Agricultural product pesticide residue detection method and system
By integrating data and modeling pesticide degradation patterns, pesticide residues in agricultural products can be detected quickly and accurately, solving the problems of long detection cycles and low accuracy in existing technologies, and enabling precise prediction of pesticide residue levels.
Patent Information
- Application Number
- CN202511351451.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-22
- Publication Date
- 2026-01-06
AI Technical Summary
Existing technologies for detecting pesticide residues in agricultural products suffer from problems such as long detection cycles and low accuracy.
A fusion dataset was constructed using data fusion technology. By combining the degradation patterns of pesticides in different agricultural products, a time-segmented prediction curve for pesticide residues was formed by fitting pesticide residue detection data. Based on this, the average pesticide residue in each time interval was determined. Finally, the predicted values and the average residue were fused to obtain a comprehensive prediction result for pesticide residues.
It enables rapid and accurate detection of pesticide residues in agricultural products, improving the accuracy of pesticide residue prediction.
Smart Images
Figure CN121278624A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of pesticide residue detection technology for agricultural products, and particularly to a method and system for detecting pesticide residues in agricultural products. Background Technology
[0002] Pesticides are widely used in agricultural production to improve crop yield and quality, but they also bring about pesticide residue problems. Pesticide residues can have adverse effects on human health; therefore, accurate detection and assessment of pesticide residue levels in agricultural products are particularly important. Traditional methods for pesticide residue detection typically include chemical analysis, biological detection, and immunoassay. While these methods can detect pesticide residues to some extent, they suffer from long detection cycles and low accuracy. Summary of the Invention
[0003] To address the aforementioned problems, this invention provides a method and system for detecting pesticide residues in agricultural products. This method solves the technical problems of long detection cycles and low accuracy in existing technologies for detecting pesticide residues in agricultural products. It enables rapid detection of pesticide residues in agricultural products and ensures the accuracy of pesticide residue prediction results.
[0004] This invention provides a method for detecting pesticide residues in agricultural products, comprising:
[0005] Based on operational data of agricultural product samples, soil monitoring data, meteorological monitoring data, and environmental monitoring data, a fused dataset was constructed using data fusion technology.
[0006] The fusion dataset of the agricultural products to be tested is input into a pre-built pesticide prediction model to obtain the predicted pesticide residue values of the agricultural product samples to be tested.
[0007] Based on the degradation patterns of pesticides in different agricultural products, a mathematical model of pesticide residue changes over time is constructed. By fitting pesticide residue detection data, a time-segmented prediction curve of pesticide residue is generated.
[0008] Based on the time-segmented prediction curve of pesticide residues, the average pesticide residue in each time interval is determined.
[0009] The predicted pesticide residue levels are combined with the average pesticide residue levels to obtain a comprehensive pesticide residue prediction result.
[0010] In some embodiments, including:
[0011] The operational data, soil monitoring data, meteorological monitoring data, and environmental monitoring data before fusion were denoised and normalized.
[0012] In some embodiments, a mathematical model of pesticide residue variation over time is constructed based on the degradation patterns of pesticides in different agricultural products. By fitting pesticide residue detection data, a time-segmented prediction curve for pesticide residue is generated. The mathematical model is as follows:
[0013] C(t) = C0 × e -kt ;
[0014] In the formula, C(t) represents the pesticide residue at time t, C0 is the initial pesticide residue, and k is the degradation rate constant.
[0015] In some embodiments, determining the degradation rate constant includes:
[0016]
[0017] In the formula, k0 is the baseline degradation rate constant, T and T0 are the current temperature and the baseline temperature, respectively, and E a R is the activation energy, φ(H) is the gas constant, and φ(H) is the humidity correction function, which corrects the degradation rate constant based on the humidity H.
[0018] In some embodiments, determining the average pesticide residue level for each time interval based on the time-segmented pesticide residue prediction curve includes:
[0019]
[0020] In the formula, This represents the average pesticide residue within the time interval [t1, t2], where T = t2 - t1 is the length of the time interval.
[0021] This invention provides a pesticide residue detection system for agricultural products, comprising:
[0022] The first fusion module is used to construct a fused dataset based on agricultural product sample operation data, soil monitoring data, meteorological monitoring data, and environmental monitoring data using data fusion technology.
[0023] The pesticide prediction module is used to input the fused dataset of the agricultural products to be tested into a pre-built pesticide prediction model to obtain the predicted value of pesticide residue in the agricultural product sample.
[0024] The module is used to construct a mathematical model of pesticide residue changes over time based on the degradation patterns of pesticides in different agricultural products. By fitting pesticide residue detection data, a time-segmented prediction curve of pesticide residue is generated.
[0025] The determination module is used to determine the average pesticide residue level in each time interval based on the pesticide residue level prediction curve for different time periods.
[0026] The second fusion module is used to fuse the predicted pesticide residue values with the average pesticide residue values to obtain a comprehensive pesticide residue prediction result.
[0027] In some embodiments, including:
[0028] The preprocessing module is used to denoise and normalize the operational data, soil monitoring data, meteorological monitoring data, and environmental monitoring data before fusion.
[0029] In some embodiments, the mathematical model is:
[0030] C(t) = C0 × e -kt ;
[0031] In the formula, C(t) represents the pesticide residue at time t, C0 is the initial pesticide residue, and k is the degradation rate constant.
[0032] In some embodiments, determining the degradation rate constant includes:
[0033]
[0034] In the formula, k0 is the baseline degradation rate constant, T and T0 are the current temperature and the baseline temperature, respectively, and E a R is the activation energy, φ(H) is the gas constant, and φ(H) is the humidity correction function, which corrects the degradation rate constant based on the humidity H.
[0035] In some embodiments, the determining module includes:
[0036]
[0037] In the formula, This represents the average pesticide residue within the time interval [t1, t2], where T = t2 - t1 is the length of the time interval.
[0038] Compared with the prior art, the present invention has the following beneficial effects:
[0039] Based on operational data from agricultural product samples, soil monitoring data, meteorological monitoring data, and environmental monitoring data, a fusion dataset is constructed using data fusion technology. This fusion dataset of the agricultural products to be tested is then input into a pre-constructed pesticide prediction model to obtain predicted pesticide residue levels for the samples. Based on the degradation patterns of pesticides in different agricultural products, a mathematical model of pesticide residue changes over time is constructed. By fitting pesticide residue detection data, a time-segmented pesticide residue prediction curve is generated. Based on this time-segmented prediction curve, the average pesticide residue level within each time interval is determined. Finally, the predicted pesticide residue levels are fused with the average pesticide residue levels to obtain a comprehensive pesticide residue prediction result. This approach enables rapid detection of pesticide residues in agricultural products and ensures the accuracy of pesticide residue prediction results. Attached Figure Description
[0040] The embodiments of the present invention will be further described below with reference to the accompanying drawings:
[0041] Figure 1 This is a schematic diagram illustrating the implementation process of a pesticide residue detection method for agricultural products provided in an embodiment of the present invention;
[0042] Figure 2 This is a schematic diagram of the structure of an agricultural product pesticide residue detection system provided in an embodiment of the present invention;
[0043] Figure 3 This is a schematic diagram of the composition structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation
[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0045] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.
[0046] If the application documents contain similar descriptions such as "first, second, third", the following explanation shall be added: In the following description, the terms "first, second, third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.
[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.
[0048] To address the problems existing in related technologies, this invention provides a method for detecting pesticide residues in agricultural products. The subject of this method can be an electronic device. The electronic device can be various types of terminals such as laptops, tablets, desktop computers, set-top boxes, and mobile devices (e.g., mobile phones, portable music players, personal digital assistants, dedicated messaging devices, portable gaming devices), or it can be implemented as a server. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.
[0049] In some embodiments, the function implemented by the detection method provided in this invention can be achieved by the processor of an electronic device calling program code, wherein the program code can be stored in a computer storage medium.
[0050] This invention provides a method for detecting pesticide residues in agricultural products. Figure 1 This is a schematic diagram illustrating the implementation process of a pesticide residue detection method for agricultural products provided in an embodiment of the present invention, as shown below. Figure 1 As shown, it includes:
[0051] Step S1: Based on the operational data of agricultural product samples, soil monitoring data, meteorological monitoring data, and environmental monitoring data, a fused dataset is constructed using data fusion technology;
[0052] In this embodiment of the invention, operational data may include application time and dosage; soil monitoring data may include pH value and organic matter content; meteorological monitoring data may include temperature, humidity, and rainfall; and environmental monitoring data may include air quality index and light intensity. A database structure is designed to store and organize all collected data. Preprocessed data is imported into the database; for example, SQL statements can be used to insert data into corresponding tables. This improves the comprehensiveness and accuracy of the data, providing a richer information foundation for subsequent pesticide residue prediction.
[0053] Step S2: Input the fused dataset of the agricultural products to be tested into the pre-built pesticide prediction model to obtain the predicted pesticide residue values of the agricultural product samples to be tested;
[0054] In this embodiment of the invention, the pesticide prediction model can be a convolutional neural network (CNN) model, whose network structure includes convolutional layers, pooling layers, and fully connected layers. Convolutional operations extract spatial features of the data, pooling operations reduce data dimensionality, and fully connected layers perform feature fusion and classification prediction. The output layer of the model uses the Softmax function for probabilistic processing to obtain the predicted probability distribution of pesticide residues. Alternatively, the pesticide prediction model can be a recurrent neural network (RNN) model, whose network structure includes an input layer, hidden layers, and an output layer. The recurrent structure captures the time-series features of the data. The hidden layers use a Long Short-Term Memory (LSTM) network or a gated recurrent unit (GRU) structure to solve the gradient vanishing and gradient exploding problems of traditional RNNs. The output layer of the model also uses the Softmax function for probabilistic processing to obtain the predicted probability distribution of pesticide residues.
[0055] Step S3: Based on the degradation patterns of pesticides in different agricultural products, construct a mathematical model of pesticide residue changes over time, and form a time-segmented prediction curve for pesticide residue by fitting pesticide residue detection data;
[0056] In some embodiments, the mathematical model is:
[0057] C(t) = C0 × e -kt ;
[0058] In the formula, C(t) represents the pesticide residue at time t, C0 is the initial pesticide residue, and k is the degradation rate constant.
[0059] In this embodiment of the invention, a mathematical model is used to characterize the degradation pattern of pesticide residues over time. By fitting pesticide residue detection data, the corresponding degradation rate constant of agricultural products can be obtained, thereby forming a time-segmented prediction curve for pesticide residues. By providing a quantitative model to describe the degradation process of pesticide residues, prediction and control are facilitated.
[0060] In some embodiments, determining the degradation rate constant includes:
[0061]
[0062] In the formula, k0 is the baseline degradation rate constant, T and T0 are the current temperature and the baseline temperature, respectively, and E a R is the activation energy, φ(H) is the gas constant, and φ(H) is the humidity correction function, which corrects the degradation rate constant based on the humidity H.
[0063] In this invention, the degradation rate constant k of pesticide residues is generally related to temperature and humidity; typically, the degradation rate of pesticides increases with increasing temperature. This relationship can be described by the Arrhenius equation, i.e. Humidity also affects the degradation rate of pesticides because it can influence pesticide solubility and microbial activity, both of which can affect pesticide degradation. A humidity correction function φ(H) is used to adjust the degradation rate constant k based on the actual humidity H. Therefore, this invention comprehensively considers the effects of temperature and humidity on the degradation rate. A correction formula is used to adjust the degradation rate constant (benchmark degradation rate constant k0) of agricultural products obtained by fitting pesticide residue detection data, resulting in the degradation rate constant of the mathematical model. This allows for more accurate prediction of pesticide residue changes over time, thereby improving the accuracy of agricultural product quality and safety assessments.
[0064] Step S4: Based on the time-segmented prediction curve of pesticide residues, determine the average pesticide residue level in each time interval;
[0065] In some embodiments, step S4 includes:
[0066]
[0067] In the formula, This represents the average pesticide residue within the time interval [t1, t2], where T = t2 - t1 is the length of the time interval.
[0068] In this embodiment of the invention, by calculating the average pesticide residue level within a specific time interval, the pesticide residue risk of agricultural products during that time period can be assessed more accurately, thereby providing a scientific basis for the safe consumption of agricultural products.
[0069] Step S5: Combine the predicted pesticide residue value with the average pesticide residue value to obtain a comprehensive pesticide residue prediction result.
[0070] In this embodiment of the invention, by fusing the predicted pesticide residue value with the average pesticide residue value, a comprehensive pesticide residue prediction result is obtained, which can quickly realize the detection of pesticide residues in agricultural products and ensure the accuracy of the pesticide residue prediction result.
[0071] In some embodiments, including:
[0072] The operational data, soil monitoring data, meteorological monitoring data, and environmental monitoring data before fusion were denoised and normalized.
[0073] In this embodiment of the invention, the operational data, soil monitoring data, meteorological monitoring data, and environmental monitoring data before fusion are denoised to eliminate outliers and noise. The operational data, soil monitoring data, meteorological monitoring data, and environmental monitoring data before fusion are also normalized to ensure they are on the same scale, facilitating model processing.
[0074] Based on the foregoing embodiments, this invention provides a pesticide residue detection system for agricultural products. The system includes various modules and units, which can be implemented by a processor in a computer device; of course, it can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.
[0075] This invention provides a pesticide residue detection system for agricultural products. Figure 2 This is a schematic diagram of the structure of an agricultural product pesticide residue detection system provided in an embodiment of the present invention, as shown below. Figure 2 As shown, it includes:
[0076] The first fusion module is used to construct a fused dataset based on agricultural product sample operation data, soil monitoring data, meteorological monitoring data, and environmental monitoring data using data fusion technology.
[0077] The pesticide prediction module is used to input the fused dataset of the agricultural products to be tested into a pre-built pesticide prediction model to obtain the predicted value of pesticide residue in the agricultural product sample.
[0078] The module is used to construct a mathematical model of pesticide residue changes over time based on the degradation patterns of pesticides in different agricultural products. By fitting pesticide residue detection data, a time-segmented prediction curve of pesticide residue is generated.
[0079] The determination module is used to determine the average pesticide residue level in each time interval based on the pesticide residue level prediction curve for different time periods.
[0080] The second fusion module is used to fuse the predicted pesticide residue values with the average pesticide residue values to obtain a comprehensive pesticide residue prediction result.
[0081] In some embodiments, including:
[0082] The preprocessing module is used to denoise and normalize the operational data, soil monitoring data, meteorological monitoring data, and environmental monitoring data before fusion.
[0083] In some embodiments, the mathematical model is:
[0084] C(t) = C0 × e -kt ;
[0085] In the formula, C(t) represents the pesticide residue at time t, C0 is the initial pesticide residue, and k is the degradation rate constant.
[0086] In some embodiments, determining the degradation rate constant includes:
[0087]
[0088] In the formula, k0 is the baseline degradation rate constant, T and T0 are the current temperature and the baseline temperature, respectively, and E a R is the activation energy, φ(H) is the gas constant, and φ(H) is the humidity correction function, which corrects the degradation rate constant based on the humidity H.
[0089] In some embodiments, the determining module includes:
[0090]
[0091] In the formula, This represents the average pesticide residue within the time interval [t1, t2], where T = t2 - t1 is the length of the time interval.
[0092] It should be noted that, in the embodiments of the present invention, if the above-described detection method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of the present invention are not limited to any specific hardware and software combination.
[0093] Accordingly, embodiments of the present invention provide a storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps in the detection method provided in the above embodiments.
[0094] This invention provides an electronic device; Figure 3 This is a schematic diagram of the composition structure of an electronic device provided in an embodiment of the present invention, such as... Figure 3As shown, the electronic device 400 includes: a processor 401, at least one communication bus 402, a user interface 403, at least one external communication interface 404, and a memory 405. The communication bus 402 is configured to enable communication between these components. The user interface 403 may include a display screen, and the external communication interface 404 may include standard wired and wireless interfaces. The processor 401 is configured to execute a program of a detection method stored in the memory to implement the steps of the detection method provided in the above embodiment.
[0095] It should be noted that the descriptions of the storage medium and electronic device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of the present invention, please refer to the descriptions of the method embodiments of the present invention for understanding.
[0096] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of the invention, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the invention. The sequence numbers of the above-described embodiments of the invention are merely descriptive and do not represent the superiority or inferiority of the embodiments.
[0097] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, object, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, object, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, object, or apparatus that includes that element.
[0098] In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.
[0099] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.
[0100] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.
[0101] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.
[0102] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.
[0103] The above description is merely an embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A method for detecting pesticide residues in agricultural products, characterized by, The application comprises the following steps: According to the operation data of agricultural product samples, soil monitoring data, meteorological monitoring data and environmental monitoring data, a data fusion technology is used to construct a fusion data set; The fusion data set of the agricultural product to be tested is input into a pre-constructed pesticide prediction model to obtain a pesticide residue prediction value of the agricultural product sample to be tested; According to the degradation law of pesticides in different agricultural products, a mathematical model of pesticide residue changing with time is constructed, and a pesticide residue prediction curve in different time periods is formed by fitting pesticide residue detection data; Based on the pesticide residue prediction curve in different time periods, the average pesticide residue in each time interval is determined; The pesticide residue prediction value and the average pesticide residue are fused to obtain a comprehensive pesticide residue prediction result.
2. The method according to claim 1, wherein, The application comprises the following steps: The operation data, soil monitoring data, meteorological monitoring data and environmental monitoring data before fusion are denoised and normalized.
3. The method according to claim 1, wherein, The mathematical model of pesticide residue changing with time is constructed according to the degradation law of pesticides in different agricultural products, and a pesticide residue prediction curve in different time periods is formed by fitting pesticide residue detection data, wherein the mathematical model is as follows: C(t) = Co x e -kt ; In the formula, C(t) represents the pesticide residue at time t, C0 is the initial pesticide residue, and k is the degradation rate constant.
4. The method according to claim 3, wherein, The determination of the degradation rate constant comprises the following steps: where k0 is a reference degradation rate constant, T and To are the current temperature and the reference temperature, respectively, E is the activation energy, R is the gas constant, and φ(H) is a humidity correction function that corrects the degradation rate constant according to the humidity H. a where k0 is a reference degradation rate constant, T and To are the current temperature and the reference temperature, respectively, E is the activation energy, R is the gas constant, and φ(H) is a humidity correction function that corrects the degradation rate constant according to the humidity H.
5. The method according to claim 3, wherein the method is characterized by, The determination module comprises the following steps: In the formula, denotes the average residue of the pesticide in the time interval [t1, t2], and T = t2- t1 is the length of the time interval.
6. An agricultural product pesticide residue detection system characterized by comprising: The application comprises the following steps: A first fusion module is configured to construct a fusion data set according to operation data of agricultural product samples, soil monitoring data, meteorological monitoring data and environmental monitoring data by using a data fusion technology; A pesticide prediction module is configured to input the fusion data set of the agricultural product to be tested into a pre-constructed pesticide prediction model to obtain a pesticide residue prediction value of the agricultural product sample to be tested; A construction module is configured to construct a mathematical model of pesticide residue changing with time according to the degradation law of pesticides in different agricultural products, and form a pesticide residue prediction curve in different time periods by fitting pesticide residue detection data; A determination module is configured to determine the average pesticide residue in each time interval based on the pesticide residue prediction curve in different time periods; A second fusion module is configured to fuse the pesticide residue prediction value and the average pesticide residue to obtain a comprehensive pesticide residue prediction result.
7. The agricultural product pesticide residue detection system according to claim 6, wherein The application comprises the following steps: A preprocessing module is configured to denoise and normalize the operation data, soil monitoring data, meteorological monitoring data and environmental monitoring data before fusion.
8. The agricultural product pesticide residue detection system of claim 6, wherein, The mathematical model is as follows: C(t) = Co x e -kt ; In the formula, C(t) represents the pesticide residue at time t, C0 is the initial pesticide residue, and k is the degradation rate constant.
9. The agricultural product pesticide residue detection system according to claim 8, wherein, The determination of the degradation rate constant comprises the following steps: where k0 is the reference degradation rate constant, T and To are the current and reference temperatures, respectively, E is the activation energy, R is the gas constant, and φ(H) is a humidity correction function that corrects the degradation rate constant based on the humidity H. a where k0 is the reference degradation rate constant, T and To are the current and reference temperatures, respectively, E is the activation energy, R is the gas constant, and φ(H) is a humidity correction function that corrects the degradation rate constant based on the humidity H.
10. The agricultural product pesticide residue detection system of claim 8, wherein, The determination module comprises the following steps: In the formula, denotes the average residue of the pesticide in the time interval [t1, t2], and T = t2- t1 is the length of the time interval.