calculate the difference between the evaluation result of the colorimetric method for drug efficacy (assumed to be 0.7) and the evaluation result of the plant fluorescence spectroscopy method for drug efficacy (assumed to be 0.3), and get 0.4. Since 0.4 is greater than the preset deviation threshold of 0.2, it is determined that the evaluation results conflict. Enter step A502, and based on historical drug efficacy evaluation data, determine the drug efficacy evaluation correction rule as: reduce the evaluation result of the colorimetric method for drug efficacy by 0.1, and increase the evaluation result of the plant fluorescence spectroscopy method for drug efficacy by 0.1. According to this correction rule, correct the evaluation result of the colorimetric method for drug efficacy to 0.6, and correct the evaluation result of the plant fluorescence spectroscopy method for drug efficacy to 0.4, to obtain the final evaluation result of the colorimetric method for drug efficacy of 0.6 and the final evaluation result of the plant fluorescence spectroscopy method for drug efficacy of 0.4. In step A504, assume that the reference weight of the adjusted evaluation result of the colorimetric method for drug efficacy is 0.6, and the reference weight of the evaluation result of the plant fluorescence spectroscopy method for drug efficacy is 0.4. Then, according to the adjusted reference weights, perform weighted synthesis on the final evaluation result of the colorimetric method for drug efficacy of 0.6 and the final evaluation result of the plant fluorescence spectroscopy method for drug efficacy of 0.4, and obtain the comprehensive evaluation result of drug efficacy as: 0.6 * 0.6 + 0.4 * 0.4 = 0.52.

[0161] Through the above technical solution, in the process of comprehensive evaluation of drug efficacy, this application can effectively solve the possible conflict problems between the evaluation results of the colorimetric method for drug efficacy and the evaluation results of the plant fluorescence spectroscopy method for drug efficacy, avoid the deviation of the comprehensive evaluation results of drug efficacy caused by the conflict of evaluation results, ensure the accuracy and reliability of the comprehensive evaluation results of drug efficacy, and thus provide a more scientific and effective technical means for the dynamic evaluation of the drug efficacy of environmentally friendly herbicides.

[0162] Reference Figure 2 , this application provides a dynamic evaluation system for herbicide efficacy, which is used to evaluate the efficacy of an environmentally friendly herbicide after it is applied. The system includes:

[0163] An image acquisition device 1, which is used to acquire the image of the chromogenic solution; the chromogenic solution is obtained by collecting field soil samples, extracting the herbicide in the field soil samples, and adding a chromogenic agent for a chromogenic reaction;

[0164] A plant fluorescence spectrometer 2, which is used to measure the chlorophyll fluorescence spectrum of weed leaves;

[0165] An environmental parameter acquisition device 3, which is used to acquire environmental parameters in real time; the environmental parameters include soil humidity and light intensity;

[0166] A host computer 4, which is used to perform the following steps:

[0167] Extract the color characteristics of the chromogenic solution from the chromogenic solution image, and use them to match the standard color blocks of a preset color comparison card to obtain the efficacy evaluation result by colorimetry;

[0168] Analyze the fluorescence spectrum characteristic parameters of the chlorophyll fluorescence spectrum, and determine the efficacy evaluation result by plant fluorescence spectrometry according to the fluorescence spectrum characteristic parameters;

[0169] Dynamically adjust the reference weights of the efficacy evaluation result by colorimetry and the efficacy evaluation result by plant fluorescence spectrometry according to the current environmental parameters;

[0170] According to the adjusted reference weights, comprehensively combine the efficacy evaluation result by colorimetry and the efficacy evaluation result by plant fluorescence spectrometry to obtain the comprehensive efficacy evaluation result.

[0171] Among them, the foregoing dynamic evaluation method for herbicide efficacy can be implemented based on this dynamic evaluation system for herbicide efficacy.

[0172] Among them, the image acquisition device 1 can be a CCD camera or a CMOS camera, etc., which is installed at a position convenient for photographing the chromogenic solution, such as fixed on a tripod or integrated into a portable device.

[0173] Among them, the plant fluorescence spectrometer 2 can be a portable or handheld fluorescence spectrometer for easy field operation. The spectral probe of the plant fluorescence spectrometer is adjusted to be aligned with the weed leaves to ensure accurate collection of fluorescence signals.

[0174] Among them, the environmental parameter acquisition device 3 integrates multiple sensors for real-time monitoring of field environmental parameters. The environmental parameter acquisition device 3 includes, but is not limited to, a soil humidity sensor and a light intensity sensor. The soil humidity sensor can be a TDR soil humidity sensor or a capacitive soil humidity sensor, and the light intensity sensor can be a silicon photovoltaic cell light intensity sensor or a photodiode light intensity sensor. The environmental parameter acquisition device 3 is placed at a representative position in the field to accurately reflect the environmental conditions of the evaluation area.

[0175] Among them, the host computer 4 is the control center and data processing unit of the system. The host computer 4 can be a high-performance computer or an embedded industrial control computer. The host computer 4 is pre-installed with a drug efficacy evaluation software, which includes functional modules such as an image processing module, a spectral analysis module, an environmental parameter processing module, a weight adjustment module, and a comprehensive evaluation module. The host computer 4 is connected to the image acquisition device 1, the plant fluorescence spectrometer 2, and the environmental parameter acquisition device 3 by wired or wireless means to achieve efficient data transmission and centralized processing.

[0176] When the host computer 4 executes the steps, first, it obtains the chromogenic solution image through the image acquisition device 1, and uses an image processing algorithm, such as an adaptive threshold segmentation algorithm, to accurately segment the chromogenic region from the chromogenic solution image. Then, it extracts the color features of the chromogenic region. The color features include the mean and standard deviation in the RGB color space and the HSV color space. The color feature vector is input into a pre-trained color matching model, which can be a support vector machine or a neural network, to obtain the standard color block matching result and determine the colorimetric method efficacy evaluation result. At the same time, the plant fluorescence spectrometer 2 collects the chlorophyll fluorescence spectrum data of the weed leaves. The host computer 4 preprocesses the original fluorescence spectrum, and the preprocessing includes noise removal, background fluorescence correction, and smoothing. Then, it extracts the fluorescence spectrum characteristic parameters from the preprocessed fluorescence spectrum. The fluorescence spectrum characteristic parameters include the F685 / F730 ratio, the maximum photochemical efficiency of photosystem II, and the actual photochemical efficiency of photosystem II. The fluorescence spectrum characteristic parameters are input into a pre-established efficacy evaluation model, which can be a partial least squares regression model or a BP neural network model, to obtain the plant fluorescence spectrometry efficacy evaluation result. In addition, the environmental parameter acquisition device 3 collects environmental parameters such as the soil humidity and light intensity in the field in real time. The host computer 4 dynamically adjusts the reference weights of the colorimetric method efficacy evaluation result and the plant fluorescence spectrometry efficacy evaluation result based on the preset mapping relationship between the environmental parameters and the reference weight adjustment coefficient according to the current environmental parameters. The mapping relationship between the environmental parameters and the reference weight adjustment coefficient can be a linear function, a polynomial function, or a fuzzy rule. Finally, the host computer 4 performs weighted synthesis on the colorimetric method efficacy evaluation result and the plant fluorescence spectrometry efficacy evaluation result according to the adjusted reference weights to obtain the comprehensive efficacy evaluation result. The comprehensive efficacy evaluation result is output in the form of numbers, charts, or reports to provide intuitive efficacy evaluation information for users.

[0177] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For another example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

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

[0179] Furthermore, in each embodiment of this application, the various functional modules may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

[0180] In this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0181] The above description is only for the embodiments of this application and is not intended to limit the protection scope of this application. For those skilled in the art, this application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. A method for dynamically evaluating the efficacy of a herbicide, which is used to evaluate the efficacy of an environmentally friendly herbicide after the application of the environmentally friendly herbicide, and is characterized in that, The steps of this method include: A1. Collect field soil samples, extract herbicides from the field soil samples, add a color reagent for a color reaction to obtain a color solution; A2. Obtain the color characteristics of the color solution to match with the standard color patches of a preset color comparison card, and obtain the evaluation result of the efficacy by colorimetry; A3. Use a plant fluorescence spectrometer to obtain the chlorophyll fluorescence spectrum of weed leaves, analyze the fluorescence spectrum characteristic parameters of the chlorophyll fluorescence spectrum, and determine the evaluation result of the efficacy by the plant fluorescence spectrometry according to the fluorescence spectrum characteristic parameters; A4. Collect environmental parameters in real time, and dynamically adjust the reference weights of the evaluation result of the efficacy by colorimetry and the evaluation result of the efficacy by the plant fluorescence spectrometry according to the current environmental parameters; the environmental parameters include soil humidity and light intensity; A5. According to the adjusted reference weights, comprehensively combine the evaluation result of the efficacy by colorimetry and the evaluation result of the efficacy by the plant fluorescence spectrometry to obtain a comprehensive evaluation result of the efficacy.

2. The dynamic evaluation method for herbicide efficacy according to claim 1, characterized in that, Step A1 includes: A101. Collect multi-point field soil samples, mix them evenly according to the quartering method to obtain a mixed soil sample; A102. Weigh a preset mass of the mixed soil sample, add a pretreatment solution, and extract the herbicide by ultrasonic oscillation to obtain a soil extract; the pretreatment solution contains acetonitrile, water and formic acid, and the volume ratio is 4:5:0.1; A103. Purify the soil extract using a solid-phase extraction column to remove pigments and humus to obtain a purified extract; A104. Concentrate the purified extract by nitrogen blowing under nitrogen protection to obtain a concentrated extract; A105. Dissolve the concentrated extract in a color reagent solution, and react in the dark for a preset time to obtain a color solution.

3. The dynamic evaluation method for herbicide efficacy according to claim 2, wherein, Step A101 includes: Based on the field terrain data, divide the target field into multiple micro-topography units with an elevation difference less than a preset threshold; the field terrain data includes elevation data and slope data; In each micro-topography unit, randomly collect multiple soil samples; Mix the soil samples within the same micro-topography unit evenly to obtain a representative soil sample corresponding to the micro-topography unit; Using the quartering method, perform weighted mixing on the representative soil samples of each micro-topography unit according to the area ratio of each micro-topography unit to obtain a mixed soil sample.

4. A method for dynamically evaluating the efficacy of a herbicide according to claim 1, characterized in that, Step A2 includes: A201. Use an image acquisition device to collect an image of the color solution, and use an adaptive threshold segmentation algorithm to segment the color area from the color solution image; A202. For the segmented color area, calculate the mean and standard deviation of the R, G, and B channels in the RGB color space, convert the RGB color space to the HSV color space, and calculate the mean and standard deviation of the H, S, and V channels to obtain 12 color characteristic parameters, which form a color characteristic vector; A203. Input the color characteristic vector into a pre-trained color matching model to obtain the standard color patch matching result output by the color matching model, and use it to determine the evaluation result of the efficacy by colorimetry.

5. A method for dynamically evaluating the efficacy of a herbicide according to claim 4, characterized in that After step A202 and before step A203, the steps also include: A204. Collect the light intensity in the field in real time, and calculate the average light intensity within the first preset time window; A205. According to the average light intensity, a polynomial regression model is used to perform light correction on the color feature vector.

6. The method for dynamically evaluating the efficacy of a herbicide according to claim 1, characterized in that Step A3 includes: A301. Using a plant fluorescence spectrometer to obtain the chlorophyll fluorescence spectral data generated by the weed leaves under the excitation spectrum irradiation, and obtaining the original fluorescence spectrum; A302. Preprocessing the original fluorescence spectrum to obtain the preprocessed fluorescence spectrum; the preprocessing includes noise removal, background fluorescence correction, and smoothing processing; A303. Extracting multiple fluorescence spectral characteristic parameters from the preprocessed fluorescence spectrum; the fluorescence spectral characteristic parameters include the F685 / F730 ratio, the maximum photochemical efficiency of photosystem II, and the actual photochemical efficiency of photosystem II; A304. Inputting the extracted fluorescence spectral characteristic parameters into a pre-established drug efficacy evaluation model to obtain the drug efficacy evaluation result of the plant fluorescence spectrometry output by the drug efficacy evaluation model.

7. The method for dynamically evaluating the efficacy of a herbicide according to claim 6, characterized in that, Step A301 includes: Determining the weed species and growth stage information of the measured weed based on image recognition technology; According to the weed species and growth stage information, adjusting the spectral parameters of the excitation spectrum; Using a plant fluorescence spectrometer, irradiating the weed leaves based on the adjusted excitation spectrum, and obtaining the chlorophyll fluorescence spectral data generated by the weed leaves under the excitation spectrum irradiation, and obtaining the original fluorescence spectrum.

8. A method for dynamically evaluating the efficacy of a herbicide according to claim 1, characterized in that, Step A4 includes: A401. Real-time collecting the soil humidity and light intensity in the field, and calculating the average soil humidity and the average light intensity within a second preset time window; A402. According to the average soil humidity and the average light intensity, based on the mapping relationship between the environmental parameters and the reference weight adjustment coefficient, calculating the weight adjustment coefficient of the colorimetric method drug efficacy evaluation result and the weight adjustment coefficient of the plant fluorescence spectrometry drug efficacy evaluation result; A403. According to the weight adjustment coefficient of the colorimetric method drug efficacy evaluation result and the weight adjustment coefficient of the plant fluorescence spectrometry drug efficacy evaluation result, respectively adjusting the reference weight of the colorimetric method drug efficacy evaluation result and the reference weight of the plant fluorescence spectrometry drug efficacy evaluation result.

9. A method for dynamically evaluating the efficacy of a herbicide according to claim 1, characterized in that, Step A5 includes: A501. Calculating the difference between the colorimetric method drug efficacy evaluation result and the plant fluorescence spectrometry drug efficacy evaluation result, comparing the difference with a preset deviation threshold, and judging whether the evaluation results conflict; A502. If the evaluation results conflict, then based on the historical drug efficacy evaluation data, determining the drug efficacy evaluation correction rule corresponding to the current evaluation result, and according to the determined drug efficacy evaluation correction rule, respectively correcting the colorimetric method drug efficacy evaluation result and the plant fluorescence spectrometry drug efficacy evaluation result to obtain the final colorimetric method drug efficacy evaluation result and the final plant fluorescence spectrometry drug efficacy evaluation result; A503. If the evaluation results do not conflict, then using the original colorimetric method drug efficacy evaluation result and the original plant fluorescence spectrometry drug efficacy evaluation result as the final colorimetric method drug efficacy evaluation result and the final plant fluorescence spectrometry drug efficacy evaluation result; A504. According to the adjusted reference weight, performing weighted synthesis on the final colorimetric method drug efficacy evaluation result and the final plant fluorescence spectrometry drug efficacy evaluation result to obtain the comprehensive drug efficacy evaluation result.

10. A herbicide efficacy dynamic evaluation system is used to evaluate the efficacy of an environmentally friendly herbicide after the application of the environmentally friendly herbicide. It is characterized in that, The system includes: An image acquisition device for acquiring images of a chromogenic solution; the chromogenic solution is obtained by collecting field soil samples, extracting herbicides from the field soil samples, and adding a chromogenic agent for a chromogenic reaction; A plant fluorescence spectrometer for measuring the chlorophyll fluorescence spectrum of weed leaves; An environmental parameter acquisition device for real-time acquisition of environmental parameters; the environmental parameters include soil humidity and light intensity; A host computer for performing the following steps: Extracting the color characteristics of the chromogenic solution from the image of the chromogenic solution, and matching them with the standard color blocks of a preset color comparison card to obtain a pharmacodynamic evaluation result by colorimetry; Analyzing the fluorescence spectrum characteristic parameters of the chlorophyll fluorescence spectrum, and determining a pharmacodynamic evaluation result by plant fluorescence spectrometry according to the fluorescence spectrum characteristic parameters; Dynamically adjusting the reference weights of the pharmacodynamic evaluation result by colorimetry and the pharmacodynamic evaluation result by plant fluorescence spectrometry according to the current environmental parameters; According to the adjusted reference weights, comprehensively combining the pharmacodynamic evaluation result by colorimetry and the pharmacodynamic evaluation result by plant fluorescence spectrometry to obtain a comprehensive pharmacodynamic evaluation result.

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