Method and system for dynamically evaluating pesticide effect of herbicide

Abstract

The invention belongs to the technical field of agriculture, and discloses a herbicide efficacy dynamic evaluation method and system, efficacy evaluation is carried out through two methods of a colorimetric method and a plant fluorescence spectrometry, weights of two evaluation results are dynamically adjusted according to environmental parameters, the two evaluation results are synthesized according to the weights to obtain a final efficacy evaluation result, and the efficacy evaluation result is evaluated according to the weight. The environment interference can be effectively overcome, and the accuracy and reliability of herbicide efficacy evaluation are improved.

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 susceptible to 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 timing, 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, thus 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 subtle changes in their physiological states may be masked, thereby 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 have 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: A1. Collect field soil samples, extract herbicides from the field soil samples, add a color reagent for a color reaction, and 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 herbicidal 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 herbicidal 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 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; 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.

[0007] Preferably, 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, and 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.

[0008] Preferably, 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; Randomly collect multiple soil samples within each micro-topography unit; Mix the soil samples within the same micro-topography unit evenly to obtain a representative soil sample corresponding to the micro-topography unit; Use the quartering method to 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.

[0009] Preferably, step A2 includes: 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; 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; 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.

[0010] Preferably, after step A202 and before step A203, the following steps are further included: A204. Real-time collect the light intensity in the field and calculate the average light intensity within the first preset time window; A205. According to the average light intensity, use a polynomial regression model to perform light correction on the color feature vector.

[0011] Preferably, step A3 includes: A301. Use a plant fluorescence spectrometer to obtain the chlorophyll fluorescence spectral data generated by the weed leaves under the excitation spectrum irradiation to obtain the original fluorescence spectrum; A302. Perform preprocessing on the original fluorescence spectrum to obtain the preprocessed fluorescence spectrum; the preprocessing includes noise removal, background fluorescence correction, and smoothing processing; A303. Extract multiple fluorescence spectral feature parameters from the preprocessed fluorescence spectrum; the fluorescence spectral feature parameters include the F685 / F730 ratio, the maximum photochemical efficiency of photosystem II, and the actual photochemical efficiency of photosystem II; A304. Input the extracted fluorescence spectral feature parameters into a pre-established drug efficacy evaluation model to obtain the plant fluorescence spectrometry drug efficacy evaluation result output by the drug efficacy evaluation model.

[0012] Preferably, step A301 includes: Based on image recognition technology, determine the weed species and growth stage information of the measured weeds; According to the weed species and growth stage information, adjust the spectral parameters of the excitation spectrum; Use a plant fluorescence spectrometer to irradiate the weed leaves based on the adjusted excitation spectrum to obtain the chlorophyll fluorescence spectral data generated by the weed leaves under the excitation spectrum irradiation to obtain the original fluorescence spectrum.

[0013] Preferably, step A4 includes: A401. Collect the soil humidity and light intensity in the field in real time, and calculate the average soil humidity and average light intensity within the second preset time window; A402. Based on the average soil humidity and average light intensity, and according to the mapping relationship between environmental parameters and reference weight adjustment coefficients, calculate the weight adjustment coefficient for the colorimetric method of efficacy evaluation and the weight adjustment coefficient for the plant fluorescence spectroscopy method of efficacy evaluation; A403. According to the weight adjustment coefficient for the colorimetric method of efficacy evaluation and the weight adjustment coefficient for the plant fluorescence spectroscopy method of efficacy evaluation, adjust the reference weight for the colorimetric method of efficacy evaluation and the reference weight for the plant fluorescence spectroscopy method of efficacy evaluation respectively.

[0014] Preferably, step A5 includes: A501. Calculate the difference between the colorimetric method of efficacy evaluation result and the plant fluorescence spectroscopy method of efficacy evaluation result, compare this difference with the preset deviation threshold, and determine whether the evaluation results conflict; A502. If the evaluation results conflict, then based on the historical efficacy evaluation data, determine the efficacy evaluation correction rule corresponding to the current evaluation results, and according to the determined efficacy evaluation correction rule, correct the colorimetric method of efficacy evaluation result and the plant fluorescence spectroscopy method of efficacy evaluation result respectively to obtain the final colorimetric method of efficacy evaluation result and the final plant fluorescence spectroscopy method of efficacy evaluation result; A503. If the evaluation results do not conflict, then use the original colorimetric method of efficacy evaluation result and the original plant fluorescence spectroscopy method of efficacy evaluation result as the final colorimetric method of efficacy evaluation result and the final plant fluorescence spectroscopy method of efficacy evaluation result; A504. According to the adjusted reference weight, perform weighted synthesis on the final colorimetric method of efficacy evaluation result and the final plant fluorescence spectroscopy method of efficacy evaluation result to obtain the comprehensive efficacy evaluation result.

[0015] In a second aspect, the present application provides an environmental - friendly herbicide efficacy dynamic evaluation system for evaluating the efficacy of an environmental - friendly herbicide after its application. The system includes: An image acquisition device for collecting the image of the chromogenic solution; the chromogenic solution is obtained by collecting a field soil sample, extracting the herbicide in the field soil sample, 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 collecting environmental parameters in real time; the environmental parameters include soil humidity and light intensity; An upper computer for performing the following steps: Extract the color characteristics of the chromogenic solution from the chromogenic solution image to match with the standard color blocks of a preset colorimetric card to obtain the colorimetric method of efficacy evaluation result; Analyze the fluorescence spectral characteristic parameters of the chlorophyll fluorescence spectrum, and determine the efficacy evaluation result of the plant fluorescence spectrometry according to the fluorescence spectral characteristic parameters; Dynamically adjust the reference weights of the colorimetric method efficacy evaluation result and the plant fluorescence spectrometry efficacy evaluation result according to the current environmental parameters; According to the adjusted reference weights, comprehensively combine the colorimetric method efficacy evaluation result and the plant fluorescence spectrometry efficacy evaluation result to obtain the comprehensive efficacy evaluation result.

[0016] Beneficial effects: A method and system for dynamically evaluating the efficacy of herbicides provided by this application evaluate the efficacy through two methods, namely the colorimetric method and the plant fluorescence spectrometry, and dynamically adjust the weights of the two evaluation results according to the environmental parameters, and comprehensively obtain the efficacy evaluation result, which can effectively overcome environmental interference and improve the accuracy and reliability of the herbicide efficacy evaluation. Description of the Drawings

[0017] Figure 1 It is a flowchart of the method for dynamically evaluating the efficacy of herbicides provided by the embodiment of this application.

[0018] Figure 2 It is a schematic structural diagram of the system for dynamically evaluating the efficacy of herbicides provided by the embodiment of this application.

[0019] Label description: 1. Image acquisition device; 2. Plant fluorescence spectrometer; 3. Environmental parameter acquisition device; 4. Host computer. Detailed Embodiments

[0020] Next, the technical solutions in this application will be clearly and completely described in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Usually, the components of this application described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of this application that is required to be protected, but only represents the selected embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0021] It should be noted that: Similar reference numerals and letters indicate 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 this application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0022] Reference Figure 1, this application proposes a method for dynamically evaluating the efficacy of herbicides, which is used to evaluate the efficacy of environmentally friendly herbicides after they are applied. The steps of this method include: A1. Collect soil samples in the field, extract the herbicides from the soil samples in the field, add a color reagent for a color reaction to obtain a color solution; A2. Obtain the color characteristics of the color solution, use them to match the standard color patches of a preset color comparison card, and obtain the efficacy evaluation result 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 efficacy evaluation result by the plant fluorescence spectrometry method according to the fluorescence spectrum characteristic parameters; 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; 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.

[0023] Among them, step A1 can be implemented by the following method: 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 method can be 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 herbicides 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 the color reagent is added. The selection of the color reagent depends on the type and properties of the herbicides 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 color solution with a color depth related to the herbicide concentration can be obtained.

[0024] Among them, in step A2, the acquisition of color features can be achieved in various ways. For example, the color of the color-developing solution can be visually observed and compared with a standard colorimetric card to determine which standard color block the color of the color-developing 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, can be used to collect the image of the color-developing solution, and then image processing software is used to analyze the color information of the image to extract color feature parameters. The color feature parameters can include numerical values in color spaces such as RGB values, HSV values, and Lab values, 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 preparing standard solutions and calibrating color measurement instruments to ensure the accuracy and reliability of the colorimetric card. By matching the color features of the color-developing solution with the standard color blocks of the preset colorimetric card, the herbicide concentration or efficacy level corresponding to the color-developing solution can be determined, thereby obtaining the colorimetric method efficacy evaluation result. The matching method can adopt visual comparison, color feature vector similarity calculation, or color matching model, etc.

[0025] Among them, in step A3, a plant fluorescence spectrometer is an instrument used to measure the chlorophyll fluorescence spectrum of plant leaves. Its working principle is to use excitation light of a specific wavelength to irradiate plant leaves. 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, thereby 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, which can reflect the degree of damage to the photosynthetic apparatus of plants. 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. By inputting the extracted fluorescence spectrum characteristic parameters into the efficacy evaluation model, the plant fluorescence spectrometry efficacy evaluation result can be obtained.

[0026] 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, the reference weights of the colorimetric method for efficacy evaluation and the plant fluorescence spectroscopy method for efficacy evaluation are dynamically adjusted. The principle of weight adjustment is that when the environmental conditions are not conducive to a certain evaluation method, the weight of this method is reduced, and the weight of the other method is increased, 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 evaluation result of the colorimetric method can be appropriately reduced, and the weight of the evaluation result of the plant fluorescence spectroscopy method can be increased. When the light intensity is too high or too low, the plant fluorescence spectroscopy method may be affected by the light conditions. At this time, the weight of the evaluation result of the plant fluorescence spectroscopy method can be appropriately reduced, and the weight of the evaluation result of the colorimetric method 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.

[0027] Among them, in step A5, after obtaining the evaluation results of the colorimetric method for efficacy evaluation and the plant fluorescence spectroscopy method for efficacy evaluation, according to the reference weights 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 evaluation result of the colorimetric method for efficacy evaluation can be set as w1, and the weight of the evaluation result of the plant fluorescence spectroscopy method for efficacy evaluation can be set as w2, and w1 + w2 = 1. Comprehensive efficacy evaluation result = w1 * evaluation result of the colorimetric method for efficacy evaluation + w2 * evaluation result of the plant fluorescence spectroscopy method for efficacy evaluation. By integrating the results of the two evaluation methods, the respective advantages can be fully utilized, the deficiencies of each other can be made up for, and the accuracy and reliability of the efficacy evaluation can be improved. In addition, before the comprehensive evaluation result, the consistency test can also be carried out on the evaluation results of the colorimetric method for efficacy evaluation and the plant fluorescence spectroscopy method for efficacy evaluation to judge whether the two evaluation results are consistent or there are large deviations. If the deviations between the two evaluation results are large, the reasons can be further analyzed, such as checking the experimental operation, instrument equipment, or environmental conditions, etc., to exclude abnormal situations and ensure the accuracy of the evaluation results.

[0028] Specifically, when the method of the present application conducts a dynamic evaluation of the herbicide efficacy, first, field soil samples are collected and pretreated through step A1 to obtain a color-developing solution that can reflect the herbicide residue amount in the soil, laying a foundation for subsequent colorimetric method efficacy evaluation. In step A2, the color characteristics of the color-developing solution are obtained and matched with a preset color comparison card to achieve colorimetric method efficacy evaluation based on the herbicide residue amount, with simple and rapid operation. At the same time, in step A3, a plant fluorescence spectroscopy technique is introduced to obtain the chlorophyll fluorescence spectrum of weed leaves, and the efficacy evaluation result of the plant fluorescence spectroscopy method is determined by analyzing the spectral characteristic parameters, reflecting the impact of the herbicide on the physiological activity of the plant, thereby realizing efficacy evaluation from two aspects of soil residue and plant physiology. More importantly, in step A4, a method for dynamically adjusting the weight of the evaluation result is proposed, environmental parameters are collected in real time, and the reference weights of the evaluation results of the colorimetric method and the plant fluorescence spectroscopy method are dynamically adjusted according to the environmental parameters, so that the evaluation result 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 weights, the evaluation results of the colorimetric method and the plant fluorescence spectroscopy method are comprehensively considered to obtain the final comprehensive efficacy evaluation result, realizing a comprehensive, accurate, and dynamic field evaluation of the efficacy of environmentally friendly herbicides. 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 environmental conditions, effectively overcoming environmental interference and realizing rapid and accurate evaluation of the efficacy of environmentally friendly herbicides.

[0029] Through the above technical solution, the present application can effectively overcome environmental interference, realize rapid and accurate evaluation of the efficacy of environmentally friendly herbicides, and provide technical support for the field application of environmentally friendly herbicides.

[0030] In some possible implementation manners, step A1 includes: A101. Collect field soil samples at multiple points, 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, with a volume ratio of 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-developing agent solution, react in the dark for a preset time to obtain a color-developing solution.

[0031] Among them, in step A101, the collection of multi-point field soil samples can cover different areas of the field, reducing the influence of local differences on the representativeness of the samples. The operation of mixing evenly by the quartering method can ensure the uniformity and representativeness of the mixed soil samples.

[0032] Among them, in step A102, a pre-set 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 to improve the extraction efficiency. The volume ratio of 4:5:0.1 achieves an optimized extraction effect. Ultrasonic oscillation uses acoustic energy to accelerate the release of the herbicide from soil particles, improving the extraction speed and efficiency. Among them, the power, frequency, and oscillation time of the ultrasonic oscillation can be set according to actual needs, for example, determined in advance through experiments according to the soil type and herbicide type.

[0033] 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, achieving a purification effect.

[0034] Among them, in step A104, nitrogen protection is used to prevent sample oxidation during the concentration process. Nitrogen blowing concentration uses nitrogen to accelerate the volatilization of the solvent, enriching the herbicide and improving the detection sensitivity.

[0035] 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 pre-set 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 pre-set time can be set according to actual needs, for example, determined in advance through experiments according to the herbicide type.

[0036] Specifically, for the problem of representativeness in 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. The 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 quantity 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. During 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 humic substances in the soil extract. In the nitrogen blowing concentration step, 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, sulfonylurea herbicides can use sulfanilamide 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.

[0037] In some preferred embodiments, step A101 includes: Based on the field terrain data, the target field is divided into multiple micro-topographic units with an elevation difference less than a preset threshold; the field terrain data includes elevation data and slope data; Within each micro-topographic unit, multiple soil samples are randomly collected; The soil samples within the same micro-topographic unit are mixed evenly to obtain a representative soil sample corresponding to the micro-topographic unit; Using the quartering method, according to the proportion of the area of each micro-topographic unit, the representative soil samples of each micro-topographic unit are weighted and mixed to obtain a mixed soil sample.

[0038] 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-topographic 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-topographic unit are relatively consistent.

[0039] Among them, randomly collecting multiple soil samples within each micro-topographic unit means collecting soil samples within each divided micro-topographic unit. In order to ensure that the collected soil samples can represent the soil characteristics of the micro-topographic unit, it is necessary to randomly select multiple sampling points within each micro-topographic 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-topographic unit and the uniformity of the soil. Usually, 3 - 5 soil samples, or more, can be collected from each micro-topographic unit. 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.

[0040] Among them, mixing the soil samples within the same micro-topographic unit evenly to obtain the representative soil sample of the corresponding micro-topographic unit means fully mixing the multiple soil samples collected within the same micro-topographic unit. The purpose of mixing is to eliminate the local differences in the soil within the micro-topographic unit and obtain a sample that can represent the average soil condition of the entire micro-topographic 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 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-topographic unit and can reflect the soil characteristics of the micro-topographic unit.

[0041] Among them, the quartile method is adopted to weight and mix the representative soil samples of each micro-topographic unit according to the area proportion of each micro-topographic unit. Obtaining a mixed soil sample means that after obtaining the representative soil samples of each micro-topographic unit, these samples need to be mixed again to obtain the final mixed soil sample. This mixing is not a simple equal-amount mixing, but rather the area proportion of each micro-topographic unit in the entire field should be considered. For a micro-topographic 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 quartile method can be used for weighted mixing. The specific steps are as follows: First, calculate the proportion of the area of each micro-topographic 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 micro-topographic unit. For example, if a certain micro-topographic 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 micro-topographic units together and use the quartile 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.

[0042] Specifically, in 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 terrain differences in the field plot, a specific improvement scheme for step A101 is proposed. This scheme first divides the target field into multiple micro-topography units based on the field terrain data. When dividing, the elevation difference within the micro-topography unit is less than the preset threshold as the standard, ensuring the relative consistency of the terrain within each micro-topography unit. The advantage of doing this is that the field can be effectively managed in sub-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-topography unit are more representative and can reflect the true soil conditions in this area. After completing the division of the micro-topography units, within each micro-topography unit, multiple soil samples are randomly collected, and the soil samples within the same micro-topography unit are mixed evenly to obtain the representative soil sample of this micro-topography unit. By randomly sampling and mixing within the unit, the influence of local soil differences on the sample representativeness 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-topography unit need to be mixed. When mixing, the quartering method is used, and weighted mixing is carried out according to the area proportion of each micro-topography unit. For the micro-topography unit with a large area proportion, 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 terrain 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.

[0043] Through the above technical solution, this application can collect more precisely representative mixed soil samples, effectively overcoming the sampling deviation problem caused by ignoring the terrain 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.

[0044] In some embodiments, step A2 includes: A201. Use an image acquisition device to acquire the image of the chromogenic solution, and adopt an adaptive threshold segmentation algorithm to segment the chromogenic region from the image of the chromogenic solution; 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; 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, so as to determine the colorimetric method drug efficacy evaluation result.

[0045] Among them, in step A201, an image acquisition device is used to obtain an image of the chromogenic solution, and the chromogenic 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.

[0046] Among them, in step A202, for the segmented chromogenic region, the mean and standard deviation of the R, G, and B channels are calculated in the RGB color space, and the mean and standard deviation of the H, S, and V channels are also calculated in the HSV color space. A total of 12 color feature parameters are extracted to form a color feature vector. By extracting multi-dimensional color features in the RGB and HSV color spaces, the color information of the chromogenic 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.

[0047] 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 the 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, 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.

[0048] 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 chromogenic region using the adaptive threshold segmentation algorithm in step A201, reduces background interference, and ensures 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, comprehensively and robustly describes the color information of the chromogenic solution, reduces the interference of environmental factors such as uneven illumination, and improves the discrimination ability of color features; uses a pre-trained color matching model to perform standard color patch matching in step A203, realizes the accurate mapping from color features to the efficacy evaluation results, and improves the objectivity and accuracy of the 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 chromogenic 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.

[0049] 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 chromogenic solution image, the OTSU algorithm is used to process the chromogenic solution image, automatically calculate the optimal threshold, and segment the chromogenic region from the background based on the optimal threshold. For the segmented chromogenic region, calculate 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, as well as 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, a total of 12 color feature parameters, to form a color feature vector. The color feature vector is input into a pre-trained support vector machine model, and the support vector machine model outputs the standard color patch matching result, which is used to determine the colorimetric efficacy evaluation result. Using the OTSU algorithm can adaptively segment the chromogenic region, 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.

[0050] In some preferred embodiments, after step A202 and before step A203, the following steps are further included: A204. Real-time collect the light intensity in the field and calculate the average light intensity within the first preset time window; A205. According to the average light intensity, perform light correction on the color feature vector using a polynomial regression model.

[0051] Among them, step A204 can be specifically implemented in the following ways: use a light intensity sensor to monitor the light intensity of the field environment in real time. The light intensity sensor can be a silicon photocell or a photodiode, etc. The light intensity sensor can be arranged close to the color solution image acquisition area to ensure that the collected light intensity data is as consistent as possible with the lighting conditions when the color solution image is acquired. The first preset time window can be set to 1 minute, 5 minutes or 10 minutes, etc. The length of the time window can be adjusted according to the frequency of light changes in the actual application scenario. The average light intensity can be obtained by taking the arithmetic average of the light intensity data collected within the preset time window. This can effectively filter out the impact of instantaneous light fluctuations, obtain a relatively stable light intensity characterization value, and provide a reliable data basis for subsequent light correction.

[0052] Among them, step A205 can be implemented in the following way: first, a polynomial regression model needs to be established in advance. 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 images of the color developing solution under different light intensity conditions, extract the color feature vector, and then use the collected data to train the model to complete the determination of the model parameters. In practical applications, the average light intensity calculated in step A204 and the color feature vector calculated in step A202 are substituted into the pre-established polynomial regression model to calculate the corrected color feature vector. By performing light correction through the polynomial regression model, the interference of light intensity changes on the color feature vector can be effectively eliminated, and the robustness and accuracy of the color feature can be improved. Specifically, the polynomial regression model can be expressed as: C1_i=a0_i+a1_i*I+a2_i*I^2+...+an_i*I^n+b1_i*C0_i; i=1,2,...,12; 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, and n is the order of the polynomial regression model. For example, for the quadratic polynomial regression model, n=2, and for the cubic polynomial regression model, n=3.

[0053] Thus, through the synergistic effect of step A204 and step A205, effective illumination correction of the color feature vector can be achieved under complex and variable field illumination conditions, reducing the interference of illumination condition changes on the efficacy evaluation results and improving the accuracy and reliability of the colorimetric method for efficacy evaluation.

[0054] Specifically, aiming at the technical problem that the illumination conditions in the field environment are complex and variable, and the unstable illumination intensity will interfere with the extraction of color features of the chromogenic solution image, thereby affecting the accuracy of the efficacy evaluation results. In this application, an illumination correction step is added after extracting the color feature vector and before performing the color matching model. First, the illumination intensity in the field is collected in real time through step A204, and the average illumination intensity within the first preset time window is calculated. This can obtain the average illumination level during color feature extraction and provide a data basis for subsequent illumination correction. Then, in step A205, according to the average illumination intensity, the polynomial regression model is used to perform illumination correction on the color feature vector. The polynomial regression model can better fit the non-linear relationship between the illumination intensity and the color features, thereby effectively eliminating the influence of illumination 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.

[0055] Through the above technical solution, this application can effectively eliminate the interference of illumination intensity changes on the color feature vector under complex and variable field illumination 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.

[0056] In some embodiments, step A3 includes: A301. Using a plant fluorescence spectrometer to obtain the chlorophyll fluorescence spectrum data generated by weed leaves under the excitation spectrum irradiation, 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 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; A304. Inputting the extracted fluorescence spectrum characteristic parameters into a pre-established efficacy evaluation model to obtain the efficacy evaluation result of the plant fluorescence spectrometry method output by the efficacy evaluation model.

[0057] 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 prerequisite for subsequent spectral analysis and efficacy evaluation. Specifically, it can be achieved by the following methods: A portable plant fluorescence spectrometer is used 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 acquisition of high-quality chlorophyll fluorescence spectral data.

[0058] 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 by the following methods: 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 the order of removing noise, background fluorescence correction and smoothing processing to ensure the effect of pretreatment.

[0059] 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 influence degree of herbicides on weeds and provide a scientific basis for the efficacy evaluation. The following methods can be specifically adopted 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 plants are 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 plants, 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 plants, 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.

[0060] 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 adopted 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 the efficacy level, the efficacy percentage, or other quantitative indicators that can characterize the efficacy degree.

[0061] Thus, through steps A301 to A304, the accurate acquisition of the efficacy evaluation results by the plant fluorescence spectrometry method 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.

[0062] Specifically, the present application defines the process of obtaining the efficacy evaluation results by plant fluorescence spectrometry. First, a chlorophyll fluorescence spectral data of a weed leaf 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, 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, and the accuracy and reliability of the evaluation results are improved, overcoming the subjectivity of traditional manual visual inspection methods and the disadvantages of time-consuming and laborious laboratory detection methods, and meeting the requirements of rapid and accurate efficacy evaluation in the field.

[0063] 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.

[0064] Preferably, step A301 includes: Determining the weed species and growth stage information of the weed to be measured based on image recognition technology; Adjusting the spectral parameters of the excitation spectrum according to the weed species and growth stage information; Using a plant fluorescence spectrometer, irradiating the weed leaf based on the adjusted excitation spectrum, and obtaining the chlorophyll fluorescence spectral data generated by the weed leaf under the irradiation of the excitation spectrum to obtain the original fluorescence spectrum.

[0065] 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 type of weeds, 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 the type and growth stage of the weeds is accurately identified. Specifically, existing image recognition technology can be used to identify the information on the type and growth stage of the weeds, and no limitation is imposed here.

[0066] Furthermore, the system pre-stores the excitation spectrum 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 spectrum 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 spectrum parameters are a spectral range of 350 - 650 nm and a peak wavelength of 400 nm. After obtaining the information on the type and growth stage of the weeds, the spectral parameter adjustment module automatically calls the matching excitation spectrum 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.

[0067] Specifically, after receiving the adjusted excitation spectrum parameters, the plant fluorescence spectrometer adjusts its light source output. For example, if the adjusted excitation spectrum parameters are a spectral range of 400 - 700 nm and a peak wavelength of 450 nm, then the light source emission spectrum range of the plant fluorescence spectrometer is 400 - 700 nm, and the light intensity is the largest at a 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 spectrum data. Since the excitation spectrum parameters have been optimized and adjusted according to the weed type and growth stage, the collected original fluorescence spectrum data can more accurately and sensitively reflect the physiological state of the weeds, providing a high-quality data basis for subsequent efficacy evaluation.

[0068] In some embodiments, step A4 includes: 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; 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 for the colorimetric method efficacy evaluation result and the weight adjustment coefficient for the plant fluorescence spectrometry method efficacy evaluation result; According to the weight adjustment coefficient of the efficacy evaluation result by colorimetry and the weight adjustment coefficient of the efficacy evaluation result by plant fluorescence spectrometry, adjust the reference weight of the efficacy evaluation result by colorimetry and the reference weight of the efficacy evaluation result by plant fluorescence spectrometry respectively.

[0069] Among them, in step A401, real-time collection means continuously collecting environmental parameter data during the efficacy evaluation process. The field soil humidity refers to the water content of the field soil, and the 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 value 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 value is to obtain a representative value of environmental parameters within the time window and reduce the interference of instantaneous environmental fluctuations on subsequent weight adjustment.

[0070] Among them, in step A402, the mapping relationship between environmental parameters and the reference weight adjustment coefficient is a pre-established corresponding relationship between the values of environmental parameters and the weight adjustment coefficient (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, the corresponding weight adjustment coefficient can be determined according to different combinations of the mean soil humidity and the mean light intensity. The weight adjustment coefficient is used to adjust the reference weights of the evaluation results of these two methods, colorimetry and plant fluorescence spectrometry.

[0071] Among them, in step A403, adjusting the reference weight means dynamically adjusting the proportions of the evaluation results of these two methods, colorimetry and plant fluorescence spectrometry, in the comprehensive efficacy evaluation. The adjustment can be increasing or decreasing the reference weight (for example, increasing or decreasing based on a preset benchmark reference weight), and the adjustment amplitude 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.

[0072] Specifically, this solution aims to dynamically adjust the reference weights of the two efficacy evaluation results of colorimetry and plant fluorescence spectroscopy 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 effects that environmental parameters may have on different efficacy evaluation methods. For example, when the soil humidity is relatively high, the evaluation result of colorimetry by soil sampling may be interfered, and at this time, the weight adjustment coefficient of colorimetry 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 change dynamically according to real-time environmental parameters. Thus, the comprehensive efficacy evaluation result can better adapt to environmental changes, and the accuracy and reliability of the evaluation result are improved.

[0073] As a preferred embodiment, the solution of this application is specifically implemented as follows: 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 look-up table. The row index of the look-up 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 colorimetry and the weight adjustment coefficients of plant fluorescence spectroscopy. 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 look-up table, the weight adjustment coefficient of colorimetry is set to 0.7, and the weight adjustment coefficient of plant fluorescence spectroscopy is set to 1.3. In step A403, the reference weights are adjusted by multiplication, that is, the original reference weights of colorimetry and plant fluorescence spectroscopy are multiplied by the corresponding weight adjustment coefficients respectively to obtain the adjusted reference weights.

[0074] Through the above technical solution, this application can dynamically adjust the reference weights of the efficacy evaluation results of colorimetry and plant fluorescence spectroscopy according to environmental parameters such as soil humidity and light intensity collected in real time, so that the comprehensive efficacy evaluation result can more accurately reflect the true efficacy of environmentally friendly herbicides, reduce the interference of environmental factors on the efficacy evaluation results of colorimetry and plant fluorescence spectroscopy, and improve the accuracy and reliability of efficacy evaluation.

[0075] In some embodiments, step A5 includes: 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; A502. If the evaluation results conflict, then based on the historical drug efficacy evaluation data, determine the drug efficacy evaluation correction rules corresponding to the current evaluation results, and according to the determined drug efficacy evaluation correction rules, correct the evaluation results of the colorimetric method for drug efficacy and the evaluation results of the plant fluorescence spectroscopy method for drug efficacy respectively, to obtain the final evaluation results of the colorimetric method for drug efficacy and the final evaluation results of the plant fluorescence spectroscopy method for drug efficacy; A503. If the evaluation results do not conflict, then use the original evaluation results of the colorimetric method for drug efficacy and the original evaluation results of the plant fluorescence spectroscopy method for drug efficacy as the final evaluation results of the colorimetric method for drug efficacy and the final evaluation results of the plant fluorescence spectroscopy method for drug efficacy; A504. According to the adjusted reference weights, perform weighted synthesis on the final evaluation results of the colorimetric method for drug efficacy and the final evaluation results of the plant fluorescence spectroscopy method for drug efficacy to obtain the comprehensive evaluation result of drug efficacy.

[0076] Among them, in step A501, the preset deviation threshold can be set according to the actual application scenario and the evaluation accuracy requirements. 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 due to directly synthesizing conflicting evaluation results.

[0077] Among them, step A502 refers to the corrective measures taken when the evaluation result is judged to be in conflict in step A501. 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, and 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 (for example, the current environmental parameters, drug types, etc.), and based on these historical data, the deviation law between the colorimetric method drug efficacy evaluation results and the plant fluorescence spectroscopy drug efficacy evaluation results can be analyzed, so as to formulate 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 correction rules, the original colorimetric method drug efficacy evaluation results and the plant fluorescence spectroscopy drug efficacy evaluation results are corrected respectively to obtain the final evaluation results. Introducing historical drug efficacy evaluation data can provide a reference basis for the correction of evaluation results, making the correction process more scientific and reasonable. Through correction, the deviation of the evaluation results can be effectively reduced or eliminated, and the accuracy of the evaluation results can be improved.

[0078] Among them, step A503 refers to the handling method taken when the evaluation result is judged to be non-conflicting in step A501. 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.

[0079] Among them, in step A504, according to the adjusted reference weight 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 result. By using 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 result more comprehensive and accurate.

[0080] Specifically, in view of the possible conflict between the colorimetric method for evaluating drug efficacy and the plant fluorescence spectroscopy method for evaluating drug efficacy, this solution proposes specific solutions to ensure the accuracy and reliability of the comprehensive drug efficacy evaluation results. 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 drug efficacy evaluation results. Through the above steps, this solution can effectively handle the possible conflict situations between the colorimetric method for evaluating drug efficacy and the plant fluorescence spectroscopy method for evaluating drug efficacy, avoid the deviation of the comprehensive drug efficacy evaluation results caused by directly synthesizing conflicting evaluation results, and ensure the accuracy and reliability of the comprehensive drug efficacy evaluation results.

[0081] As a preferred embodiment, the solution of this application is specifically implemented as follows: Set the preset deviation threshold to 0.2. In step A501, calculate the difference between the colorimetric method for evaluating drug efficacy (assumed to be 0.7) and the plant fluorescence spectroscopy method for evaluating drug efficacy (assumed to be 0.3), obtaining 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 colorimetric method for evaluating drug efficacy by 0.1 and increase the plant fluorescence spectroscopy method for evaluating drug efficacy by 0.1. According to this correction rule, correct the colorimetric method for evaluating drug efficacy to 0.6 and the plant fluorescence spectroscopy method for evaluating drug efficacy to 0.4, obtaining the final colorimetric method for evaluating drug efficacy of 0.6 and the final plant fluorescence spectroscopy method for evaluating drug efficacy of 0.4. In step A504, assume that the reference weight of the adjusted colorimetric method for evaluating drug efficacy is 0.6 and the reference weight of the plant fluorescence spectroscopy method for evaluating drug efficacy is 0.4. Then, according to the adjusted reference weights, perform weighted synthesis on the final colorimetric method for evaluating drug efficacy of 0.6 and the final plant fluorescence spectroscopy method for evaluating drug efficacy of 0.4, and the comprehensive drug efficacy evaluation result obtained is: 0.6 * 0.6 + 0.4 * 0.4 = 0.52.

[0082] Through the above technical solution, in the process of comprehensive drug efficacy evaluation of this application, it can effectively solve the possible conflict problems between the colorimetric method for evaluating drug efficacy and the plant fluorescence spectroscopy method for evaluating drug efficacy, avoid the deviation of the comprehensive drug efficacy evaluation results caused by the conflict of evaluation results, ensure the accuracy and reliability of the comprehensive drug efficacy evaluation results, and thus provide a more scientific and effective technical means for the dynamic evaluation of the drug efficacy of environment-friendly herbicides.

[0083] refer to Figure 2 The present application provides a herbicide efficacy dynamic evaluation system for evaluating the efficacy of an environmentally friendly herbicide after applying the environmentally friendly herbicide. The system comprises: The image acquisition device 1 is used to acquire an image of a color developing solution; the color developing solution is obtained by collecting field soil samples, extracting herbicides from the field soil samples, and adding a color developer to perform a color development reaction; Plant fluorescence spectrometer 2, used to measure the chlorophyll fluorescence spectrum of weed leaves; Environmental parameter acquisition device 3, used for real-time acquisition of environmental parameters; environmental parameters include soil moisture and light intensity; The host computer 4 is used to perform the following steps: The color characteristics of the color developing solution are extracted according to the color developing solution image, and used to match with the standard color block of the preset colorimetric card to obtain the colorimetric drug efficacy evaluation result; Analyze the fluorescence spectrum characteristic parameters of chlorophyll fluorescence spectrum, and determine the drug efficacy evaluation results of plant fluorescence spectrum method according to the fluorescence spectrum characteristic parameters; Dynamically adjust the reference weights of the colorimetric efficacy evaluation results and the plant fluorescence spectroscopy efficacy evaluation results according to the current environmental parameters; According to the adjusted reference weights, the colorimetric efficacy evaluation results and the plant fluorescence spectroscopy efficacy evaluation results are combined to obtain a comprehensive efficacy evaluation result.

[0084] Among them, the above-mentioned herbicide efficacy dynamic evaluation method can be implemented based on the herbicide efficacy dynamic evaluation system.

[0085] The image acquisition device 1 may be a CCD camera or a CMOS camera, etc., which is installed in a position convenient for photographing the color-developing solution, such as fixed on a tripod or integrated in a portable device.

[0086] The plant fluorescence spectrometer 2 can be a portable or handheld fluorescence spectrometer for field operation. The spectrum probe of the plant fluorescence spectrometer is adjusted to aim at the weed leaves to ensure accurate collection of fluorescence signals.

[0087] Among them, the environmental parameter acquisition device 3 integrates a variety of sensors for real-time monitoring of field environmental parameters. The environmental parameter acquisition device 3 includes but is not limited to a soil moisture sensor and a light intensity sensor. The soil moisture sensor can be a TDR soil moisture sensor or a capacitive soil moisture sensor, and the light intensity sensor can be a silicon photocell light intensity sensor or a photodiode light intensity sensor. The environmental parameter acquisition device 3 is placed in a representative position in the field to accurately reflect the environmental conditions of the assessment area.

[0088] 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.

[0089] When the host computer 4 executes the steps, first, it acquires 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 drug 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 processing. 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 drug efficacy evaluation model, which can be a partial least squares regression model or a BP neural network model, to obtain the plant fluorescence spectrometry drug 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 drug efficacy evaluation result and the plant fluorescence spectrometry drug efficacy evaluation result according to the current environmental parameters based on the preset mapping relationship between the environmental parameters and the reference weight adjustment coefficient. 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 drug efficacy evaluation result and the plant fluorescence spectrometry drug efficacy evaluation result according to the adjusted reference weights to obtain the comprehensive drug efficacy evaluation result. The comprehensive drug efficacy evaluation result is output in the form of numbers, charts, or reports to provide intuitive drug efficacy evaluation information for users.

[0090] 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. In actual implementation, there may be other division methods. 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 coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0091] In addition, the units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units. They can be located in one place or distributed to 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.

[0092] Furthermore, in each embodiment of the present application, the various functional modules can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0093] In this article, 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.

[0094] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for dynamically evaluating the efficacy of an environmentally friendly herbicide, which is used to evaluate the efficacy of an environmentally friendly herbicide after applying the environmentally friendly herbicide, characterized in that: The steps of the method include: A1. Collect field soil samples, extract herbicides from the field soil samples, add a color developer to perform a color reaction, and obtain a color solution; A2. Obtain the color characteristics of the color-developing solution to match it with the standard color block of the preset colorimetric card to obtain the colorimetric efficacy evaluation result; A3. Use a plant fluorescence spectrometer to obtain the chlorophyll fluorescence spectrum of the weed leaves, analyze the fluorescence spectrum characteristic parameters of the chlorophyll fluorescence spectrum, and determine the plant fluorescence spectroscopy efficacy evaluation result according to the fluorescence spectrum characteristic parameters; A4. Real-time collection of environmental parameters, and dynamic adjustment of the reference weights of the colorimetric efficacy evaluation results and the plant fluorescence spectroscopy efficacy evaluation results according to the current environmental parameters; the environmental parameters include soil moisture and light intensity; A5. Based on the adjusted reference weights, the colorimetric efficacy evaluation results and the plant fluorescence spectroscopy efficacy evaluation results are combined to obtain a comprehensive efficacy evaluation result.

2. A method for dynamic evaluation of herbicide efficacy according to claim 1, characterized in that: Step A1 includes: A101. Collect soil samples from multiple fields and mix them evenly according to the quartering method to obtain mixed soil samples; A102. Weigh a preset mass of mixed soil sample, add a pretreatment solution, extract the herbicide by ultrasonic oscillation to obtain a soil extract; the pretreatment solution comprises acetonitrile, water and formic acid in a volume ratio of 4:5:0.1; A103. Using a solid phase extraction column to purify the soil extract, remove pigments and humus to obtain a purified extract; A104. The purified extract was concentrated by nitrogen blowing under nitrogen protection to obtain a concentrated extract; A105. Dissolve the concentrated extract in the color developer solution, protect from light and react for a preset time to obtain a color developing solution.

3. A method for dynamic evaluation of herbicide efficacy according to claim 2, characterized in that: Step A101 includes: Based on field topography data, the target field is divided into a plurality of micro-topography units whose elevation differences are less than a preset threshold; the field topography data includes elevation data and slope data; Within each microtopographic unit, multiple soil samples were randomly collected; The soil samples in the same micro-topographic unit are mixed evenly to obtain representative soil samples of the corresponding micro-topographic unit; The four-division method was used to weight the representative soil samples of each microtopography unit according to the area proportion of each microtopography unit to obtain a mixed soil sample.

4. A method for dynamic evaluation of herbicide efficacy according to claim 1, characterized in that: Step A2 includes: A201. Use an image acquisition device to acquire a color solution image, and use an adaptive threshold segmentation algorithm to segment the color solution image into a color region; A202. For the segmented color display area, calculate the mean and standard deviation of the three channels R, G, and B in the RGB color space, convert the RGB color space to the HSV color space, calculate the mean and standard deviation of the three channels H, S, and V, and obtain 12 color feature parameters to form a color feature vector; 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 to determine the colorimetric drug efficacy evaluation result.

5. A method for dynamic evaluation of herbicide efficacy according to claim 4, characterized in that: After step A202 and before step A203, the method further includes the following steps: A204. Real-time collection of field light intensity, calculation of the average light intensity within the first preset time window; A205. Use a polynomial regression model to perform illumination correction on the color feature vector based on the average light intensity.

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

7. A method for dynamic evaluation of herbicide efficacy according to claim 6, characterized in that: Step A301 includes: Determine the weed species and growth stage information of the detected weeds based on image recognition technology; Adjust the spectral parameters of the excitation spectrum according to the weed species and growth stage information; Using a plant fluorescence spectrometer, the weed leaves are irradiated based on the adjusted excitation spectrum to obtain chlorophyll fluorescence spectrum data generated by the weed leaves under the excitation spectrum, thereby obtaining the original fluorescence spectrum.

8. A method for dynamic evaluation of herbicide efficacy according to claim 1, characterized in that: Step A4 includes: A401 real-time collection of field soil moisture and light intensity, calculate the mean soil moisture and light intensity within the second preset time window; A402. According to the mean soil moisture and the mean light intensity, based on the mapping relationship between environmental parameters and reference weight adjustment coefficients, calculate the weight adjustment coefficients of the colorimetric efficacy evaluation results and the weight adjustment coefficients of the plant fluorescence spectroscopy efficacy evaluation results; A403. According to the weight adjustment coefficient of the colorimetric efficacy evaluation results and the weight adjustment coefficient of the plant fluorescence spectroscopy efficacy evaluation results, adjust the reference weight of the colorimetric efficacy evaluation results and the reference weight of the plant fluorescence spectroscopy efficacy evaluation results respectively.

9. A method for dynamic evaluation of herbicide efficacy according to claim 1, characterized in that: Step A5 includes: A501. Calculate the difference between the colorimetric efficacy evaluation result and the plant fluorescence spectroscopy efficacy evaluation result, compare the difference with the preset deviation threshold, and determine whether the evaluation results conflict; A502. If the evaluation results conflict, then based on the historical efficacy evaluation data, determine the efficacy evaluation correction rule corresponding to the current evaluation result, and according to the determined efficacy evaluation correction rule, respectively correct the colorimetric efficacy evaluation result and the plant fluorescence spectroscopy efficacy evaluation result to obtain the final colorimetric efficacy evaluation result and the final plant fluorescence spectroscopy efficacy evaluation result; A503. If the evaluation results are not conflicting, the original colorimetric efficacy evaluation results and the original plant fluorescence spectroscopy efficacy evaluation results shall be used as the final colorimetric efficacy evaluation results and the final plant fluorescence spectroscopy efficacy evaluation results; A504. Based on the adjusted reference weights, the final colorimetric efficacy evaluation results and the final plant fluorescence spectroscopy efficacy evaluation results are weighted combined to obtain a comprehensive efficacy evaluation result.

10. A herbicide efficacy dynamic evaluation system, used for evaluating the efficacy of an environmentally friendly herbicide after applying the environmentally friendly herbicide, characterized in that: The system includes: An image acquisition device is used to acquire an image of a color developing solution; the color developing solution is obtained by collecting field soil samples, extracting herbicides from the field soil samples, and adding a color developer to perform a color development reaction; Plant fluorescence spectrometer, used to measure the chlorophyll fluorescence spectrum of weed leaves; An environmental parameter acquisition device, used to acquire environmental parameters in real time; the environmental parameters include soil moisture and light intensity; The host computer is used to perform the following steps: The color characteristics of the color developing solution are extracted according to the color developing solution image, and used to match with the standard color block of the preset colorimetric card to obtain the colorimetric drug efficacy evaluation result; Analyzing the fluorescence spectrum characteristic parameters of the chlorophyll fluorescence spectrum, and determining the drug efficacy evaluation result of the plant fluorescence spectrum method according to the fluorescence spectrum characteristic parameters; Dynamically adjust the reference weights of the colorimetric efficacy evaluation results and the plant fluorescence spectroscopy efficacy evaluation results according to the current environmental parameters; According to the adjusted reference weights, the colorimetric efficacy evaluation results and the plant fluorescence spectroscopy efficacy evaluation results are combined to obtain a comprehensive efficacy evaluation result.

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