Method for screening pesticide residues of crops by hyperspectral

Abstract

The present application relates to pesticide residue hyperspectral screening method of crop, utilize preset pesticide action mechanism coding table to the pesticide type to be measured is carried out identification, according to the identification result judges whether pesticide produces enzyme class response or structural change in crop surface layer or tissue layer;When determining that there is structural change, start hyperspectral feature extraction process;In the extracted hyperspectral image, analyze local spectral disturbance mode, and determine the asymmetric disturbance region according to the distribution of disturbance signal in the key physiological wave band of vegetation;Metabolic abnormality positioning is carried out on the asymmetric disturbance region by using nonlinear brightness parameter, water index parameter and pigment abnormality parameter;The present application realizes the structured modeling of pesticide type, action site and physiological response by constructing pesticide action mechanism coding table, which opens the data correlation channel between pesticide chemical characteristics and crop tissue response from the source.

Description

Technical Field

[0001] This invention relates to a hyperspectral screening method for pesticide residues, specifically a hyperspectral screening method for pesticide residues in crops. Background Technology

[0002] The prior art document CN2012102657377 discloses a device and method for detecting pesticide residues in leafy vegetables based on the fusion technology of hyperspectral images and electronic nose multi-sensor information. This method improves the test sensitivity, selectivity and repeatability, and expands the recognition range by fusing hyperspectral image acquisition with volatile odor information from electronic nose odor probes and processing the data with a real-time pattern classification system.

[0003] However, this comparative technique, which relies on the simple fusion of hyperspectral images and sensors such as electronic noses, cannot fundamentally achieve high-precision, high-throughput, and high-reliability quantitative determination of pesticide residues. This is because its data fusion mainly remains at the level of splicing heterogeneous signals and depends on predefined knowledge bases and pattern classification libraries. Its ability to distinguish pesticide residue signals in complex environments is limited, especially when low concentrations and multiple types of pesticide residues coexist. Traditional hyperspectral and electronic nose fusion techniques struggle to effectively extract weak signals and features, and are highly dependent on spectral preprocessing, feature selection, and discrimination models, thus limiting its practical application. Limited by the size of the sample library, the quality of the annotation, and the influence of environmental conditions, it is difficult to achieve the ideal detection accuracy. Compared with the deep spectral response analysis algorithm proposed in this invention, which establishes multi-level parameter coding, reaction mechanism model, and perturbation integral judgment mechanism, existing comparison techniques have not fully explored the micro-perturbation features in hyperspectral data and their correlation with pesticide action mechanisms. They lack endogenous structural analysis of different pesticide action depths and physiological responses, underestimate the contribution of spectral perturbations to pesticide residue signals, and make their sensitivity and specificity insufficient in complex crop tissue structures, thus affecting the stability and generalizability of detection.

[0004] Traditional methods for detecting leafy vegetables primarily rely on hyperspectral images and overall state features captured by electronic nose sensors. They depend on pattern classification accuracy but fail to systematically model key chemical information such as the penetration depth of different pesticide types, enzyme reaction mechanisms, and tissue structure changes. Consequently, these fusion methods struggle to adaptively adjust judgment thresholds and feature weights in the face of environmental changes and sample heterogeneity, leading to increased false positive rates and decreased repeatability. This is especially problematic in precision agriculture scenarios, where the performance of hyperspectral images and electronic nose pattern classification is limited and lacks dynamic correction mechanisms due to spectral differences between crop varieties and minute variations in pesticide residues, exacerbating detection errors. Furthermore, traditional hyperspectral and electronic nose fusion technologies typically require the establishment of sample and knowledge bases. This preprocessing involves extensive manual chemical analysis and standard sample labeling, which is not only time-consuming and labor-intensive but also ill-suited to rapidly changing pesticide types and the emergence of new pesticides. If the pattern library fails to cover new situations, it becomes difficult to make effective predictions. Summary of the Invention

[0005] The purpose of this invention is to provide a hyperspectral screening method for pesticide residues in crops, thereby addressing some of the drawbacks and shortcomings pointed out in the background art.

[0006] The present invention adopts the following technical solution to solve the above-mentioned technical problems:

[0007] The system uses a pre-defined pesticide action mechanism coding table to identify the type of pesticide to be tested. Based on the identification results, it determines whether the pesticide produces enzymatic reactions or structural changes in the crop epidermis or tissue layer. If structural changes are determined to exist, the hyperspectral feature extraction process is initiated.

[0008] Local spectral perturbation patterns were analyzed in the extracted hyperspectral images, and asymmetric perturbation regions were determined based on the distribution of perturbation signals in key physiological bands of vegetation.

[0009] The asymmetric perturbation region is located for metabolic anomalies using nonlinear brightness parameters, moisture index parameters, and pigment anomaly parameters. The location results are then compared with crop category databases to identify high-risk areas for pesticide residues. Hyperspectral analysis is performed on the high-risk areas to generate pesticide residue probability determination results.

[0010] Furthermore, the pesticide action mechanism coding table consists of a pesticide type index area, a site of action parameter area, and a reaction characteristic parameter area; the pesticide type index area is used to record the category identifiers of contact, systemic, systemic, and penetrating pesticides; the site of action parameter area is used to store the penetration depth parameters of pesticides in the crop epidermis, cuticle, vascular bundles, and intercellular spaces; the reaction characteristic parameter area is used to store the esterase activity changes, redox reaction rates, and tissue structure reflectance change thresholds induced by the corresponding pesticides in crop tissues.

[0011] Furthermore, the pesticide action mechanism coding table includes characteristic parameters related to crop epidermal esterase, peroxidase, and polyphenol oxidase reactions, which are used to identify the types of enzyme reactions triggered by pesticide residues; when determining structural changes, the hyperspectral feature extraction process is triggered by comparing the tissue reflectance change threshold before and after pesticide application.

[0012] Furthermore, the analysis of the local spectral perturbation mode is characterized by calculating the spectral energy density of the vegetation water band, chlorophyll absorption band, and red edge region, and quantifying the asymmetry of energy distribution using a spectral perturbation integral function:

[0013] ;

[0014] in:

[0015] This represents the spectral perturbation integral function, used to quantify the overall shift intensity of the energy distribution in the target band; and These represent the upper and lower limits of integration in the visible light band and the near-infrared band, respectively. This is the spectral energy density function per unit wavelength, used to characterize the energy contribution of the spectrum at different wavelengths; This represents the change in crop reflectance after pesticide application, where The current reflectivity, The baseline reflectance before drug application; These are small positive numbers used to prevent the denominator from being zero and to reduce the effects of noise. It is a spectral perturbation enhancement index, used to amplify weak perturbation features; These are the phase modulation coefficients, used to reflect the periodic response of the disturbance; when the integral result... Greater than or equal to the preset threshold When the corresponding spectral segment is identified as an asymmetric perturbation region, the pesticide residue identification process is triggered; wherein, the threshold... The critical value for judging spectral perturbations determined by the system based on the calibration dataset.

[0016] Furthermore, the nonlinear brightness parameter is calculated by performing gamma correction on the reflected light signal from the crop surface; the moisture index parameter is obtained by calculating the ratio of spectral reflectance between the 970nm and 900nm wavelengths; and the pigment anomaly parameter is determined by comparing the rate of change of the spectral gradient at the red edge position.

[0017] Furthermore, the pesticide type index area includes a pesticide chemical structure classification field, used to indicate chemical category information such as organophosphates, pyrethroids, carbamates, and neonicotinoids; the pesticide type index area includes pesticide volatility parameters, photosensitivity parameters, and stability parameters under environmental temperature and humidity conditions.

[0018] Furthermore, the penetration depth parameters recorded in the action site parameter area include three types of parameters: epidermal permeability, cuticle diffusion time, and vascular bundle migration rate; the penetration depth parameters in the action site parameter area establish a corresponding index relationship with the tissue thickness parameters in the crop category database; the action site parameter area stores the penetration difference coefficients of pesticides in the three types of tissues: leaves, fruits, and stems, which are used for subsequent tissue region determination.

[0019] Furthermore, a one-to-many mapping relationship is established between the category identifier in the pesticide type index area and the enzyme activity change value in the reaction characteristic parameter area to distinguish different reaction intensities of the same type of pesticide; the action site parameter area and the reaction characteristic parameter area are associated through a parameter link field to synchronously retrieve the reaction characteristic changes of the same pesticide in tissue sites.

[0020] Furthermore, the one-to-many mapping relationship is achieved by establishing a reaction intensity matrix. The matrix uses pesticide category identifiers as row indexes and multi-level threshold ranges of enzyme activity change values ​​as column indexes, which is used to quantify the response amplitude of the same type of pesticide under different reaction conditions. The parameter link field establishes an index table, which is used to simultaneously call the penetration depth parameter and the corresponding enzyme activity change value during querying.

[0021] Furthermore, the reaction characteristic parameter area includes an environmental correction field, which is used to correct the enzyme activity change value according to the temperature, humidity and light conditions at the time of sampling; the parameter link field can interface with an external crop metabolism database, and when the metabolic state of crop tissue changes beyond a preset threshold, it triggers the update and remapping of enzyme activity parameters.

[0022] The hyperspectral screening method for pesticide residues in crops proposed in this invention has significant comprehensive technological advancements and application advantages compared to existing technologies. Its beneficial effects are mainly reflected in the following aspects:

[0023] By constructing a pesticide action mechanism coding table, a structured model of pesticide type, site of action, and physiological response is achieved, establishing a data correlation channel between pesticide chemical characteristics and crop tissue response at the source. Compared with traditional detection methods that rely on empirical models or single spectral feature comparisons, this method can determine the distribution and mode of action of pesticides in crop tissue layers based on multi-dimensional features such as pesticide category identification, penetration depth parameters, and enzyme activity changes, thereby achieving a unified approach to type identification and response characteristic determination during the screening process.

[0024] A hyperspectral perturbation mode analysis mechanism is introduced, employing an integral quantization function to quantitatively analyze the energy distribution shifts in the vegetation moisture band, chlorophyll absorption band, and red edge region. This effectively overcomes the shortcomings of traditional spectral detection methods, which are sensitive to local noise and changes in illumination. By setting an asymmetric perturbation threshold and extracting the perturbation integral results, the system can accurately identify energy imbalance regions caused by pesticide residues, thus compensating for the deficiency of existing hyperspectral methods that rely solely on the overall spectral morphology and struggle to capture subtle differences in reflectance.

[0025] By integrating multi-dimensional indicators such as nonlinear brightness parameters, moisture index parameters, and pigment abnormality parameters, a multi-channel metabolic abnormality localization model is constructed. Through gamma correction of crop reflected light signals, calculation of the ratio of 970 nm to 900 nm bands, and comparison of red edge gradient change rate, the system can simultaneously monitor metabolic disturbance effects such as tissue water migration, photosynthetic pigment abnormalities, and enhanced surface structure reflectivity.

[0026] The pesticide action mechanism coding table in this invention adopts a three-layer data structure design, including a pesticide type index area, a site of action parameter area, and a reaction characteristic parameter area. The type index area can record major pesticide categories such as organophosphates, pyrethroids, carbamates, and neonicotinoids in detail, and includes parameters such as volatility, photosensitivity, and environmental stability, enabling the system to adaptively adjust the identification strategy under different environments. The site of action parameter area establishes a correspondence with the tissue thickness parameters in the crop category database to achieve dynamic correction of penetration rate, diffusion time, and migration rate. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the pesticide residue screening logic of the present invention.

[0028] Figure 2 This is a diagram showing the relationship between the pesticide action mechanism coding and spectral detection function of this invention.

[0029] Figure 3 This is a multi-dimensional parameter mapping diagram of the pesticide action mechanism of the present invention.

[0030] Figure 4 This is a schematic diagram of the reflectance curves of tomato samples before and after pesticide application in the red-edge band of Example 1 of the present invention.

[0031] Figure 5 This is a schematic diagram of the distribution of the integrand function of the spectral perturbation and the integration result in Embodiment 1 of the present invention.

[0032] Figure 6 This is a schematic diagram of the metabolic abnormality parameters and fusion score results in Example 1 of the present invention.

[0033] Figure 7 The reaction intensity matrix of Embodiment 2 of the present invention and Interval mapping diagram.

[0034] Figure 8 Environmental correction for organophosphorus pesticides in Example 2 of this invention and Impact diagram. Detailed Implementation

[0035] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0036] like Figure 1 As shown, this embodiment of the invention provides a method for screening pesticide residues in crops using a pesticide action mechanism coding table. This method establishes a pesticide action mechanism coding table that can be automatically identified by the system, enabling the identification and judgment of the type of pesticide to be tested and its biochemical reaction characteristics in crop tissues, thereby dynamically deciding whether to initiate the subsequent hyperspectral feature extraction process. The coding table pre-stores the action categories, sites of action, and typical reaction characteristic parameters of various common pesticides. Before detection, the system first calls this coding table for comparison and identification. After the detection device collects the basic information of the pesticide to be analyzed, it matches its chemical category, molecular structure characteristics, polarity coefficient, and known residue spectrum characteristics with the data stored in the coding table. Based on this, the system identifies whether the pesticide belongs to the contact, systemic, or penetrating type. Subsequently, the system extracts the corresponding action mechanism parameters based on the identified pesticide type, including data on the penetration depth of the pesticide in the crop epidermis, cuticle, vascular bundles, and intercellular spaces, as well as characteristic values ​​of the enzyme reactions or tissue structural changes it may cause.

[0037] The system performs logical judgments based on these parameters. If the judgment results show that the pesticide induces significant changes in esterase activity, increased cell membrane permeability, or damage to microstructure morphology in crop tissues, it is considered that structural changes exist. The determination criteria for structural changes are based on the reaction threshold information in the coding table. When the detected biochemical or structural parameters exceed the corresponding threshold range, the system automatically generates a positive judgment signal to trigger the hyperspectral feature extraction process. After the hyperspectral feature extraction process is initiated, the detection device activates the spectral acquisition unit to perform multi-band scanning of the crop sample and generate a hyperspectral data cube. At the same time, the band selection strategy and exposure calibration method are adjusted according to the aforementioned pesticide type parameters to ensure that the spectral features correspond to the structural changes caused by the pesticide.

[0038] The key spectral bands mainly include the water absorption band, chlorophyll absorption band, and red edge region, which reflect crop water content, chlorophyll concentration, and tissue health status. The system calculates the local shift in energy density for each band to obtain a preliminary distribution map of spectral perturbations. Then, it uses the derivative response method to detect the continuity and directionality of reflectance changes, forming a spectral perturbation function. This function combines spectral energy changes with wavelength change rates, calculates the cumulative perturbation energy value through integration, and compares the energy differences between the visible and near-infrared bands. When the energy distribution exhibits significant asymmetry between different bands, the system identifies that region as a potential perturbation zone.

[0039] To ensure the judgment results have physical meaning, the system compares the integral result of the perturbation intensity function with a preset threshold. If the integral value reaches or exceeds the threshold, it indicates the presence of a structural reflectance shift in the local spectrum caused by pesticide residues. The system labels these pixel clusters in the image space, forming asymmetric perturbation regions. These regions represent abnormal spectral responses of plant surfaces or internal tissues after being chemically treated by pesticides, exhibiting specific energy distribution imbalances. The system then performs masking segmentation on these regions for use by subsequent pesticide residue concentration analysis and classification modules.

[0040] The nonlinear brightness parameter characterizes the intensity variation of reflected light from crop surfaces. The system employs a nonlinear correction model to perform gamma-ray mapping transformation on the spectral brightness signal, enhancing the brightness contrast between low and high reflectance areas, thereby revealing the uneven reflection phenomenon caused by pesticide residues. The moisture index parameter is calculated from the ratio of reflectance in the near-infrared band to that in the moisture absorption band, describing the relative change in leaf cell water content. When this parameter deviates from normal levels, it indicates that tissue water metabolism is affected by chemical substances. The pigment abnormality parameter is obtained by comparing the rate of change of reflectance gradient in the red edge region and the chlorophyll absorption band, used to determine abnormalities in pigment synthesis or decomposition processes. The system fuses the three parameters according to their weighted proportions to generate a comprehensive metabolic abnormality score map; a higher score indicates a more significant metabolic disturbance in the tissue.

[0041] The system then calls the crop category database to compare the parameter distribution models of different crops under normal conditions.

[0042] The crop category database is uniformly constructed and maintained by the detection system, and is used to store baseline information corresponding to different crop categories and their tissue structures. The database's data sources include crop tissue measurement data collected through standardized procedures, such as statistical distribution information on pericarp thickness, leaf thickness, and stem thickness, as well as hyperspectral baseline characteristic data of the corresponding tissues obtained under unstressed conditions, including spectral indices such as red edge location characteristics and water-sensitive band ratios. Simultaneously, the database also contains basic metadata information such as crop category, variety type, and growth stage.

[0043] The key fields of the crop category database include at least crop category identification information, crop variety and growth stage information, tissue type identification information, and tissue thickness parameter information, wherein the tissue thickness parameter is stored in the form of mean, variance and value range; baseline spectral feature information or key band feature parameter information; and the spectral fluctuation threshold range allowed for the corresponding crop under normal growth conditions.

[0044] By comparing feature similarity, the system determines whether abnormal areas in the current sample exceed the normal metabolic fluctuation range of the crop. When the difference between the scoring result and the database benchmark exceeds a set threshold, the system marks the area as a high-risk area for pesticide residues. For identified high-risk areas, the system performs a high-spectral fine analysis process, fitting the energy density of the local spectral curve and calculating the perturbation trend, extracting the feature vector of reflectance change and inputting it into the residue determination module. The determination module calculates the residue probability value based on historical calibration data, outputs the probability determination result of pesticide residues in the area, and displays the risk level in a visual form on the spectral image.

[0045] like Figure 2 As shown, the pesticide action mechanism coding table is stored in a structured manner in the system database, consisting of a pesticide type index area, a site of action parameter area, and a reaction characteristic parameter area. These three areas are interconnected and can be automatically retrieved during the screening process. The pesticide type index area records the category identification information of different pesticides, specifically including four basic modes of action: contact, systemic, systemic, and penetrating. After receiving the information of the pesticide to be tested, the system first determines the pesticide's mode of action based on the category code in this index area. The site of action parameter area stores the penetration depth data of pesticides in different layers of crop structure, including the four main layers: epidermis, cuticle, vascular bundles, and intercellular spaces. The system can determine whether a pesticide has penetrating or systemic absorption capabilities based on the penetration depth parameters, thereby inferring its potential impact on tissue structure.

[0046] The reaction characteristic parameter area stores core characteristic values ​​related to pesticide biochemical reactions, including changes in esterase activity, redox reaction rates, and tissue reflectance change thresholds. These parameters constitute the key criteria for determining whether pesticides induce structural changes at the physiological level. The system reads the values ​​in the reaction characteristic parameter area and compares them with spectral and biochemical data obtained in real time. If the changes in esterase activity or redox rate exceed the reference range defined in the coding table, or if the reflectance change exceeds the set threshold, it can be confirmed that structural changes have occurred in crop tissue.

[0047] The pesticide action mechanism coding table also includes characteristic parameters related to crop epidermal esterase, peroxidase, and polyphenol oxidase reactions. The system uses these data to identify specific enzymatic reaction types triggered by pesticide residues, thus distinguishing the influence patterns of different pesticides at the enzymatic reaction level. In this way, the system can infer and judge pesticide effects at the mechanistic level in the early stages of detection. When parameter comparison results show structural anomalies, the system automatically triggers the hyperspectral feature extraction process and enters the spectral perturbation analysis stage.

[0048] This invention analyzes the water absorption band, chlorophyll absorption band, and red edge region of vegetation as the main target wavelengths, and uses the multi-band spectral energy density distribution characteristics to reveal energy transfer and reflectance perturbations caused by chemical residues. The system first calculates the spectral energy density function per unit wavelength. This is used to describe the proportion of energy contributed by different wavelengths in spectral reflectance. Subsequently, by analyzing the difference between the reflectance curves before and after drug application, the change in reflectance is obtained. This change represents the perturbation effect of pesticide action on the reflectivity of tissue surfaces. To further capture the asymmetry of energy distribution and the cumulative effect of perturbation, this invention proposes a spectral perturbation integral function. Its expression is

[0049] ;

[0050] In this formula, This represents the spectral perturbation integral function, used to quantify the shift intensity of the overall energy distribution of the target band; and These are the upper and lower limits of the integration interval, corresponding to the boundaries of the visible light and near-infrared bands, respectively; This is the spectral energy density function, used to measure the weight of energy at different wavelengths; The difference in reflectivity before and after application of the drug. The current spectral reflectance, Reference reflectivity; To prevent the denominator from being zero and to reduce the effect of random noise; It is a spectral perturbation enhancement index, used to amplify weak perturbation signals during the integration process, giving them a significant weight in the cumulative energy distribution; The phase modulation coefficient is used to introduce a periodic response to spectral perturbations, enabling the system to identify local fluctuations caused by chemical intervention.

[0051] The system calculates the rate of reflectivity change using the derivative term during integration, thus reflecting the continuous variation trend of energy density along the wavelength direction. When the integral calculation yields... The value is greater than or equal to the preset disturbance threshold. When the system determines that there is a significant asymmetry in the energy distribution of the spectral region, it indicates that the crop tissue has undergone structural or biochemical reactions resulting in a reflection shift after being exposed to pesticides. Therefore, the system marks this region as an asymmetric perturbation region and triggers the subsequent pesticide residue identification process. The threshold... The value is determined by the system through calibration datasets and is a dynamic critical value obtained statistically based on different crops, different pesticide types, and spectrometer response characteristics.

[0052] function The derivation process includes:

[0053] Let the change in reflectance be ,in The baseline reflectance before drug application. This represents the measured reflectance after pesticide application. A normalization term is introduced to reflect the trend of disturbance intensity relative to the baseline. ,in It is a small positive number, used to avoid the denominator being zero and to reduce noise interference.

[0054] Considering that the impact of perturbation amplitude at different wavelengths on the overall energy distribution is nonlinear, a perturbation enhancement index is adopted. The normalization term is amplified by a power factor to enhance the contribution of weak perturbation signals to the integral. Further analysis reveals that the reflection changes caused by pesticide action exhibit periodic micro-oscillations in time or wavelength. To capture this high-frequency perturbation characteristic, a phase modulation coefficient is introduced. and using the sine function Periodically map the perturbation changes to make the function locally sensitive to directional changes.

[0055] Combined with spectral energy density function It represents the light energy weight per unit wavelength, obtained through multiplication. Energy-weighted differentiation is performed to capture the cumulative effect of perturbation rate changes at different wavelengths. Integrating this product across the visible and near-infrared bands yields the cumulative energy value of the perturbation. The integral function integrates four factors: spectral energy, perturbation amplitude, rate of change, and periodic shift, and can simultaneously reflect the high-frequency perturbation and asymmetric energy shift caused by pesticide residues.

[0056] when The value reaches or exceeds the threshold This indicates that the vegetation spectrum experienced significant asymmetric perturbations between key physiological bands, and this segment can be identified as an area affected by pesticide residues. The entire derivation process combines spectral physical changes with mathematical calculus, transforming perturbation characteristics from empirical judgments into quantifiable integral determinations.

[0057] Disturbance Enhancement Index This term describes the nonlinear contribution of the relative change in reflectance caused by pesticide residues to the integral discrimination. When the pesticide residue concentration is low or the presence of texture or waxy layer on the crop surface dilutes the perturbation signal, the normalized perturbation term ( The amplitude of ) usually falls within a small range, so a power coefficient is used. It can increase the weight of weak perturbations in the integral accumulation, thereby improving the sensitivity to low residue and early stress; when the sample spectrum signal-to-noise ratio is high or the residual perturbation is strong, a smaller β can be used to avoid excessive amplification of noise.

[0058] Phase modulation coefficient Used to characterize the oscillation or alternating shift of the perturbation term in the wavelength dimension, through ( The perturbation directionality and local fluctuations are mapped to an accumulative sign-sensitive quantity, enabling the algorithm to distinguish the asymmetric energy shift caused by pesticide stress even when there is a superposition of tissue scattering enhancement and absorption changes.

[0059] The value is determined by the system on the calibration dataset: under different crop types, tissue parts, and pesticide categories, it is determined by the residue determination accuracy or recall rate or... The optimal range is obtained by grid search or cross-validation with the threshold separation as the target, and can be adaptively corrected in actual operation according to crop tissue thickness, instrument spectral resolution and sample signal-to-noise ratio.

[0060] In a general implementation that does not limit specific crops or pesticides, A value of 1.5–3.5 is acceptable. A value of 0.8–2.0 is acceptable; when the mechanism of action coding table indicates deeper penetration, stronger systemicity, or greater crop tissue thickness, a smaller value should be preferred. and appropriately increase To enhance the response to changes in the morphology of the red edge and near-infrared bands; when the indicator is mainly epidermal adhesion or low-dose residue, the larger value should be preferentially selected. To improve the separability of weak perturbations.

[0061] In this embodiment, the nonlinear brightness parameter, moisture index parameter, and pigment anomaly parameter analyze the asymmetric perturbation region from three dimensions: brightness change, moisture content change, and pigment metabolism change, respectively. To obtain the nonlinear brightness parameter, the system first performs gamma correction on the crop surface reflected light signal. By nonlinearly mapping the reflected light intensity, subtle changes in the low-brightness area are amplified while suppressing the saturation effect in the high-brightness area. The corrected brightness signal can more clearly express the nonlinear brightness shift caused by changes in reflection behavior due to pesticide action. The system calculates the nonlinear brightness parameter based on the correction result to reflect the degree of imbalance in the brightness structure. The moisture index parameter is constructed using the reflectance characteristics most sensitive to moisture in the near-infrared band. The system extracts reflectance values ​​from the 970 nm and 900 nm bands and calculates their ratio to describe the relative change in plant cell water content in this region.

[0062] When pesticide residues cause cellular water evaporation or water channel blockage, this ratio will significantly deviate from the normal reference range, thus becoming a reliable indicator of abnormal water metabolism. Pigment abnormality parameters are determined by analyzing the rate of change of the spectral gradient at the red edge position. The system first locates the wavelength range of the red edge region, then calculates the gradient slope of the reflectance gradient with wavelength in that region. When plants are subjected to pesticide stress, chlorophyll structure and content often change, leading to a shift in the red edge gradient or an abnormal rate of change. These subtle changes can be quantified through pigment abnormality parameters. The system integrates these three parameters to assess the metabolic state of tissues. If any parameter or its combination exceeds the normal metabolic fluctuation range of the crop in the database, the system can determine that there is an abnormal metabolic phenomenon in that region and designate it as a high-risk area for pesticide residues, providing accurate data for subsequent hyperspectral residue identification.

[0063] like Figure 3 As shown in this embodiment of the invention, to enable the detection system to comprehensively identify the chemical properties, penetration behavior, and biochemical characteristics of different pesticides in crop tissues, a pesticide action mechanism coding table is constructed as a multi-dimensional parameter set, whose content is divided into three functional units: a pesticide type index area, a site of action parameter area, and a reaction characteristic parameter area. The pesticide type index area records the chemical category, physical properties, and environmental sensitivity of pesticides. The chemical structure classification field stores pesticide types with different structural systems, such as organophosphates, pyrethroids, carbamates, and neonicotinoids. Typical toxicokinetic characteristics of this type of pesticide can be inferred from the chemical structure information. This index area also includes volatility parameters, photolysis sensitivity parameters, and environmental stability parameters, which respectively describe the pesticide's volatilization rate in a normal temperature environment, its decomposition sensitivity under natural light conditions, and its degradation trend under different temperature and humidity conditions, thus providing a basic reference for the possible modes of action of pesticides in actual field environments.

[0064] The site-of-action parameter area is used to precisely describe the penetration behavior of pesticides in different tissue layers of crops. This area stores three core penetration depth parameters: epidermal permeability, cuticle diffusion time, and vascular bundle migration rate. These parameters allow the system to infer the arrival speed and distribution range of pesticides in different tissue layers. Furthermore, the penetration depth information in this parameter area establishes a corresponding index relationship with the tissue thickness parameters in the crop category database. The system automatically corrects the penetration inference based on the differences in tissue structure among different crop categories, enabling the penetration data to be adaptable across crops. This parameter area also stores the penetration difference coefficients of pesticides in three main crop tissues: leaves, fruits, and stems. This is used to determine the degree of pesticide influence in different tissue regions after spectral detection, combined with tissue location identification algorithms.

[0065] The reaction characteristic parameter area records biochemical reaction data triggered by pesticides in crops and establishes a connection with the two parameter areas mentioned above through a parameter association mechanism. A one-to-many mapping relationship is established between the category identifier in the pesticide type index area and the esterase activity change values ​​in the reaction characteristic parameter area. Pesticides of the same class may exhibit different intensities of enzyme activity inhibition or enhancement depending on their dosage, structural derivatives, or mode of action. This mapping relationship allows the system to distinguish different reaction intensities among pesticides of the same class, thereby improving the accuracy of biochemical interpretation. Simultaneously, to achieve joint analysis of tissue level and biochemical effects, the site of action parameter area and the reaction characteristic parameter area establish a correlation mechanism through parameter link fields, enabling the system to simultaneously retrieve the reaction characteristic changes produced by the same pesticide in different tissue sites. For example, when a pesticide exhibits a significant change in redox rate in the vascular bundle region, the system can quickly locate its site of action and corresponding reaction characteristics based on the link field, achieving bidirectional verification of osmotic behavior and biochemical reactions.

[0066] This invention achieves a one-to-many mapping relationship by constructing a reaction intensity matrix. This matrix uses pesticide category identifiers as row indices and multi-level threshold ranges of enzyme activity change values ​​as column indices, ensuring that each cell in the matrix corresponds to the actual response amplitude of a specific pesticide category within a specified reaction intensity range. The reaction intensity matrix categorizes the enzyme activity test values ​​of multiple experimental samples, expressing the effects of pesticides on esterases, oxidases, or antioxidant systems using different threshold ranges. This allows the system to clearly identify the differential biochemical reaction performance between pesticides of different dosages, different action environments, or different derived structures within the same chemical category. This makes the pesticide reaction mechanics characteristics quantifiable in the coding table, facilitating the accurate triggering of subsequent spectral perturbation determination processes.

[0067] To further enhance the efficiency of joint querying of permeation and reaction parameters, the system establishes a parameter link field between the site of action parameter area and the reaction characteristic parameter area. This field, organized through an index table, automatically associates the corresponding enzyme activity change value when retrieving the permeation depth information of a specific pesticide. During the query process, this index table can simultaneously retrieve parameters such as epidermal permeability, cuticle diffusion time, and vascular bundle migration rate, while also loading enzyme activity change values. This establishes a one-to-one logical chain link between permeation behavior and biochemical reactions, enabling the system to quickly identify whether a specific permeation pathway leads to anomalies in the corresponding enzyme system during the detection phase, thereby improving the accuracy of structural change assessment.

[0068] To ensure the comparability of enzyme activity changes under different sampling conditions, an environmental correction field is included in the reaction characteristic parameter area. This field records the correction coefficients for temperature, humidity, and light intensity on the enzyme reaction rate. When comparing enzyme activity changes, the system first extracts the corresponding correction factors from the correction field based on the environmental parameters of the sampling site, and then standardizes the detected enzyme activity changes to eliminate the influence of environmental fluctuations on the enzyme reaction judgment results. For example, if an increase in sampling temperature may accelerate enzyme activity changes, the system will reduce the weight of this change value according to the correction field, ensuring that the reaction intensity reflects the actual pesticide effect rather than environmental interference.

[0069] Furthermore, to achieve dynamic adaptation of pesticide response mechanisms in cross-crop detection, the parameter link field can be connected with external crop metabolism databases.

[0070] The crop metabolism database, either built into the detection system or acquired through an external metabolic database interface, provides baseline information on the metabolic state of crops under different environmental conditions and growth stages, as well as their stress response range. This database characterizes the metabolic changes of crops under normal physiological conditions and under exogenous stress. Key fields in the crop metabolism database include at least baseline distribution information for water metabolism-related indicators, baseline characteristic information for pigment metabolism-related indicators, and response range information for antioxidant systems and related enzyme systems. The enzyme systems include at least esterases, peroxidases, and polyphenol oxidases. Additionally, it includes correction parameters such as temperature, humidity, and light intensity for environmental correction.

[0071] The detection system uses crop category identifiers, tissue type identifiers, and growth stage information as a combined index key to establish a correspondence between the crop metabolism database and the parameter fields in the pesticide action mechanism coding table. When a metabolic characteristic deviates from the corresponding baseline range and exceeds a preset threshold, the system triggers a parameter update mechanism and readjusts the relevant mapping relationships.

[0072] The crop metabolism database, either built into the detection system or acquired through an external metabolic database interface, provides baseline information on the metabolic state of crops under different environmental conditions and growth stages, as well as their stress response range. This database characterizes the metabolic changes of crops under normal physiological conditions and when subjected to exogenous stress. Key fields in the crop metabolism database include at least baseline distribution information for water metabolism-related indicators, baseline characteristic information for pigment metabolism-related indicators, and response range information for antioxidant systems and related enzyme systems. These enzyme systems include at least esterases, peroxidases, and polyphenol oxidases. It also includes correction parameters such as temperature, humidity, and light intensity for environmental correction. The detection system uses crop type identifiers, tissue type identifiers, and growth stage information as a joint index key to establish a correspondence between the crop metabolism database and parameter fields in the pesticide action mechanism coding table. When a metabolic characteristic deviates from the corresponding baseline range and exceeds a preset threshold, the system triggers a parameter update mechanism and readjusts the relevant mapping relationships.

[0073] This database stores metabolic parameters of different crops under normal and stress conditions, including water metabolism rate, pigment synthesis rate, and antioxidant system response. When the detection system identifies that the metabolic parameters of a certain tissue region deviate from the normal baseline in the database by more than a preset threshold, the system automatically triggers an enzyme activity parameter update mechanism. By recalculating the enzyme activity change value and remapping it to the reaction intensity matrix, the coding table can reflect the latest metabolic response. In this way, the present invention achieves real-time adaptive updates based on crop metabolic state, making the determination of pesticide response mechanisms more accurate and reliable.

[0074] Example 1:

[0075] The example used a batch of tomato samples from an agricultural experimental base as the research object. Some samples were treated with a certain dose of organophosphorus pesticides, while the remaining samples served as a control group without pesticide treatment. The experimental environment was maintained at a temperature of 25 degrees Celsius, a humidity of 65%, and a light intensity of 8500 lux. All environmental parameters were recorded for subsequent correction of the response characteristics.

[0076] First, construct the pesticide type index area of ​​the pesticide action mechanism coding table.

[0077] The sources of parameters in the coding table include: (1) Standardized experimental measurement data: Under controlled temperature, humidity and light conditions, permeability parameters such as permeability, diffusion time and vascular bundle migration rate are obtained through tissue sections or microscopic imaging and quantitative analysis; the activity changes of esterase, peroxidase, polyphenol oxidase and other enzymes are measured by enzymatic colorimetry or spectroscopy; the reflectance change threshold and key band response characteristics are obtained by hyperspectral acquisition before and after pesticide application. (2) Public data and registration information: For physicochemical and environmental behavior parameters such as volatility, photolysis sensitivity and environmental stability, initial values ​​can be formed by combining pesticide registration data, product instructions and public research data. (3) System calibration and online update: When the metabolic parameters of field samples continue to deviate from the crop baseline, the system re-estimates the parameters based on calibration data and environmental correction fields, and updates the mapping relationship and threshold range.

[0078] The pesticide used in this embodiment belongs to the organophosphate class, which is recorded as category 1 in the system. Pyrethroids, carbamates, and neonicotinoids are numbered 2, 3, and 4, respectively. The index area also records volatility parameters. Experiments showed that the daily volatility of this organophosphate pesticide at 25 degrees Celsius was 0.8%, the photolysis sensitivity parameter was moderate, and the stability parameter showed a stability retention rate of 62% at 30 degrees Celsius and 70% humidity.

[0079] Subsequently, a site-of-action parameter region was constructed. Preliminary experiments used microscopic tissue section analysis to obtain the penetration parameters of this organophosphorus pesticide in different tissue layers. The epidermal permeability was 48%, the cuticle diffusion time was 60 minutes, the vascular bundle migration rate was 0.35 mm / h, and the intercellular permeability index was 22%. Furthermore, this parameter region was based on external crop database records showing an average tomato peel thickness of 2.1 mm and an average tomato leaf thickness of 0.3 mm. During actual detection, the system adaptively adjusts the permeability parameters according to the sample's own tissue thickness. For example, if the local peel thickness of a tomato fruit is detected to be 2.3 mm, the system automatically scales the permeability to approximately 46% based on the ratio of 2.3 to 2.1.

[0080] Next, a region of characteristic reaction parameters was constructed. Laboratory measurements showed that 24 hours after application, tomato epidermal esterase activity decreased by 32%, peroxidase reaction rate increased by 18%, and polyphenol oxidase reaction rate increased by 21%. Spectroscopic experiments showed that the reflectance change threshold of the treated sample at 750 nm was 12%, while the reflectance change of the untreated sample at the same wavelength did not exceed 2%. Therefore, 12% was recorded as the trigger threshold for structural changes.

[0081] To verify the triggering logic of the encoding table, an actual test was conducted. The pre-treatment reflectance curve R0 of a tomato fruit was collected. Reflectance curve after drug application At the 750 nm wavelength, the reflectance before application was 0.162, rising to 0.185 after application, with a change ΔR of 0.023. Substituting the reflectance change ratio into the system's judgment model, the system determines that the threshold of 12% for structural change has been exceeded, triggering the hyperspectral feature extraction process. This process generates... Figure 4 The reflectance curves shown in the figure reveal a significant increase in reflectance in the red-edge region of the spectrum before and after pesticide application. Figure 4 The data shows that ΔR at 750 nm is 0.023, representing a change of 14.2%, which is greater than the system's set threshold of 12%, triggering the hyperspectral feature extraction process. This indicates that the increase in reflectance at 750 nm is consistent with the structural changes in crop tissue after chemical stress, meeting the criteria for enhanced reflectance.

[0082] The system identifies the pesticide as category number 1 based on the pesticide type index area. At the same time, it extracts the biochemical reaction pattern that is expected to decrease by 32% from the reaction characteristic parameter area. Combined with the vascular bundle migration rate of 0.35 mm / h and the pericarp thickness correction value in the penetration depth parameter area, it infers that the pesticide may cause structural reflection shift in both the inner layer of the pericarp and the vascular tissue, thereby triggering subsequent hyperspectral perturbation analysis.

[0083] To further verify the effectiveness of the method, spectral data from a sample area treated with the drug were substituted into the system for calculation. Taking the spectrum in the 700 nm to 900 nm range as an example, the system quantifies spectral asymmetry based on the spectral perturbation integral function. The integral formula is as follows:

[0084] ;

[0085] in =0.001, =2.5, =1.2. The system uses discrete integration, sampling once every 2 nanometers, for a total of 100 sampling points. At 750 nanometers, =0.023 / 0.162 + 0.001 = 0.1415, which, after exponential transformation, yields... =0.0075, sinusoidal modulation term =0.169. Energy density =0.94, substituting into the formula yields the integrand value of 0.00119. After accumulating and integrating over the 700 to 900 nanometer range, the perturbation integral Φλ = 0.164, which is greater than the threshold. =0.120, indicating a significant energy distribution asymmetry in the system. The results are shown below. Figure 5 The figure shows the distribution characteristics of the integrand function of the spectral perturbation. Figure 5 This indicates that the integrand increases significantly in the red-edge neighborhood, and the integral value Φ=0.164 exceeds the threshold. =0.120, the system determines that the sample is in an area affected by pesticide residues and triggers the residue identification process.

[0086] In the same wavelength band calculation, the difference in reflectance before and after drug application for the control group samples was only 0.004, corresponding to the perturbation integral. =0.037, far below the threshold of 0.120, the system did not trigger the residue identification process, and the result is consistent with the actual untreated state. Further comparison of the average perturbation integrals of the five treated samples and the five untreated samples, which were 0.158 and 0.042 respectively, showed a significant difference between the two groups, verifying the sensitivity and stability of the integral function in identifying pesticide spectral perturbations.

[0087] In this embodiment, five tomato samples treated with organophosphorus pesticides and five untreated control samples were selected for comparative testing. The spectroscopic acquisition instrument operated in the 400-1000 nm wavelength range with a resolution of 2 nm. The ambient light intensity was maintained at 8500 lux, and the sample temperature was kept at 25 degrees Celsius. The hyperspectral image of each sample had a resolution of 512×512 pixels, with each pixel representing approximately 1 square millimeter of crop surface area. Through preliminary spectral perturbation identification, the system determined that there was an asymmetric energy distribution in the red-edge region and near-infrared moisture band of the treated samples. Subsequently, metabolic anomaly localization analysis was performed in these regions.

[0088] First, the nonlinear brightness parameter is calculated. The system acquires the original brightness L of the light signal reflected from the tomato surface after pesticide application and performs gamma correction. The correction formula is:

[0089] ;

[0090] in =2.2, the original mean brightness of the treated samples was 0.42, after correction =0.68; the original brightness of the control sample was 0.38, after correction =0.64. The brightness difference is 0.04, corresponding to a brightness change rate. =0.68-0.64 / 0.64=0.0625, which exceeds the system threshold of 0.05, indicating that the surface of the drug-treated sample shows enhanced optical reflection, consistent with the tissue scattering enhancement effect.

[0091] Next, the moisture index parameter is calculated. This parameter is determined by the ratio of reflectance in the 900 nm to 970 nm wavelength bands:

[0092] ;

[0093] Reflectance of drug-treated samples =0.31, =0.27, therefore =1.148; Reflectance of control sample =0.30, =0.29, therefore =1.034. The difference between the two is about 11%, indicating that water migration in the cells of the drug-treated sample was hindered and the dehydration effect was enhanced.

[0094] Then, pigment aberration parameters were calculated. The average gradient in the red-edge region, 680–750 nm, was used. As an indicator, it is defined as follows:

[0095] ;

[0096] Experimental results show that the mean gradient of the drug-treated samples is 0.00042 nanometers. The control sample was 0.00037 nanometers. ,have to =0.135, which is higher than the threshold of 0.10, indicating that the pesticide action caused the chlorophyll absorption band to shift and the pigment metabolism to be abnormal.

[0097] The system integrates the three parameters to calculate a comprehensive metabolic abnormality score:

[0098] ;

[0099] in To obtain the normalized water parameters after demeaning, we get =0.099, which is greater than the threshold of 0.08, so the system determines that there is a metabolic abnormality. Figure 6 The bar chart showing the calculation results and the overall score is as follows. =0.0625, The difference is approximately 11%. =0.135, =0.099, exceeding the threshold of 0.08, indicating that the sample area has obvious metabolic stress characteristics. Figure 6 The explanation indicates that the fusion of the three characteristic parameters effectively revealed the spectral anomalies caused by pesticide residues, and the results were consistent with the experimentally observed chemical reaction trends.

[0100] Example 2:

[0101] Using a batch of tomato samples from a greenhouse growing area of ​​an agricultural research institute as the research object, this invention employs a hyperspectral screening method for pesticide residues in crops to systematically verify the mechanism of action, penetration characteristics, and reaction features of different types of pesticides in crop tissues. Four common pesticides were used as analytical samples: organophosphates, pyrethroids, carbamates, and neonicotinoids. The application concentration was uniformly controlled at 2.5 mg / m², the ambient temperature was maintained at 25°C, the relative humidity at 65%, and the light intensity at 8500 lux. All environmental parameters were recorded in real time to correct the spectral response data.

[0102] The system first establishes a pesticide type index area to classify and record the chemical structure information and key physicochemical properties of different pesticides. Experimental measurements showed that organophosphorus pesticides had a daily volatilization rate of 0.8% at 25°C, moderate photolysis sensitivity, and a stability retention rate of 62% at 30°C and 70% humidity. Pyrethroids had a volatilization rate of 0.35%, low photolysis sensitivity, and a stability retention rate of 79%. Carbamates had a relatively high volatilization rate of 1.2% and a stability retention rate of 55%. Neonicotinoids had the lowest volatilization rate of 0.18% and a stability retention rate of 88%. Through comparison, the system found that organophosphorus pesticides containing phosphoester bonds in their chemical structure decomposed faster under ambient light conditions, while carbamates, due to their hydrolyzable groups, were most sensitive to humidity changes. The index area thus constructed a pesticide stability matrix based on structural characteristics, providing fundamental data for subsequent determination.

[0103] Subsequently, a site-of-action parameter region was constructed to describe the penetration behavior of different pesticides in crop tissue layers. Using tissue sections and microscopic imaging, the penetration rate of organophosphorus pesticides in the tomato fruit epidermis was determined to be 48%, with a cuticle diffusion time of 60 minutes and a vascular bundle migration rate of 0.35 mm / h. Pyrethroids showed a penetration rate of 26% in the epidermis, with a diffusion time extended to 90 minutes. Carbamates had a high penetration rate of 62%, but a migration rate of only 0.21 mm / h. Neonicotinoids had a penetration rate of 41% and a migration rate of 0.39 mm / h. During calculation, the system indexed and correlated these parameters with tissue thickness information in the crop category database. For example, the average thickness of tomato peel is 2.1 mm, the average thickness of leaves is 0.3 mm, and the average thickness of stems is 1.4 mm. When the peel thickness of the tested sample was 2.3 mm, the system automatically corrected the penetration rate, calculating a correction ratio of 2.3 / 2.1 = 1.095. The corrected organophosphorus pesticide penetration rate was 48% × 1.095 ≈ 52.6%.

[0104] The system further trains the tissue identification model by establishing penetration difference coefficients for different tissue layers. For example, the penetration difference coefficients for organophosphates in fruits, leaves, and stems are 1.00, 0.74, and 0.68, respectively; for pyrethroids, they are 1.00, 0.81, and 0.59; for carbamates, they are 1.00, 0.93, and 0.85; and for neonicotinoids, they are 1.00, 0.87, and 0.79. These difference coefficients indicate that organophosphate pesticides are more likely to accumulate in the fruit epidermis, while carbamate pesticides, due to their higher diffusivity, migrate more evenly across different tissues. Using these difference parameters, the system can automatically screen high-risk residue areas in hyperspectral images based on tissue type.

[0105] Next, a reaction characteristic parameter area was established to record the changes in enzyme activity induced by various pesticides in crop tissues. The experiment measured the changes in esterase, peroxidase, and polyphenol oxidase activities in fruit peel samples within 24 hours after pesticide application: For organophosphate samples, esterase activity decreased by 32%, peroxidase activity increased by 18%, and polyphenol oxidase increased by 21%; for pyrethroids, esterase decreased by 19%, peroxidase increased by 12%, and polyphenol oxidase increased by 9%; for carbamates, esterase decreased by 28%, peroxidase increased by 16%, and polyphenol oxidase increased by 18%; neonicotinoids showed a smaller change in esterase, decreasing by only 11%, but polyphenol oxidase activity increased by 14%. The system established a one-to-many mapping relationship between category numbers and enzyme activity change values ​​in the index area to reflect the dynamic characteristics of the same type of pesticide under different reaction intensities. For example, category number 1 corresponds to organophosphates, and its esterase change threshold range is defined as -20% to -35%. If the system detects a decrease in esterase within this range, it directly classifies it as an organophosphate reaction mode.

[0106] The system establishes a reaction intensity matrix to quantify the magnitude of enzyme reactions. A comprehensive reaction intensity factor is also used. The calculation formula is:

[0107] ;

[0108] in, We took values ​​of 0.5, 0.3, and 0.2 respectively. Substituting the measured data of organophosphates into the calculation, we obtained... =25.6; Pyrethroids =14.9; carbamates =22.4. This result corresponds to a mild to strong response range and is calibrated as a metabolic response level of I to III in the system.

[0109] Figure 7 Reaction intensity matrix ( )and The interval mapping shows the correspondence between different pesticide types in various enzyme activity ranges. The calculation results indicate that: Organophosphates = 25.6, pyrethroids = 14.9, carbamates = 22.4, neonicotinoids = 11.9; each row only shows values ​​in the column corresponding to its esterase range, reflecting the quantitative characteristics of a one-to-many mapping. Ir simultaneously considers changes in esterases, peroxidases, and polyphenol oxidases, achieving quantitative differentiation of the same type of pesticides under different reaction intensities, thus providing a stable basis for pesticide type identification.

[0110] To verify the stability of the matrix under environmental fluctuations, the system introduces an environmental correction field to adjust for temperature, humidity, and light intensity changes in enzyme activity. The correction function is as follows:

[0111] ;

[0112] in The values ​​were 0.02, 0.015, and 0.01, respectively. Under the conditions of an outdoor temperature of 28 degrees Celsius, humidity of 70%, and illumination of 8200 lux, the corrected results for the organophosphorus samples were... =-32.10. Therefore, recalculate. The value before correction was 25.6, and after correction it was 25.7, an increase of 0.1. The change was only 0.3%, verifying that the system maintains high robustness under environmental changes. Figure 8 The effect of environmental correction on organophosphorus phosphorus ΔE and Ir. The figure shows that the difference between the original and corrected values ​​of ΔE is minimal, while Ir increases slightly from 25.6 to 25.7. Deviations in temperature, humidity, and light intensity are compensated for by the environmental correction field. The change in Ir is slight, and the model remains stable under external conditions, demonstrating the effectiveness of the correction mechanism.

[0113] During system operation, an index table is created for the parameter link fields to dynamically retrieve penetration depth parameters and enzyme activity values. When an external crop metabolism database detects an increase in fruit metabolic rate exceeding 10%, the system automatically triggers a parameter update mechanism, remapping the corresponding enzyme activity threshold to a new range. For example, if the external database reports a 12% increase in tomato tissue metabolic levels compared to normal, the system raises the proesterase threshold by 12%, adjusting the moderate reaction threshold from -20% to -30% to -22.4% to -33.6%, thereby ensuring more accurate screening calculations.

[0114] The results above demonstrate that the reaction intensity matrix, combined with environmental correction and dynamic parameter linkage mechanisms, can not only quantify the response amplitudes of different pesticides in multi-enzyme systems, but also maintain high sensitivity and stability under complex environments, providing a solid mathematical and experimental foundation for the hyperspectral perturbation analysis of pesticide residues.

Claims

1. A hyperspectral screening method for pesticide residues in crops, characterized in that... include: The system uses a pre-defined pesticide action mechanism coding table to identify the type of pesticide to be tested. Based on the identification results, it determines whether the pesticide produces enzymatic reactions or structural changes in the crop epidermis or tissue layer. The pesticide action mechanism coding table consists of a pesticide type index area, a site of action parameter area, and a reaction characteristic parameter area. The pesticide type index area records the category identifiers of contact, systemic, and penetrating pesticides. The site of action parameter area stores the penetration depth parameters of pesticides in the crop epidermis, cuticle, vascular bundles, and intercellular spaces. The reaction characteristic parameter area stores the changes in esterase activity, redox reaction rate, and tissue structure reflectance thresholds induced by the corresponding pesticide in crop tissues. When a structural change is detected, a hyperspectral feature extraction process is initiated. Local spectral perturbation patterns were analyzed in the extracted hyperspectral images, and asymmetric perturbation regions were determined based on the distribution of perturbation signals in key physiological bands of vegetation. The asymmetric perturbation region is located for metabolic anomalies using nonlinear brightness parameters, moisture index parameters, and pigment anomaly parameters. The location results are then compared with crop category databases to identify high-risk areas for pesticide residues. Hyperspectral analysis is performed on the high-risk areas to generate pesticide residue probability determination results.

2. The method for hyperspectral screening of pesticide residues in crops according to claim 1, characterized in that, The pesticide action mechanism coding table contains characteristic parameters related to crop epidermal esterase, peroxidase and polyphenol oxidase reactions, which are used to identify the types of enzyme reactions triggered by pesticide residues; when determining structural changes, the hyperspectral feature extraction process is triggered by comparing the tissue reflectance change threshold before and after pesticide application.

3. The method for hyperspectral screening of pesticide residues in crops according to claim 1, characterized in that, The analysis of the local spectral perturbation mode includes calculating the distribution offset of spectral energy density in the vegetation water zone, chlorophyll absorption zone, and red edge region; the determination of the asymmetric perturbation region includes determining whether the energy distribution difference of the perturbation signal in the near-infrared and visible light bands exceeds a set offset threshold.

4. The hyperspectral screening method for pesticide residues in crops according to claim 1, characterized in that, The nonlinear brightness parameter is calculated by performing gamma correction on the reflected light signal from the crop surface; the moisture index parameter is obtained by calculating the ratio of spectral reflectance between the 970nm and 900nm bands; and the pigment anomaly parameter is determined by comparing the rate of change of the spectral gradient at the red edge position.

5. The method for hyperspectral screening of pesticide residues in crops according to claim 1, characterized in that, The pesticide type index area includes a pesticide chemical structure classification field, used to indicate chemical category information for organophosphates, pyrethroids, carbamates, and neonicotinoids; the pesticide type index area includes pesticide volatility parameters, photosensitivity parameters, and stability parameters under environmental temperature and humidity conditions.

6. The method for hyperspectral screening of pesticide residues in crops according to claim 1, characterized in that, The penetration depth parameters recorded in the site of action parameter area include three types of parameters: epidermal permeability, cuticle diffusion time, and vascular bundle migration rate. The penetration depth parameters in the site of action parameter area are linked to the tissue thickness parameters in the crop category database. The site of action parameter area stores the penetration difference coefficients of pesticides in the three types of tissues: leaves, fruits, and stems, which are used for subsequent tissue region determination.

7. The method for hyperspectral screening of pesticide residues in crops according to claim 1, characterized in that, A one-to-many mapping relationship is established between the category identifier in the pesticide type index area and the enzyme activity change value in the reaction characteristic parameter area to distinguish different reaction intensities of the same type of pesticide; the action site parameter area and the reaction characteristic parameter area are associated through parameter link fields to synchronously retrieve the reaction characteristic changes of the same pesticide in tissue sites.

8. The method for hyperspectral screening of pesticide residues in crops according to claim 7, characterized in that, The one-to-many mapping relationship is achieved by establishing a reaction intensity matrix. The matrix uses pesticide category identifiers as row indexes and multi-level threshold ranges of enzyme activity change values ​​as column indexes to quantify the response amplitude of the same type of pesticide under different reaction conditions. The parameter link field establishes an index table to simultaneously call the penetration depth parameter and the corresponding enzyme activity change value during querying.

9. The method for hyperspectral screening of pesticide residues in crops according to claim 7, characterized in that, The reaction characteristic parameter area includes an environmental correction field, which is used to correct enzyme activity changes based on temperature, humidity, and light conditions during sampling; the parameter link field can interface with an external crop metabolism database, and when the metabolic state of crop tissue changes beyond a preset threshold, it triggers the update and remapping of enzyme activity parameters.

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