Agricultural product pesticide residue detection method and system based on artificial intelligence
By acquiring soil and atmospheric environmental parameters in real time, combined with femtosecond laser and quantum dot probe technology, a pesticide degradation rate model is established to identify pesticide types and calculate initial concentrations. This solves the problem that traditional pesticide residue detection methods cannot take environmental factors into account, and realizes real-time dynamic monitoring and accurate prediction of pesticide residues in agricultural products.
Patent Information
- Application Number
- CN202510748840.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-06
- Publication Date
- 2025-09-26
AI Technical Summary
In the existing technology, traditional pesticide residue detection methods mainly perform static analysis under laboratory conditions, which cannot take into account the impact of environmental factors on pesticide degradation in real time, resulting in deviations between the test results and the actual situation.
By acquiring soil and atmospheric environmental parameters in real time, a direct correlation model between environmental parameters and pesticide degradation rate is established. Femtosecond laser is used to excite the chemical bond vibration of pesticide molecules. Quantum dot probes and photoelectric sensors are combined to obtain chemical bond resonance images, extract laser-induced phonon resonance frequency characteristics, identify pesticide types and calculate initial concentrations, thereby achieving dynamic monitoring and accurate prediction of pesticide residues.
It realizes real-time dynamic monitoring and accurate prediction of pesticide residues in agricultural products, improves detection efficiency and accuracy, and can comprehensively consider the impact of pesticide types and environmental factors on residue amounts to achieve more accurate quantitative evaluation.
Smart Images
Figure CN120703041A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of agricultural detection technology, and in particular to an artificial intelligence-based agricultural product pesticide residue detection method and system. Background Art
[0002] Pesticides are widely used in modern agriculture, effectively improving crop yields and quality. However, the resulting pesticide residue problem threatens human health and the ecological environment. Current pesticide residue detection technologies primarily include gas chromatography, liquid chromatography, chromatography-mass spectrometry, and rapid methods such as enzyme inhibition and immunoassay. These technologies vary in accuracy, sensitivity, and applicability, but generally focus on static quantitative analysis.
[0003] However, conventional pesticide residue detection methods currently available primarily focus on static analysis under laboratory conditions. These methods are often unable to capture real-time soil and atmospheric parameters in the agricultural product's actual growing environment. Consequently, existing technologies struggle to accurately reflect the impact of environmental factors on pesticide degradation, leading to discrepancies between test results and actual residue levels. Summary of the Invention
[0004] The embodiments of the present application provide an artificial intelligence-based agricultural product pesticide residue detection method and system, which solves the problem that traditional pesticide residue detection methods in the prior art cannot fully consider the impact of environmental factors on pesticide degradation, and realizes real-time dynamic monitoring and accurate prediction of pesticide residues in agricultural products.
[0005] The embodiment of the present application provides an artificial intelligence-based method and system for detecting pesticide residues in agricultural products, comprising the following steps: acquiring environmental parameters in the soil and atmosphere in real time through sensors, simultaneously acquiring pesticide degradation rates, and performing pre-processing;
[0006] Acquire a chemical bond resonance image of the agricultural product surface when the specific chemical bond vibration of the pesticide molecules on the agricultural product surface is excited;
[0007] Based on the environmental parameters in the pretreated soil and atmosphere, a direct correlation model between environmental parameters and pesticide degradation rate was established;
[0008] Calculate the change curve of pesticide residue concentration over time based on the correlation model, output the dynamic attenuation trajectory, and express the change of residue concentration in the form of time series data;
[0009] By analyzing the chemical bond resonance image, the laser-induced phonon resonance frequency characteristics are extracted;
[0010] According to the laser-induced phonon resonance frequency characteristics, the type of pesticide is identified and the initial concentration of the pesticide is calculated;
[0011] The dynamic attenuation trajectory of pesticide residues is integrated with the type of pesticide and initial concentration to generate the final pesticide residue value.
[0012] Furthermore, the steps for obtaining a chemical bond resonance image of the surface of the agricultural product when the specific chemical bond vibration of the pesticide molecules on the surface of the agricultural product is excited are as follows:
[0013] Emitting femtosecond laser pulses onto the surface of agricultural products to stimulate the vibration signals of the chemical bonds of pesticide molecules;
[0014] Capturing the vibrational signals of chemical bonds through targeted modified quantum dot probes;
[0015] The quantum dot probe converts the captured vibration signal into a photon signal;
[0016] The released photon signals are collected by a photoelectric sensor array to generate raw photon distribution data;
[0017] Based on the generated raw photon distribution data, a chemical bond resonance image of the agricultural product surface is obtained.
[0018] Furthermore, the steps to establish a direct correlation model between environmental parameters and pesticide degradation rate are as follows:
[0019] Obtain historical soil and atmospheric environmental parameter data and corresponding pesticide degradation rate data, and preprocess the acquired data to form a training data set;
[0020] The physical characteristics of environmental parameters are extracted from the formed training data set as the input variables of the model, and the corresponding pesticide degradation rate is used as the output variable of the model;
[0021] The extracted physical characteristics and pesticide degradation rate data are trained to construct an initial correlation model between environmental parameters and pesticide degradation rate;
[0022] The trained initial correlation model was verified using the validation data set, and the final direct correlation model between environmental parameters and pesticide degradation rate was established.
[0023] Furthermore, the steps for calculating the curve of pesticide residue concentration over time according to the correlation model are as follows:
[0024] Set the pesticide residue concentration at the initial time point t0 as C0;
[0025] Discretize the time axis into n dynamic intervals, each interval length Δt i Adaptively determined by the rate of change of environmental parameters:
[0026]
[0027] Where τ is the reference time step, ‖▽Et ‖ is the gradient modulus of the environmental parameters at time t;
[0028] For each time interval [t i , t i +Δt i ]Perform iterative calculation of residuals;
[0029] The iterative calculation results are stored as discrete concentration point sets in time sequence;
[0030] The discrete concentration point set is input into the cubic spline interpolation algorithm to generate the curve of pesticide residue concentration changing with time.
[0031] Furthermore, the calculation formula for the residual iteration is:
[0032]
[0033] Where, t i +Δt i Pesticide residues at any given moment, t i The pesticide residue at the time, e is a natural constant, t i The environmental parameter vector at each moment, Θ(·) is the degradation rate function directly related to the model output, is the environmental coupling correction term, Where μ is the coupling coefficient and ΔE is the time interval Δt i Changes in internal environmental parameters.
[0034] Furthermore, the method of analyzing the chemical bond resonance image and extracting the laser-induced phonon resonance frequency characteristics includes the following steps:
[0035] When analyzing the chemical bond resonance image, the image is converted into a grayscale image to obtain a grayscale value matrix;
[0036] Perform a two-dimensional fast Fourier transform on the gray value matrix to obtain frequency domain data;
[0037] The frequency points whose amplitudes exceed the preset threshold are screened out in the frequency domain data and their frequency values are recorded to extract the characteristic set of laser-induced phonon resonance frequencies.
[0038] Furthermore, based on the laser-induced phonon resonance frequency characteristics, the method for identifying the type of pesticide is as follows:
[0039] Conduct experiments on a variety of known pesticides in advance to determine their characteristic frequencies, and establish a database containing pesticide types and corresponding characteristic frequencies;
[0040] After extracting the characteristic frequency of the sample, it is compared and matched with the characteristic frequency in the database, the similarity is calculated, and the pesticide type closest to the sample frequency characteristic is selected as the identification result.
[0041] Furthermore, the initial concentration of pesticides is calculated as follows:
[0042] Conduct experiments on known pesticides at different concentrations to determine their corresponding laser-induced phonon resonance frequency characteristics and establish a calibration curve;
[0043] The frequency characteristics of the extracted samples were substituted into the calibration curve formula to calculate the initial concentration of the pesticide.
[0044] Furthermore, the dynamic attenuation trajectory of pesticide residues is integrated with the pesticide type and initial concentration to generate the final pesticide residue value as follows:
[0045] The residual concentration data at each time point in the dynamic attenuation trajectory are correlated and matched with the pesticide type and initial concentration data, and the residual concentration C0 at the initial time point is obtained as the initial concentration. The residual concentration C at subsequent time points is calculated based on the dynamic attenuation trajectory. t ;
[0046] Then, the degradation rate correction coefficient k is determined according to the characteristics of the pesticide type, and the residual concentration data in the dynamic attenuation trajectory is corrected to obtain the corrected residual concentration;
[0047] Finally, the weighted average algorithm is used to comprehensively calculate the corrected dynamic attenuation trajectory data and the initial concentration according to the time weight to generate the final pesticide residue value.
[0048] The embodiment of the present application provides an artificial intelligence-based agricultural product pesticide residue detection system, which includes: a data acquisition module, an image acquisition module, a model building module, an attenuation trajectory output module, an initial concentration calculation module, and a pesticide residue acquisition module;
[0049] The data acquisition module is used to obtain environmental parameters in the soil and atmosphere in real time through sensors, and simultaneously obtain the degradation rate of pesticides and perform preprocessing;
[0050] The image acquisition module is used to acquire a chemical bond resonance image of the surface of the agricultural product when the specific chemical bonds of the pesticide molecules on the surface of the agricultural product are excited to vibrate;
[0051] The model building module is used to establish a direct correlation model between environmental parameters and pesticide degradation rate based on environmental parameters in the pretreated soil and atmosphere;
[0052] The attenuation trajectory output module is used to calculate the change curve of pesticide residue concentration over time based on the correlation model, output the dynamic attenuation trajectory, and represent the change of residue concentration in the form of time series data;
[0053] The initial concentration calculation module is used to extract the laser-induced phonon resonance frequency characteristics by analyzing the chemical bond resonance image;
[0054] According to the laser-induced phonon resonance frequency characteristics, the type of pesticide is identified and the initial concentration of the pesticide is calculated;
[0055] The pesticide residue acquisition module is used to fuse the dynamic attenuation trajectory of pesticide residues with the pesticide type and initial concentration to generate the final pesticide residue value.
[0056] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:
[0057] 1. Through sensors, the environmental parameters and pesticide degradation rates in the soil and atmosphere are acquired in real time, and a direct correlation model between environmental parameters and pesticide degradation rates is established. This allows the changing trend of pesticide residue concentration to be dynamically predicted according to changes in environmental parameters, thereby achieving real-time dynamic monitoring and accurate prediction of pesticide residues in agricultural products. This effectively solves the limitation of traditional pesticide residue detection methods in existing technologies that cannot consider the impact of environmental factors on pesticide degradation.
[0058] 2. By emitting femtosecond laser pulses to the surface of agricultural products to stimulate the vibration of the chemical bonds of pesticide molecules, and then using targeted modified quantum dot probes to capture the vibration signals and convert them into photon signals, the chemical bond resonance image of the agricultural product surface is obtained. By analyzing the chemical bond resonance image and extracting the laser-induced phonon resonance frequency characteristics, the type of pesticide is identified and the initial concentration is calculated, thereby realizing rapid and non-destructive detection of pesticide residues, effectively improving detection efficiency and accuracy.
[0059] 3. By integrating the dynamic attenuation trajectory of pesticide residues with the type of pesticides and initial concentration, the weighted average algorithm is used to comprehensively calculate and generate the final pesticide residue value, thereby comprehensively considering the impact of pesticide types and environmental factors on residues, thereby achieving a more accurate quantitative assessment of pesticide residues in agricultural products. BRIEF DESCRIPTION OF THE DRAWINGS
[0060] Figure 1 A flow chart of an artificial intelligence-based agricultural product pesticide residue detection method provided in an embodiment of the present application.
[0061] Figure 2 A schematic structural diagram of an artificial intelligence-based agricultural product pesticide residue detection system provided in an embodiment of the present application. DETAILED DESCRIPTION
[0062] This application provides an artificial intelligence-based method and system for detecting pesticide residues in agricultural products, addressing the difficulty of traditional pesticide residue detection methods in fully accounting for the impact of environmental factors on pesticide degradation. By acquiring soil and atmospheric environmental parameters in real time and establishing a direct correlation model between these and pesticide degradation rates, and using chemical bond resonance images to extract features, identify pesticide types, and calculate initial concentrations, this approach enables dynamic monitoring and accurate assessment of pesticide residues in agricultural products.
[0063] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.
[0064] like Figure 1 The figure shows a flow chart of an artificial intelligence-based agricultural product pesticide residue detection method provided by an embodiment of the present application. The method includes the following steps: using deployed nanoscale environmental sensors to obtain real-time environmental parameters in the soil and atmosphere, including temperature, humidity, light intensity, and soil pH value; and simultaneously obtaining pesticide degradation rates by querying a database and performing preprocessing.
[0065] Femtosecond laser pulses are used to excite specific chemical bond vibrations in pesticide molecules on the surface of agricultural products. Laser parameters are set to a safe threshold to avoid damaging the agricultural products. Targeted modified quantum dot probes are used to capture the specific chemical bond vibration signals of the pesticide molecules, generating a chemical bond resonance image on the surface of the agricultural products. This resonance image reflects the vibration characteristics of the pesticide molecules based on the environmental parameters of the pretreated soil and atmosphere.
[0066] Based on the environmental parameters in the pretreated soil and atmosphere, a direct correlation model between environmental parameters and pesticide degradation rate was established;
[0067] Calculate the change curve of pesticide residue concentration over time based on the correlation model, output the dynamic attenuation trajectory, and express the change of residue concentration in the form of time series data;
[0068] By analyzing the chemical bond resonance image, the analysis process includes feature matching and concentration estimation algorithms using pre-trained deep neural networks to extract the laser-induced phonon resonance frequency characteristics;
[0069] Based on the correspondence between the laser-induced phonon resonance frequency characteristics and the electronic structure of the pesticide molecule, which is based on the electronic energy level database of known pesticides, the pesticide type is identified and the initial pesticide concentration is calculated;
[0070] The dynamic attenuation trajectory of pesticide residues is fused with the type and initial concentration of pesticides through a tensor fusion network. The tensor fusion network reorganizes the input data into a multidimensional data structure and applies spatiotemporal convolution operations to integrate the time dimension and spatial features. During the fusion process, a convolutional neural network is used to learn pattern dependencies to generate the final pesticide residue value.
[0071] Furthermore, the steps for obtaining a chemical bond resonance image of the surface of the agricultural product when the specific chemical bond vibration of the pesticide molecules on the surface of the agricultural product is excited are as follows:
[0072] Emitting femtosecond laser pulses onto the surface of agricultural products to stimulate the vibration signals of the chemical bonds of pesticide molecules;
[0073] The vibration signal of chemical bonds is captured by a targeted modified quantum dot probe, and the antibody modified on the surface of the probe specifically binds to the pesticide molecule;
[0074] The quantum dot probe converts the captured vibration signal into a photon signal by releasing photons of corresponding wavelengths through the transition of electron energy levels inside the quantum dot.
[0075] The released photon signals are collected by a photoelectric sensor array to generate raw photon distribution data;
[0076] An image reconstruction algorithm is performed on the raw photon distribution data to generate a spatially resolved chemical bond resonance image.
[0077] Furthermore, the steps to establish a direct correlation model between environmental parameters and pesticide degradation rate are as follows:
[0078] Obtain historical soil and atmospheric environmental parameter data and corresponding pesticide degradation rate data, and perform preprocessing such as sorting and cleaning the acquired data to remove abnormal data and missing values to form a training data set;
[0079] Based on the physical characteristics of environmental parameters extracted from the training data set, including temperature, humidity, light intensity, and soil pH, etc., they are used as input variables of the model, and the corresponding pesticide degradation rate is used as the output variable of the model;
[0080] The extracted physical characteristics and pesticide degradation rate data are trained through machine learning algorithms to build an initial correlation model between environmental parameters and pesticide degradation rate;
[0081] The trained initial association model was verified using the validation dataset to evaluate the model’s accuracy and generalization ability. The model parameters were adjusted and optimized based on the validation results to determine the optimal combination of model parameters. This established a final direct correlation model between environmental parameters and pesticide degradation rates, allowing the model to be used to accurately calculate pesticide degradation rates based on real-time environmental parameter data.
[0082] Furthermore, the steps for calculating the curve of pesticide residue concentration over time according to the direct correlation model are as follows:
[0083] Set the pesticide residue concentration at the initial time point t0 as C0;
[0084] Discretize the time axis into n dynamic intervals, each interval length Δt i Adaptively determined by the rate of change of environmental parameters:
[0085]
[0086] Where τ is the reference time step, ‖▽E t ‖ is the gradient modulus of the environmental parameters at time t;
[0087] For each time interval [t i , t i +Δt i ]Perform iterative calculation of residuals;
[0088] The iterative calculation results are stored as discrete concentration point sets in time sequence;
[0089] The discrete concentration point set is input into the cubic spline interpolation algorithm to generate the curve of pesticide residue concentration changing with time.
[0090] Furthermore, the calculation formula for the residual iteration is:
[0091]
[0092] Where, t i +Δt i Pesticide residues at any given moment, t i The pesticide residue at the time, e is a natural constant, t i The environmental parameter vector at each moment, Θ(·) is the degradation rate function directly related to the model output, is the environmental coupling correction term, Where μ is the coupling coefficient and ΔE is the time interval Δt i Changes in internal environmental parameters.
[0093] Furthermore, the method of analyzing the chemical bond resonance image and extracting the laser-induced phonon resonance frequency characteristics includes the following steps:
[0094] When analyzing the chemical bond resonance image, the image is converted into a grayscale image to obtain a grayscale value matrix;
[0095] Perform a two-dimensional fast Fourier transform on the gray value matrix to obtain frequency domain data;
[0096] The frequency points whose amplitudes exceed the preset threshold are screened out in the frequency domain data and their frequency values are recorded to extract the characteristic set of laser-induced phonon resonance frequencies.
[0097] Furthermore, based on the laser-induced phonon resonance frequency characteristics, the method for identifying the type of pesticide is as follows:
[0098] Conduct experiments on a variety of known pesticides in advance to determine their characteristic frequencies, and establish a database containing pesticide types and corresponding characteristic frequencies;
[0099] After extracting the characteristic frequency of the sample, it is compared and matched with the characteristic frequency in the database, the similarity is calculated, and the pesticide type with the closest frequency characteristics to the sample is selected as the identification result;
[0100] The similarity calculation formula is:
[0101]
[0102] Among them, F 样本 is the sample frequency eigenvector, F 数据库,i is the frequency feature vector of the i-th pesticide in the database.
[0103] Furthermore, the initial concentration of pesticides is calculated as follows:
[0104] Experiments were conducted in advance on known pesticides of different concentrations to determine their corresponding laser-induced phonon resonance frequency characteristics, and a calibration curve C = af + b was established, where C is the concentration, f is the frequency characteristic, and a and b are experimentally determined coefficients.
[0105] Extract the frequency feature f of the sample 样本 Substitute into the calibration curve formula to calculate the initial concentration of pesticide C 初始 =af 样本 +b.
[0106] Furthermore, the dynamic attenuation trajectory of pesticide residues is integrated with the pesticide type and initial concentration to generate the final pesticide residue value as follows:
[0107] The residual concentration data at each time point in the dynamic attenuation trajectory are correlated and matched with the pesticide type and initial concentration data, and the residual concentration C0 at the initial time point is obtained as the initial concentration. The residual concentration C at subsequent time points is calculated based on the dynamic attenuation trajectory. t ;
[0108] Then, the degradation rate correction coefficient k is determined according to the characteristics of the pesticide type, and the residual concentration data in the dynamic attenuation trajectory is corrected to obtain the corrected residual concentration C t′ =C t ×e kΔt, where e is a natural constant and Δt is the time interval;
[0109] Finally, the weighted average algorithm is used to comprehensively calculate the corrected dynamic attenuation trajectory data and the initial concentration according to the time weight to generate the final pesticide residue value.
[0110] like Figure 2 , which is a structural diagram of an artificial intelligence-based agricultural product pesticide residue detection system provided in an embodiment of the present application, and includes: a data acquisition module, an image acquisition module, a model building module, an attenuation trajectory output module, an initial concentration calculation module, and a pesticide residue acquisition module;
[0111] The data acquisition module is used to obtain environmental parameters in the soil and atmosphere in real time through sensors, and simultaneously obtain the degradation rate of pesticides and perform preprocessing;
[0112] An image acquisition module is used to acquire a chemical bond resonance image of the surface of the agricultural product when specific chemical bonds of the pesticide molecules on the surface of the agricultural product are excited to vibrate;
[0113] A model building module is used to establish a direct correlation model between environmental parameters and pesticide degradation rate based on environmental parameters in pretreated soil and atmosphere;
[0114] The attenuation trajectory output module is used to calculate the change curve of pesticide residue concentration over time based on the direct correlation model, output the dynamic attenuation trajectory, and express the change of residue concentration in the form of time series data;
[0115] The initial concentration calculation module is used to extract the laser-induced phonon resonance frequency characteristics by analyzing the chemical bond resonance image;
[0116] According to the laser-induced phonon resonance frequency characteristics, the type of pesticide is identified and the initial concentration of the pesticide is calculated;
[0117] The pesticide residue acquisition module is used to fuse the dynamic attenuation trajectory of pesticide residues with the pesticide type and initial concentration to generate the final pesticide residue value.
[0118] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0119] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0120] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.
[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0122] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.
[0123] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.
Claims
1. A method for detecting pesticide residues in agricultural products based on artificial intelligence, characterized in that: The following steps are involved: The environmental parameters in the soil and atmosphere are acquired in real time through sensors, and the degradation rate of pesticides is obtained and pre-processed; Acquire a chemical bond resonance image of the agricultural product surface when the specific chemical bond vibration of the pesticide molecules on the agricultural product surface is excited; Based on the environmental parameters in the pretreated soil and atmosphere, a direct correlation model between environmental parameters and pesticide degradation rate was established; Calculate the change curve of pesticide residue concentration over time based on the correlation model, output the dynamic attenuation trajectory, and express the change of residue concentration in the form of time series data; By analyzing the chemical bond resonance image, the laser-induced phonon resonance frequency characteristics are extracted; According to the laser-induced phonon resonance frequency characteristics, the type of pesticide is identified and the initial concentration of the pesticide is calculated; The dynamic attenuation trajectory of pesticide residues is integrated with the type of pesticide and initial concentration to generate the final pesticide residue value.
2. The method for detecting pesticide residues in agricultural products based on artificial intelligence according to claim 1, wherein: The steps for obtaining a chemical bond resonance image of the agricultural product surface when the specific chemical bond vibration of the pesticide molecules on the agricultural product surface is excited are as follows: Emitting femtosecond laser pulses onto the surface of agricultural products to stimulate the vibration signals of the chemical bonds of pesticide molecules; Capturing the vibrational signals of chemical bonds through targeted modified quantum dot probes; The quantum dot probe converts the captured vibration signal into a photon signal; The released photon signals are collected by a photoelectric sensor array to generate raw photon distribution data; Based on the generated raw photon distribution data, a chemical bond resonance image of the agricultural product surface is obtained.
3. The method for detecting pesticide residues in agricultural products based on artificial intelligence according to claim 1, wherein: The steps to establish a direct correlation model between environmental parameters and pesticide degradation rate are as follows: Obtain historical soil and atmospheric environmental parameter data and corresponding pesticide degradation rate data, and preprocess the acquired data to form a training data set; The physical characteristics of environmental parameters are extracted from the formed training data set as the input variables of the model, and the corresponding pesticide degradation rate is used as the output variable of the model; The extracted physical characteristics and pesticide degradation rate data are trained to construct an initial correlation model between environmental parameters and pesticide degradation rate; The trained initial correlation model was verified using the validation data set, and the final direct correlation model between environmental parameters and pesticide degradation rate was established.
4. The method for detecting pesticide residues in agricultural products based on artificial intelligence according to claim 1, wherein: The steps for calculating the curve of pesticide residue concentration over time based on the correlation model are as follows: Set the pesticide residue concentration at the initial time point t0 as C0; Discretize the time axis into n dynamic intervals, each interval length Δt i Adaptively determined by the rate of change of environmental parameters: Where τ is the reference time step, is the gradient modulus of the environmental parameters at time t; For each time interval [t i , t i +Δt i ] Perform iterative calculation of residual volume; The iterative calculation results are stored as discrete concentration point sets in time sequence; The discrete concentration point set is input into the cubic spline interpolation algorithm to generate the curve of pesticide residue concentration changing with time.
5. The method for detecting pesticide residues in agricultural products based on artificial intelligence according to claim 1, wherein: The calculation formula for the residual iteration is: Where, t i +Δt i Pesticide residues at any time, t i The pesticide residue at the time, e is a natural constant, t i The environmental parameter vector at each moment, Θ(·) is the degradation rate function directly related to the model output, is the environmental coupling correction term, Where μ is the coupling coefficient and ΔE is the time interval Δt i Changes in internal environmental parameters.
6. The method for detecting pesticide residues in agricultural products based on artificial intelligence according to claim 1, wherein: The method for analyzing the chemical bond resonance image and extracting the laser-induced phonon resonance frequency characteristics includes the following steps: When analyzing the chemical bond resonance image, the image is converted into a grayscale image to obtain a grayscale value matrix; Perform a two-dimensional fast Fourier transform on the gray value matrix to obtain frequency domain data; The frequency points whose amplitudes exceed the preset threshold are screened out in the frequency domain data and their frequency values are recorded to extract the characteristic set of laser-induced phonon resonance frequencies.
7. The method for detecting pesticide residues in agricultural products based on artificial intelligence according to claim 1, wherein: According to the laser-induced phonon resonance frequency characteristics, the method of identifying the type of pesticide is as follows: Experiments were conducted on a variety of known pesticides to determine their characteristic frequencies. And establish a database containing pesticide types and corresponding characteristic frequencies; After extracting the characteristic frequency of the sample, it is compared and matched with the characteristic frequency in the database, the similarity is calculated, and the pesticide type closest to the sample frequency characteristic is selected as the identification result.
8. The method for detecting pesticide residues in agricultural products based on artificial intelligence according to claim 1, wherein: The initial concentration of pesticide is calculated as follows: Conduct experiments on known pesticides at different concentrations to determine their corresponding laser-induced phonon resonance frequency characteristics and establish a calibration curve; The frequency characteristics of the extracted samples were substituted into the calibration curve formula to calculate the initial concentration of the pesticide.
9. The method for detecting pesticide residues in agricultural products based on artificial intelligence according to claim 1, wherein: The dynamic attenuation trajectory of pesticide residues is integrated with the pesticide type and initial concentration to generate the final pesticide residue value as follows: The residual concentration data at each time point in the dynamic attenuation trajectory are correlated and matched with the pesticide type and initial concentration data, and the residual concentration C0 at the initial time point is obtained as the initial concentration. The residual concentration C at subsequent time points is calculated based on the dynamic attenuation trajectory. t ; Then, the degradation rate correction coefficient k is determined according to the characteristics of the pesticide type, and the residual concentration data in the dynamic attenuation trajectory is corrected to obtain the corrected residual concentration; Finally, the weighted average algorithm is used to comprehensively calculate the corrected dynamic attenuation trajectory data and the initial concentration according to the time weight to generate the final pesticide residue value.
10. An artificial intelligence-based agricultural product pesticide residue detection system, used to implement the artificial intelligence-based agricultural product pesticide residue detection method according to any one of claims 1 to 9, characterized in that: include: Data acquisition module, image acquisition module, model building module, attenuation trajectory output module, initial concentration calculation module, pesticide residue acquisition module; The data acquisition module is used to obtain environmental parameters in the soil and atmosphere in real time through sensors, and simultaneously obtain the degradation rate of pesticides and perform preprocessing; The image acquisition module is used to acquire a chemical bond resonance image of the surface of the agricultural product when the specific chemical bonds of the pesticide molecules on the surface of the agricultural product are excited to vibrate; The model building module is used to establish a direct correlation model between environmental parameters and pesticide degradation rate based on environmental parameters in the pretreated soil and atmosphere; The attenuation trajectory output module is used to calculate the change curve of pesticide residue concentration over time based on the correlation model, output the dynamic attenuation trajectory, and represent the change of residue concentration in the form of time series data; The initial concentration calculation module is used to extract the laser-induced phonon resonance frequency characteristics by analyzing the chemical bond resonance image; According to the laser-induced phonon resonance frequency characteristics, the type of pesticide is identified and the initial concentration of the pesticide is calculated; The pesticide residue acquisition module is used to fuse the dynamic attenuation trajectory of pesticide residues with the pesticide type and initial concentration to generate the final pesticide residue value.