A method for evaluating the utilization effect of medicine and fertilizer based on spectral analysis

Abstract

This invention belongs to the field of agricultural resource management and performance evaluation technology, specifically relating to a method for evaluating the effectiveness of pesticide and fertilizer utilization based on spectral analysis. The method includes: acquiring a multispectral time-series sequence of the operational area and performing normalization processing to obtain a vegetation index; acquiring the pesticide and fertilizer application rate and the vegetation index change rate to obtain a metabolic response coefficient; correcting the metabolic response coefficient using the environmental humidity integral value to obtain a corrected metabolic coefficient; combining the total pesticide and fertilizer application mass and the vegetation index gradient norm to obtain a comprehensive utilization efficiency score reflecting the level of pesticide and fertilizer utilization; and obtaining a frequency conversion command based on the comprehensive utilization efficiency score and a preset utilization rate benchmark to adjust the speed of the pesticide and fertilizer pump motor, thereby achieving automated closed-loop regulation of pesticide and fertilizer application dosage. This invention solves the technical problems of pesticide and fertilizer utilization evaluation being greatly affected by environmental interference and lacking real-time feedback, thus improving the depth and accuracy of performance evaluation of agricultural resource inputs.

Description

Technical Field

[0001] This invention relates to the field of agricultural resource management and performance evaluation technology. More specifically, this invention relates to a method for evaluating the effectiveness of pesticide and fertilizer utilization based on spectral analysis. Background Technology

[0002] In the resource management and operation system of modern agriculture, performance evaluation of pesticide and fertilizer utilization efficiency is a core task for improving farmland management and optimizing input costs. Currently, farmland operation evaluation often employs experience-based qualitative analysis or simple sensor threshold early warning models. These methods, when used for business process optimization and performance management, are often limited by the simplistic data collection methods, making it difficult to establish a scientific performance evaluation indicator system. In actual operations, the utilization efficiency of pesticides and fertilizers is constrained by multiple factors, including crop physiological characteristics, soil conditions, and real-time weather conditions, resulting in significant randomness and non-stationarity in the data flow during the evaluation process. Existing management decision-making systems typically lack effective benchmark comparison mechanisms, making it impossible to accurately analyze the actual conversion efficiency of pesticides and fertilizers within the dynamically changing production cycle.

[0003] Furthermore, environmental factors such as humidity fluctuations can directly interfere with the sensor responses of monitoring equipment. In evaluation logic lacking feature decoupling capabilities, these interfering factors are often mistakenly identified as fluctuations in management performance, leading to biased performance analysis results and misleading management in resource allocation decisions. In the context of improving operational efficiency, the lack of a robust, multi-dimensional performance measurement model makes it difficult for agricultural operators to identify inefficient links in the production process and to achieve refined performance evaluation and dynamic adjustment based on real-time crop growth feedback.

[0004] Meanwhile, existing evaluation methods are mostly post-event assessments, lacking real-time monitoring and performance insights into the dynamics of pesticide and fertilizer utilization during the application phase. This not only increases the risk of resource misallocation but also hinders the evolution of agricultural production management towards digital and intelligent decision-making. From the perspective of organizational performance analysis, the lack of a scientific and unified key performance indicator system makes it difficult to benchmark the effects of operations on different plots and batches, limiting the continuous improvement of management strategies and the enhancement of operational excellence. In summary, how to overcome environmental noise interference, establish cross-dimensional performance evaluation benchmarks, and form an evaluation system that forms a closed loop from data perception to management decision-making has become a systemic management problem that urgently needs to be solved in the process of improving the efficiency of agricultural resource management and achieving high-precision business analysis. Summary of the Invention

[0005] To address the technical problems of low resource allocation efficiency and lack of accurate comparative basis for agricultural management decisions caused by the lack of multi-dimensional performance indicator quantification methods and environmental interference identification mechanisms in existing pesticide and fertilizer application evaluation systems, this invention provides a pesticide and fertilizer utilization effect evaluation method based on spectral analysis. The method includes: acquiring a multispectral time series of the operating area and performing normalization processing to obtain a vegetation index; acquiring the pesticide and fertilizer application rate and the vegetation index change rate, and obtaining a metabolic response coefficient based on the pesticide and fertilizer application rate and the vegetation index change rate; acquiring an environmental humidity fluctuation series and performing integration processing to obtain an environmental humidity integral value, and obtaining a corrected metabolic coefficient based on the metabolic response coefficient and the environmental humidity integral value; acquiring the total pesticide and fertilizer application mass and the vegetation index gradient norm, and obtaining a comprehensive utilization efficiency score based on the corrected metabolic coefficient, the vegetation index gradient norm, and the total pesticide and fertilizer application mass; obtaining a frequency conversion command based on the comprehensive utilization efficiency score and a preset utilization rate benchmark, and using the frequency conversion command to adjust the pesticide and fertilizer application rate to achieve automated closed-loop regulation of pesticide and fertilizer application dosage.

[0006] This invention obtains vegetation indices by acquiring and normalizing multispectral time series, and obtains metabolic response coefficients by combining pesticide and fertilizer application rates. It then obtains corrected metabolic coefficients by utilizing environmental humidity integral values, and finally obtains frequency conversion commands based on comprehensive utilization efficiency scores and preset utilization rate benchmarks. This achieves automated closed-loop management from spectral sensing to pesticide and fertilizer application dosage adjustment, improves the objectivity of the evaluation process, and reduces judgment errors that may be caused by human observation.

[0007] Preferably, the metabolic response coefficient satisfies the expression: In the formula, Indicates time The metabolic response coefficient; Represents differentiation operations; Indicates time The vegetation index; Indicates time The speed of pesticide and fertilizer application; Indicates the physiological response lag period; This represents a constant used to prevent the denominator from being zero.

[0008] This invention obtains the metabolic response coefficient by calculating the ratio of the vegetation index change rate to the cumulative application amount within the physiological response lag period, directly linking crop growth changes with the actual input of pesticide and fertilizer resources. This allows the evaluation indicators to more accurately reflect the crop's ability to convert pesticides and fertilizers, providing reliable data support for subsequent resource allocation.

[0009] Preferably, the physiological response lag period is obtained by analyzing the phase difference between the drug application pulse signal and the vegetation index response waveform using a cross-correlation function, and taking the time deviation corresponding to the highest correlation as the physiological response lag period.

[0010] This invention utilizes cross-correlation functions to analyze the phase difference between the pesticide application pulse signal and the vegetation index response waveform to obtain the physiological response lag period. Through this signal-level alignment processing, the misalignment of evaluation indicators on the time axis caused by crop physiological feedback lag is reduced, and the matching degree between the evaluation results and the actual physiological process is improved.

[0011] Preferably, the corrected metabolic coefficient satisfies the expression: In the formula, Indicates time Corrected metabolic coefficient; Indicates time The metabolic response coefficient; Indicates time The ambient humidity; This represents the humidity sensitivity constant; This represents an exponential function with the natural constant as its base.

[0012] This invention corrects the metabolic response coefficient based on the integral value of environmental humidity and uses an exponential function. By introducing a humidity sensitivity constant, it offsets the interference of moisture in the air on the spectral signal, so that the corrected metabolic coefficient can more accurately describe the true metabolic state of crops and reduce the misleading influence of non-physiological factors on the assessment of pesticide and fertilizer utilization levels.

[0013] Preferably, the humidity sensitivity constant is obtained by: establishing a correspondence between reflectance deviation and the integral value of ambient humidity using regression analysis, and using the calculated proportionality coefficient as the humidity sensitivity constant.

[0014] This invention obtains the humidity sensitivity constant by performing regression analysis in a controlled meteorological chamber, establishes the correspondence between reflectance deviation and environmental humidity integral value, provides parameter basis for data compensation under different environmental conditions, and improves the evaluation model's adaptability to variable meteorological environments.

[0015] Preferably, the comprehensive utilization efficiency score satisfies the expression: In the formula, This indicates the score for comprehensive utilization efficiency; Indicates the duration of the evaluation period; Indicates time Corrected metabolic coefficient; Indicates time The vegetation index gradient norm; Indicates the total application quality of pesticides and fertilizers; This represents a constant to prevent the denominator from being zero. Gradient operators that represent the degree of drastic change in the computation space; This represents the norm operation used to calculate the length of a vector.

[0016] This invention comprehensively evaluates the modified metabolic coefficient, vegetation index gradient norm, and total pesticide and fertilizer application quality within a given evaluation period to obtain a comprehensive utilization efficiency score. Through spatiotemporal fusion evaluation, it not only reflects the contribution of pesticides and fertilizers to crop growth but also demonstrates the uniformity of pesticide and fertilizer effects, making the evaluation results more comprehensive.

[0017] Preferably, the step of acquiring the multispectral time series of the work area and performing normalization processing to obtain the vegetation index includes: periodically scanning the work area using a UAV spectral imager to acquire red band reflectance data and near-infrared band reflectance data, arranging the data in chronological order to acquire the multispectral time series, and converting the multispectral time series into a vegetation index using a standard normalized vegetation index calculation method.

[0018] This invention utilizes a UAV spectral imager to acquire red light band reflectance data and near-infrared band reflectance data, and converts the multispectral time series into vegetation indices in chronological order, enabling rapid monitoring of large-area farmland growth and providing a basic data stream for spatiotemporal evaluation of pesticide and fertilizer utilization.

[0019] Preferably, the step of obtaining the frequency conversion command based on the comprehensive utilization efficiency score and the preset utilization rate benchmark includes: using a proportional-integral-derivative control strategy to determine the deviation between the comprehensive utilization efficiency score and the preset utilization rate benchmark; when the comprehensive utilization efficiency score is lower than the preset utilization rate benchmark, the main controller sends a frequency conversion command to the servo driver of the application pump motor.

[0020] This invention utilizes a proportional-integral-derivative control strategy to obtain frequency conversion commands and send them to the servo driver of the spray pump motor. It transforms the comprehensive utilization efficiency score into specific hardware control commands, enabling the system to automatically adjust its operating status based on the actual growth feedback of the crop, thereby improving the precision of management.

[0021] Preferably, the adjustment of the fertilizer and pesticide application speed includes: adjusting the rotational speed of the fertilizer and pesticide application pump motor by a servo driver of the application pump motor, and adjusting the fertilizer and pesticide application speed by changing the output frequency of the application pump motor.

[0022] This invention adjusts the application speed of pesticides and fertilizers by regulating the rotation speed and output frequency of the pesticide pump motor through a servo driver, thereby achieving flexible adjustment of the pesticide and fertilizer dosage. This ensures real-time synchronization between pesticide and fertilizer application and crop metabolic needs, reducing resource waste caused by excessive pesticide and fertilizer application.

[0023] Preferably, the step of obtaining the environmental humidity fluctuation sequence includes: collecting air moisture data in real time through wireless sensor nodes deployed in the work area, and combining the air moisture data into an environmental humidity fluctuation sequence according to the time series.

[0024] The beneficial effects of this invention are as follows: This invention obtains metabolic response coefficients by integrating multispectral time series and real-time pesticide and fertilizer application data, and introduces environmental humidity integral values ​​for decoupling correction, thus obtaining a set of evaluation index systems covering growth feedback and environmental disturbances, enabling managers to clearly grasp the actual utilization level of pesticides and fertilizers in crops.

[0025] This invention uses the comprehensive utilization efficiency score as a feedback control variable. Through the coordinated work of the main controller, the servo driver of the spray pump motor and the spray pump motor, it realizes the dynamic closed-loop adjustment of the pesticide and fertilizer application dosage, transforming traditional experience-based management into data-driven automated precision operation, and improving the resource coordination efficiency of agricultural production.

[0026] This invention extracts spatial features such as the vegetation index gradient norm and combines them with the physiological response lag period to align metabolic signals in time, thereby achieving a comprehensive view of the effects of pesticide and fertilizer utilization in both time and space dimensions. This reduces decision-making bias caused by the incomplete evaluation of a single indicator and provides a scientific evaluation tool for improving farmland management performance. Attached Figure Description

[0027] Figure 1 The flowchart of a method for evaluating the utilization effect of pesticides and fertilizers based on spectral analysis is illustrated in the present invention. Figure 2 This diagram illustrates the changes in metabolic response coefficient and modified metabolic coefficient over time. Figure 3 The diagram illustrates the changes in comprehensive utilization efficiency score and fertilizer application rate over time. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0030] This invention discloses a method for evaluating the utilization effect of pesticides and fertilizers based on spectral analysis, referring to... Figure 1 This includes steps S1 to S5: S1. Obtain the multispectral time series of the work area and perform normalization processing to obtain the vegetation index.

[0031] It should be noted that the reason why this invention acquires multispectral time series and performs data normalization is that when the UAV collects spectral data at different dates and times, changes in the angle of sunlight and cloud cover can cause unnatural fluctuations in the intensity of light reflected from the ground, and the soil background can also interfere with the original data. By acquiring the time series and performing normalization, these environmental noises that are irrelevant to crop growth can be eliminated, and the original brightness information can be transformed into a vegetation index that can accurately reflect the crop growth status, thereby ensuring that the evaluation model has a unified feature comparison benchmark throughout the entire crop growth period.

[0032] Specifically, this invention utilizes a UAV spectral imager to periodically scan the work area, acquiring red band reflectance data and near-infrared band reflectance data. The red band reflectance data and near-infrared band reflectance data are arranged in chronological order to obtain a multispectral time series. The reflectance data in the multispectral time series is converted into a vegetation index using a standard normalized vegetation index calculation method.

[0033] S2. Obtain the application rate of pesticides and fertilizers and the rate of change of vegetation index, and obtain the metabolic response coefficient based on the application rate of pesticides and fertilizers and the rate of change of vegetation index.

[0034] It should be noted that after pesticides and fertilizers are applied to crops, there is a significant time delay between the changes in the crop's physiological state and the application pulse signal. That is, the chlorophyll content or water characteristics of the crop will not change immediately at the moment of application. This physiological lag phenomenon will cause the observed changes in vegetation index to be mismatched with the current pesticide and fertilizer application rate in time. If the relationship between the changes in vegetation index and the current pesticide and fertilizer application rate is directly calculated, the evaluation results will be distorted. Therefore, this invention introduces the physiological response lag period and calculates the cumulative application amount. The aim is to obtain a metabolic response coefficient that can truly reflect the crop's internal response to the pesticide and fertilizer conversion intensity through time dimension translation and cumulative compensation.

[0035] Specifically, this invention obtains the application rate of pesticides and fertilizers through a flow sensor and obtains the metabolic response coefficient by combining it with the vegetation index change rate.

[0036] The metabolic response coefficient satisfies the expression:

[0037] In the formula, Indicates time The metabolic response coefficient; Represents differentiation operations; Indicates time The vegetation index; Indicates time The speed of pesticide and fertilizer application; Indicates the physiological response lag period; This represents a constant to prevent the denominator from being zero, and its value is... .

[0038] In the formula, the metabolic response coefficient The magnitude of this value reflects the crop's ability to convert pesticides and fertilizers. The denominator term reflects the lag period of the physiological response. The application rate of pesticides and fertilizers within this time period is integrated to physically reconstruct the total amount of pesticides and fertilizers actually applied to the leaves during the crop's physiological absorption window, thereby accurately calculating the utilization background per unit dose. The numerator is used to extract the vegetation index change rate through differential calculation, which is used to measure the instantaneous acceleration of crop growth and reflects the speed of chlorophyll synthesis driven by pesticides and fertilizers. When the application rate of pesticides and fertilizers... In the physiological response lag period As the internal rate of increase continues, the cumulative amount of pesticides and fertilizers applied in the denominator also increases; if the crop growth rate fails to increase synchronously at this time, i.e., the vegetation index change rate... Maintaining a low level will lead to a lower metabolic response coefficient. A decrease in efficiency means lower utilization of pesticides and fertilizers.

[0039] It should be further added that this invention performs continuous spectral sampling on experimental samples, uses cross-correlation functions to analyze the phase difference between the drug application pulse signal and the vegetation index response waveform, and takes the time deviation corresponding to the highest correlation as the physiological response lag period.

[0040] S3. Obtain the environmental humidity fluctuation sequence and perform integration processing to obtain the environmental humidity integral value. Obtain the corrected metabolic coefficient based on the metabolic response coefficient and the environmental humidity integral value.

[0041] It should be noted that the moisture content in the environment directly changes the refractive properties of crop leaf surfaces, causing the reflected signals received by the spectral sensor to contain moisture interference noise. Furthermore, in high humidity environments, fertilizers and pesticides are more likely to be lost or have their penetration blocked. In order to isolate the interference of environmental factors from the observed metabolic signals, this invention obtains the environmental humidity integral value and performs dynamic gain correction on the metabolic response coefficient. By reducing the weight of the high humidity interference term, a corrected metabolic coefficient that can purely reflect the physiological absorption capacity of crops is obtained.

[0042] Specifically, the present invention acquires the environmental humidity fluctuation sequence through wireless sensing nodes and obtains the corrected metabolic coefficient based on the environmental humidity integral value.

[0043] The corrected metabolic coefficient satisfies the expression:

[0044] In the formula, Indicates time Corrected metabolic coefficient; Indicates time The metabolic response coefficient; Indicates time The ambient humidity; This represents the humidity sensitivity constant; This represents an exponential function with the natural constant as its base.

[0045] In the formula, the corrected metabolic coefficient The smaller the value, the stronger the masking effect of ambient humidity on the spectral signal or the higher the risk of pesticide and fertilizer runoff. The expression integrates the ambient humidity to calculate the cumulative infiltration depth of crop leaves due to water exposure. A continuously increasing humidity integral value indicates the formation of a continuous water film on the leaves, which not only causes sensor readings to deviate but also means a continuously accumulating risk of pesticide and fertilizer runoff from rainwater. When the ambient humidity... When humidity remains consistently high, the integral value of ambient humidity increases, causing the overall value of the exponential term to decrease, which in turn affects the corrected metabolic coefficient. Attenuation occurs.

[0046] It should be further added that this invention simulates spectral reflectance experiments under different environmental humidity conditions in a controlled weather chamber, establishes the correspondence between reflectance deviation and the integral value of environmental humidity using regression analysis, and uses the calculated proportionality coefficient as a humidity sensitivity constant. .

[0047] For example, Figure 2 This is a schematic diagram illustrating the changes in metabolic response coefficient and corrected metabolic coefficient over time. The figure shows that the uncorrected metabolic response coefficient exhibits high fluctuations and high-frequency jumps, which includes interference noise caused by environmental moisture on the spectral data. After processing by incorporating the integrated value of environmental humidity, the curve containing the corrected metabolic coefficient shows a clear downward peeling trend and a more stable trajectory, indicating that the false metabolic signal caused by high humidity has been successfully eliminated, restoring the true physiological absorption efficiency of the crop.

[0048] S4. Obtain the total application quality of pesticides and fertilizers and the vegetation index gradient norm. Based on the corrected metabolic coefficient, vegetation index gradient norm and total application quality of pesticides and fertilizers, obtain the comprehensive utilization efficiency score.

[0049] It should be noted that evaluating the effectiveness of pesticide and fertilizer utilization cannot only refer to the metabolic intensity at a single time point, but must also consider the duration of action of pesticides and fertilizers throughout the entire evaluation period, as well as whether pesticides and fertilizers promote the uniform growth of crops in spatial distribution. This invention integrates the modified metabolic coefficient with the vegetation index gradient norm, which characterizes spatial growth differences, in a spatiotemporal manner. This allows for a comprehensive assessment of the total contribution of pesticides and fertilizers to crop growth and their effect on the overall growth of the operating area, thereby obtaining a comprehensive utilization efficiency score that can evaluate the performance of pesticide and fertilizer utilization.

[0050] Specifically, this invention obtains the total application quality of pesticides and fertilizers through a main controller, and obtains a comprehensive utilization efficiency score by combining the modified metabolic coefficient and the variation law of vegetation index.

[0051] The comprehensive utilization efficiency score satisfies the expression:

[0052] In the formula, This indicates the score for comprehensive utilization efficiency; Indicates the duration of the evaluation period; Indicates time Corrected metabolic coefficient; Indicates time The vegetation index gradient norm; Indicates the total application quality of pesticides and fertilizers; This represents a constant to prevent the denominator from being zero, and its value is... ; Gradient operators that represent the degree of drastic change in the computation space; This represents the norm operation used to calculate the length of a vector.

[0053] In the formula, the comprehensive utilization efficiency score The higher the value, the higher the average conversion gain of the fertilizer and pesticide. Duration of the entire evaluation cycle Integrating the data, the total effective physiological contribution of pesticides and fertilizers throughout the entire cycle is summarized. The vegetation index gradient norm is used to calculate the growth differences between different locations within the plot using gradient operators, and norm operations are used to convert this into an intensity value representing growth uniformity, which is used to assess whether pesticides and fertilizers act evenly throughout the entire operating area. When adjusting the metabolic coefficient... It remains at a high level, and the vegetation index gradient norm An increase in the value of the numerator indicates improved crop growth and more even application of pesticides and fertilizers, thus increasing the integral value of the numerator and the overall application quality of pesticides and fertilizers. Under constant conditions, the final driver of comprehensive utilization efficiency score Increase.

[0054] S5. Based on the comprehensive utilization efficiency score and the preset utilization rate benchmark, obtain the frequency conversion command, and adjust the application speed of pesticides and fertilizers through the frequency conversion command to realize the automatic closed-loop adjustment of pesticide and fertilizer application dosage.

[0055] It should be noted that, in order to address the shortcomings of fixed dosage and inability to dynamically optimize according to the real-time needs of crops in traditional pesticide application, this invention constructs a closed-loop feedback system with comprehensive utilization efficiency score as its core. By monitoring crop growth feedback in real time and comparing the evaluation results with the preset utilization rate benchmark, the system can automatically determine whether the current pesticide application plan is efficient and adjust the working status of the execution mechanism accordingly, thereby achieving precise control of pesticide and fertilizer application dosage.

[0056] Specifically, the present invention uses the comprehensive utilization efficiency score as a control variable and uses a proportional-integral-derivative control strategy to determine the fertilizer and pesticide utilization status. When the comprehensive utilization efficiency score is lower than the preset utilization rate benchmark, the present invention sends a frequency conversion command to the servo driver of the pesticide pump motor through the main controller.

[0057] Furthermore, the servo driver of the spray pump motor adjusts the speed of the spray pump motor according to the frequency conversion command, and adjusts the application speed of pesticides and fertilizers in real time by changing the output frequency of the spray pump motor, thereby realizing the automated closed-loop adjustment of the application dosage of pesticides and fertilizers.

[0058] For example, Figure 3 This diagram illustrates the changes in comprehensive utilization efficiency score and fertilizer / pesticide application rate over time. The diagram shows the dynamic closed-loop adjustment process. As the comprehensive utilization efficiency score of the crop continuously climbs and exceeds the preset utilization rate benchmark, indicating good fertilizer / pesticide conversion efficiency, the system immediately sends frequency conversion commands to maintain or slightly increase the fertilizer / pesticide application rate. When the efficiency score drops below the benchmark due to absorption saturation or environmental factors, the system keenly detects this downward trend and immediately reduces the application rate. Combined with a control mechanism that stops large-scale operations at night, the goal of avoiding input waste based on actual crop growth feedback is achieved.

Claims

1. A method for evaluating the utilization effect of pesticides and fertilizers based on spectral analysis, characterized in that, include: The multispectral time series of the work area was obtained and normalized to obtain the vegetation index; Obtain the application rate of pesticides and fertilizers and the rate of change of vegetation index; obtain the metabolic response coefficient based on the application rate of pesticides and fertilizers and the rate of change of vegetation index. The environmental humidity fluctuation sequence is acquired and integrated to obtain the environmental humidity integral value. The corrected metabolic coefficient is obtained based on the metabolic response coefficient and the environmental humidity integral value. Obtain the total pesticide and fertilizer application quality and vegetation index gradient norm, and obtain the comprehensive utilization efficiency score based on the corrected metabolic coefficient, vegetation index gradient norm and total pesticide and fertilizer application quality; Based on the comprehensive utilization efficiency score and the preset utilization rate benchmark, frequency conversion commands are obtained, and the application speed of pesticides and fertilizers is adjusted through frequency conversion commands to achieve automated closed-loop regulation of pesticide and fertilizer application dosage.

2. The method for evaluating the utilization effect of pesticides and fertilizers based on spectral analysis according to claim 1, characterized in that, The metabolic response coefficient satisfies the expression: ； In the formula, Indicates time The metabolic response coefficient; Represents differentiation operations; Indicates time The vegetation index; Indicates time The speed of pesticide and fertilizer application; Indicates the physiological response lag period; This represents a constant used to prevent the denominator from being zero.

3. The method for evaluating the utilization effect of pesticides and fertilizers based on spectral analysis according to claim 2, characterized in that, The physiological response lag period is obtained as follows: The phase difference between the drug application pulse signal and the vegetation index response waveform was analyzed using the cross-correlation function, and the time deviation corresponding to the highest correlation was taken as the physiological response lag period.

4. The method for evaluating the effectiveness of pesticide and fertilizer utilization based on spectral analysis according to claim 1, characterized in that, The corrected metabolic coefficient satisfies the expression: ； In the formula, Indicates time Corrected metabolic coefficient; Indicates time The metabolic response coefficient; Indicates time The ambient humidity; This represents the humidity sensitivity constant; This represents an exponential function with the natural constant as its base.

5. The method for evaluating the effectiveness of pesticide and fertilizer utilization based on spectral analysis according to claim 4, characterized in that, The humidity sensitivity constant is obtained as follows: Regression analysis was used to establish the correspondence between reflectance deviation and the integral value of ambient humidity, and the calculated proportionality coefficient was used as the humidity sensitivity constant.

6. The method for evaluating the effectiveness of pesticide and fertilizer utilization based on spectral analysis according to claim 1, characterized in that, The comprehensive utilization efficiency score satisfies the expression: ； In the formula, This indicates the score for comprehensive utilization efficiency; Indicates the duration of the evaluation period; Indicates time Corrected metabolic coefficient; Indicates time The vegetation index gradient norm; Indicates the total quality of pesticide and fertilizer application; This represents a constant to prevent the denominator from being zero; Gradient operators that represent the degree of drastic change in the computation space; This represents the norm operation used to calculate the length of a vector.

7. The method for evaluating the utilization effect of pesticides and fertilizers based on spectral analysis according to claim 1, characterized in that, The process of acquiring the multispectral time series of the work area and performing normalization to obtain the vegetation index includes: The operation area is periodically scanned using a drone spectral imager to obtain red band reflectance data and near-infrared band reflectance data. The data are then arranged in chronological order to obtain a multispectral time series. The multispectral time series is then converted into a vegetation index using the standard normalized vegetation index calculation method.

8. The method for evaluating the utilization effect of pesticides and fertilizers based on spectral analysis according to claim 1, characterized in that, The process of obtaining frequency conversion commands based on comprehensive utilization efficiency scores and preset utilization rate benchmarks includes: The deviation between the comprehensive utilization efficiency score and the preset utilization rate benchmark is determined by the proportional-integral-derivative control strategy. When the comprehensive utilization efficiency score is lower than the preset utilization rate benchmark, the main controller sends a frequency conversion command to the servo driver of the spraying pump motor.

9. The method for evaluating the utilization effect of pesticides and fertilizers based on spectral analysis according to claim 8, characterized in that, The regulation of fertilizer and pesticide application rate includes: The servo driver of the spray pump motor adjusts the speed of the spray pump motor, and the application speed of pesticides and fertilizers is adjusted by changing the output frequency of the spray pump motor.

10. The method for evaluating the effectiveness of pesticide and fertilizer utilization based on spectral analysis according to claim 1, characterized in that, The acquisition of the environmental humidity fluctuation sequence includes: Air moisture data is collected in real time by wireless sensor nodes deployed in the work area, and the air moisture data is combined into an environmental humidity fluctuation sequence according to the time series.

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