System and method for detecting pesticide residues for agricultural products

Abstract

The invention provides a pesticide residue detection system and method for agricultural products, and the method comprises the steps: obtaining the basic information of a to-be-detected agricultural product region, including boundary information, internal planting structure difference information and key agricultural condition information affecting the distribution of pesticide residues; secondly, dividing the area into a plurality of primary sampling units based on boundary information, dynamically determining the number of sampling points in each unit according to key agricultural condition information, generating position coordinates of the sampling points by adopting a random algorithm, and forming a total sampling point set; and ensuring that the sampling point density is matched with the residual risk weight. Generating a moving path of sampling equipment according to the adjusted sampling point set, and controlling the equipment to move along the path and execute standardized sample collection to obtain an original sample. And finally, carrying out normalized layered preprocessing on the original sample to form a mixed sample, and carrying out identification, packaging and recording. According to the scheme, dynamic sampling based on agricultural condition homogeneity and risk guidance can be realized, and the sampling representativeness, accuracy and efficiency are improved.

Description

Technical Field

[0001] This application relates to the field of agricultural product testing technology, and more specifically, to a system and method for detecting pesticide residues in agricultural products. Background Technology

[0002] Pesticide residue testing in agricultural products is a crucial step in ensuring food safety and agricultural product quality. Especially in modern agriculture, while the widespread use of pesticides has improved crop yields and resistance to pests and diseases, it has also increased the risk of residue exceeding limits. According to national food safety standards (such as GB 2763-2021 "Maximum Residue Limits for Pesticides in Food"), pesticide residue monitoring requires accurate assessment through scientific sampling. However, existing methods for sampling pesticide residues in agricultural products often employ simple grid division or equidistant random sampling. For example, the area to be tested is uniformly divided into units of fixed area, with a fixed number of sampling points within each unit. This method ignores the internal heterogeneity of agricultural product areas (such as differences in soil type, uneven application history, diverse planting structures, and topographic slope, etc.), resulting in a lack of targeted sampling point distribution and an inability to effectively capture residue variations in high-risk areas.

[0003] Specifically, the main drawbacks of traditional methods include: First, the sampling unit division is guided by the principle of uniform area rather than the homogeneity of agricultural conditions, resulting in extremely uneven residue distribution within the same unit. For example, the residue concentration in high-application areas can be more than five times that in low-application areas, leading to poor sampling representativeness and easy bias in overall residue assessment. Second, the density of sampling points does not consider risk weights, allocating points according to area proportions and ignoring insufficient coverage of small high-risk sub-areas (such as leafy vegetable areas with high incidence of pests and diseases), wasting resources on large low-risk areas. Third, sample pretreatment often involves uniformly mixing all original samples, violating the principle of stratified representativeness, resulting in dilution of high-residue samples or contamination of low-residue samples, distorting laboratory test results and making it difficult to locate specific risk sources. Fourth, the generation of sampling paths and equipment control lack optimization, resulting in low efficiency of manual or mechanical traversal and susceptibility to terrain influences, increasing errors. In addition, although current standards (such as NY / T 789-2023 "Sampling Methods for Pesticide Residue Analysis Samples") emphasize randomness and standardization, they do not fully integrate GIS and risk assessment technologies, causing sampling schemes to deviate from actual production scenarios and affecting monitoring efficiency and accuracy. Therefore, how to develop a dynamic sampling method based on the homogeneity of agricultural conditions and risk orientation to achieve accurate sampling point layout, stratified sample processing, and efficient path planning has become an urgent technical challenge. Summary of the Invention

[0004] This application provides a system and method for detecting pesticide residues in agricultural products, which can realize dynamic sampling unit division based on agricultural homogeneity and risk orientation, optimized layout of sampling points, stratified sample preprocessing, and efficient path planning, thereby improving the representativeness, accuracy, and operational efficiency of sampling.

[0005] In a first aspect, this application provides a method for sampling pesticide residues in agricultural products, comprising the following steps: Obtain basic information about the agricultural product area to be tested. The basic information includes the boundary information of the area, the internal planting structure difference information, and at least one key agricultural condition information that affects the distribution of pesticide residues. Based on the boundary information, the area of ​​agricultural products to be tested is divided into several primary sampling units. Within each primary sampling unit, the number of sampling points is dynamically determined according to the key agricultural information. A random algorithm is used to generate the location coordinates of the corresponding number of sampling points to form a total set of sampling points. The total set of sampling points is verified and adjusted based on the internal planting structure difference information to ensure that the sampling point density in the sub-regions of different planting structures matches their residual risk weights. Based on the position coordinates of each sampling point in the total set of sampling points after verification and adjustment, a movement path is generated that allows the sampling device to sequentially traverse all sampling points. The sampling device is controlled to move along the moving path, and after reaching the coordinate position of each sampling point, a standardized sample collection operation is performed to obtain the original sample; All collected raw samples were preprocessed in a standardized manner to form mixed samples, which were then labeled, packaged, and recorded for laboratory analysis of pesticide residues.

[0006] In some embodiments, based on the boundary information, the area of ​​agricultural products to be tested is divided into several primary sampling units. Within each primary sampling unit, the number of sampling points is dynamically determined according to the key agricultural information. A random algorithm is used to generate the location coordinates of the corresponding number of sampling points to form a total set of sampling points. Specifically, this includes: Based on the key agricultural information, assess the homogeneity of agricultural conditions within the area of ​​the agricultural product to be tested, and divide the sub-areas with consistent agricultural conditions into primary sampling units; Within each primary sampling unit, the residual variation degree is calculated based on the key agricultural information, and the number of sampling points is dynamically determined, with more sampling points allocated to units with a high residual variation degree. A hierarchical random algorithm is used to generate the location coordinates of sampling points, ensuring that the sampling points meet the minimum spacing constraint and avoid non-planting areas, thus forming a total set of sampling points.

[0007] In some embodiments, verifying and adjusting the total set of sampling points based on the internal planting structure difference information to ensure that the sampling point density in sub-regions of different planting structures matches their residual risk weights specifically includes: The residual risk weight of each sub-region is assessed based on the internal planting structure differences and key agricultural information, with the weight of high-risk sub-regions being higher than that of low-risk sub-regions. The total set of sampling points is verified, and the sampling point density is adjusted to match the residual risk weight, with the sampling point density of high-risk sub-regions being increased accordingly. Ensure that the adjusted sampling point distribution covers all planting structure sub-regions and complies with risk-oriented principles.

[0008] In some embodiments, generating a movement path that allows the sampling device to sequentially traverse all sampling points based on the position coordinates of each sampling point in the total set of sample points after verification and adjustment specifically includes: Extract the location coordinates from the total set of sampling points after verification and adjustment, and combine them with the terrain and accessibility information of the agricultural product area to be tested; The path optimization algorithm is used to calculate the shortest or optimal movement path of the sampling device, ensuring that the path avoids obstacles and minimizes the movement time. The output shows the generated movement path, including the order of each sampling point and the expected movement distance.

[0009] In some embodiments, controlling the sampling device to move along the movement path, and performing a standardized sample collection operation after reaching the coordinates of each sampling point to obtain the original sample specifically includes: Start the sampling equipment and automatically navigate to each sampling point along the generated movement path; Upon arrival at the sampling point, appropriate sampling tools and depths are selected based on the key agricultural information, and standardized sampling operations are performed, including collecting samples of a fixed mass and recording environmental parameters. The collected raw samples are classified and stored, and labeled by sampling point or sub-region to obtain an independent set of raw samples.

[0010] In some embodiments, the normalization preprocessing of all collected original samples to form a mixed sample, and the identification, packaging and recording of the mixed sample specifically include: The collected raw samples were stratified and grouped according to primary sampling units or residual risk levels to avoid indiscriminate mixing of all samples; Standardized preprocessing is performed on each group, including washing, cutting and equal-mass mixing, to form a mixed sample of the corresponding group, ensuring that high-residue samples are not diluted; Each pooled sample was uniquely identified, vacuum-sealed, and its grouping information, sampling point origin, and pretreatment details were recorded for laboratory analysis.

[0011] In some embodiments, basic information about the agricultural product area to be tested is obtained. This basic information includes the area's boundary information, internal planting structure differences, and at least one key agricultural condition information affecting pesticide residue distribution. Specifically, this includes: Obtain the boundary information and internal planting structure differences of the area through a GIS system or on-site survey; Collect key agricultural information that influences pesticide residue distribution, including soil type, application history, crop growth stage, and irrigation method; Integrate basic information to form a structured dataset for subsequent sampling unit division and risk assessment.

[0012] Secondly, this application provides a detection system for pesticide residues in agricultural products, including a sampling unit, wherein the sampling unit specifically includes: The acquisition module is used to acquire basic information about the agricultural product area to be tested. The basic information includes the boundary information of the area, the internal planting structure difference information, and at least one key agricultural condition information that affects the distribution of pesticide residues. The processing module is used to divide the area of ​​the agricultural product to be tested into several primary sampling units of equal area based on the boundary information, and dynamically determine the number of sampling points in each primary sampling unit according to the key agricultural information, and use a random algorithm to generate the position coordinates of the corresponding number of sampling points to form a total set of sampling points. The processing module is also used to verify and adjust the total set of sampling points based on the internal planting structure difference information to ensure that the sampling point density in the sub-regions of different planting structures matches its area ratio. The processing module is also used to generate a movement path that allows the sampling device to sequentially traverse all sampling points based on the position coordinates of each sampling point in the total set of sampling points after verification and adjustment. The processing module is also used to control the sampling device to move along the moving path, and after arriving at the coordinate position of each sampling point, to perform a standardized sample collection operation to obtain the original sample; The execution module is used to perform standardized preprocessing on all collected raw samples to form a mixed sample, and to identify, package and record the mixed sample for laboratory analysis of pesticide residues.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described sampling method for pesticide residues in agricultural products.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-mentioned method for sampling pesticide residues in agricultural products.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In the process of pesticide residue sampling of agricultural products, this application firstly achieves scientific division of sampling units through assessment of agricultural homogeneity, avoiding heterogeneity interference caused by traditional uniform area division and ensuring the consistency of residue distribution within units. Secondly, it dynamically allocates the number of sampling points based on the degree of residue variability and adjusts the density with risk weights, prioritizing coverage of high-risk sub-regions, improving the targeting and representativeness of sampling, and solving the problem of resource waste. Thirdly, it adopts stratified grouping preprocessing of raw samples to avoid dilution or contamination caused by uniform mixing, ensuring the accuracy and traceability of laboratory testing. Finally, it improves sampling efficiency and reduces human error by combining path optimization algorithms and automated equipment control. Furthermore, this application integrates GIS and random algorithms to directly output stratified mixed samples and risk reports, thereby achieving efficient monitoring and risk location of pesticide residues and providing a reliable basis for food safety supervision and production optimization. Attached Figure Description

[0016] Figure 1 This is an exemplary flowchart of a method for sampling pesticide residues in agricultural products according to some embodiments of this application; Figure 2 This is a schematic diagram of the sampling unit shown in some embodiments of this application; Figure 3 This is a schematic diagram of the structure of a computer device for implementing a method for sampling pesticide residues in agricultural products, according to some embodiments of this application. Detailed Implementation

[0017] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific implementation methods. (Reference) Figure 1 The figure is an exemplary flowchart of a method for sampling pesticide residues in agricultural products according to some embodiments of this application. The method mainly includes the following steps: In step 101, basic information about the agricultural product area to be tested is obtained. The basic information includes the boundary information of the area, the internal planting structure difference information, and at least one key agricultural condition information that affects the distribution of pesticide residues.

[0018] The agricultural product area to be tested refers to farmland, orchards, or facility agriculture areas where pesticide residue monitoring will be conducted, such as a 10-acre vegetable planting base, which will not be elaborated further here. Basic information is the core input data of the sampling plan, used to guide subsequent unit division and risk assessment.

[0019] In some embodiments, obtaining basic information about the region of the agricultural product to be tested specifically includes: Obtain the boundary information and internal planting structure differences of the area through GIS system or on-site survey. For example, use drone aerial photography or satellite remote sensing data to draw the boundary coordinates (latitude and longitude range) of the area and mark the internal planting structure (e.g., leafy vegetable area accounts for 40%, root and tuber area accounts for 60%). Collect key agricultural information that affects pesticide residue distribution, including soil type, application history, crop growth stage and irrigation method. For example, record the number of applications (8 times per year in leafy vegetable areas and 3 times per year in root and tuber areas) and soil pH (20% higher residue adsorption rate in acidic soil areas) through farmland logs or IoT sensors. Integrate basic information to form a structured dataset, such as storing boundary polygon coordinates, planting structure vector layers, and agricultural attribute tables in JSON format, for subsequent sampling unit division and risk assessment.

[0020] It should be noted that the collection of basic information in this application emphasizes real-time performance and accuracy. For example, it integrates agricultural Internet of Things (IoT) data (such as real-time uploading of irrigation records by soil moisture sensors) to dynamically update agricultural information and ensure that the sampling plan adapts to seasonal changes. This will not be elaborated further here.

[0021] In step 102, based on the boundary information, the area of ​​agricultural products to be tested is divided into several primary sampling units. Within each primary sampling unit, the number of sampling points is dynamically determined according to the key agricultural information. A random algorithm is used to generate the location coordinates of the corresponding number of sampling points to form a total set of sampling points.

[0022] The primary sampling unit refers to a sub-region divided based on the homogeneity of agricultural conditions. For example, areas with consistent soil types and similar pesticide application histories are grouped into one unit to avoid heterogeneity interfering with the representativeness of the sampling.

[0023] In some embodiments, reference Figure 1 As shown, based on the boundary information, the area of ​​the agricultural product to be tested is divided into several primary sampling units. Within each primary sampling unit, the number of sampling points is dynamically determined according to the key agricultural information. A random algorithm is used to generate the location coordinates of the corresponding number of sampling points, forming a total set of sampling points. Specifically, this includes: Based on the key agricultural information, assess the homogeneity of agricultural conditions within the area of ​​the agricultural product to be tested, and divide the sub-regions with consistent agricultural conditions into primary sampling units. For example, use clustering algorithms (such as K-means) to analyze soil type and pesticide application history data, and cluster sub-regions with similar agricultural conditions into units, such as acidic soil high pesticide application areas as one unit. This is only an example and is not intended to limit the specific application of this application. Within each primary sampling unit, the residual variability is calculated based on the key agricultural information, and the number of sampling points is dynamically determined. Units with high residual variability are allocated more sampling points. For example, the coefficient of variation (CV) is calculated as standard deviation / mean. If CV > 0.3 (high variability, such as leafy vegetable area), 10 points are allocated; if CV < 0.1 (low variability, such as root and stem area), 3 points are allocated. A hierarchical random algorithm is used to generate the location coordinates of sampling points, ensuring that the sampling points meet the minimum spacing constraint and avoid non-planting areas, forming a total set of sampling points. For example, roads / ditches are excluded within the unit (GIS filtering), and a minimum spacing of 5m is set (for field crops). Then, a pseudo-random number generator (such as Python's random module) is used to evenly distribute the points within the effective grid. The total set is the union of all unit points.

[0024] It should be noted that the assessment of crop homogeneity in this application can adopt the fuzzy clustering method, considering the weight of multiple factors (such as the weight of pesticide application history of 0.4 and soil type of 0.3) to ensure that the unit division complies with the NY / T 789-2023 standard, which will not be elaborated here.

[0025] In step 103, the total set of sampling points is verified and adjusted based on the internal planting structure difference information to ensure that the sampling point density in the sub-regions of different planting structures matches their residual risk weights.

[0026] Among them, the information on differences in internal planting structure refers to the distribution of different crop types (such as leafy vegetables vs. root vegetables), and the residual risk weight is a priority indicator for quantifying high-risk areas.

[0027] In some embodiments, the total set of sampling points is verified and adjusted based on the internal planting structure difference information to ensure that the sampling point density in sub-regions of different planting structures matches their residual risk weights. Specifically, the following methods can be used: The residual risk weight of each sub-region is assessed based on the internal planting structure differences and key agricultural information. The weight of high-risk sub-regions is higher than that of low-risk sub-regions. For example, the risk weight W = application frequency × crop sensitivity coefficient × soil adsorption rate. For leafy vegetables, the high-risk area W = 0.8, and for root vegetables, the low-risk area W = 0.2. The total set of sampling points is verified, and the sampling point density is adjusted to match the residual risk weight. The sampling point density of high-risk sub-regions is increased accordingly. For example, if the area of ​​high-risk areas accounts for 20% and the initial sampling point accounts for 15%, then random sampling points are added to adjust it to 25%. Ensure that the adjusted sampling point distribution covers all planting structure sub-regions and conforms to risk-oriented principles, such as using Monte Carlo simulation to verify coverage >95% and avoiding point clustering.

[0028] In one example, the risk weight table for a vegetable base can be divided into sub-regions such as leafy vegetable area (risk weight 0.8, area ratio 40%, recommended density 2 points / acre) and root vegetable area (risk weight 0.2, area ratio 60%, recommended density 0.5 points / acre). This example is only for illustration, and the actual weight can be dynamically calculated based on historical residual data. No specific limitations are made here.

[0029] It should be noted that the risk weight assessment in this application can integrate machine learning models (such as random forests) and use historical pesticide residue datasets as input to predict mutation risk, which will not be elaborated here.

[0030] In step 104, based on the position coordinates of each sampling point in the total set of sampling points after verification and adjustment, a movement path is generated that allows the sampling device to sequentially traverse all sampling points.

[0031] The movement path refers to the optimized route of the sampling equipment (such as an automated picking robot) to ensure efficient coverage of all locations.

[0032] In some embodiments, the following method can be used to generate a movement path for the sampling device to sequentially traverse all sampling points based on the position coordinates of each sampling point in the total set of sample points after verification and adjustment: Extract the location coordinates from the total set of sampling points after verification and adjustment, and combine them with the terrain and accessibility information of the agricultural product area to be tested, such as importing DEM (Digital Elevation Model) data to identify inaccessible areas with a slope >15°; Path optimization algorithms are used to calculate the shortest or optimal movement path for the sampling devices, ensuring that the path avoids obstacles and minimizes movement time. For example, a TSP (Traveling Salesman Problem) solver (such as Google OR-Tools) is used to generate loop paths, minimizing the total distance by 20%. Output the generated movement path, including the order of each sampling point and the expected movement distance, for example, export the path file in KML format, label the point number and cumulative distance.

[0033] In step 105, the sampling device is controlled to move along the moving path, and after reaching the coordinate position of each sampling point, a standardized sample collection operation is performed to obtain the original sample.

[0034] Among them, sampling equipment refers to an automated platform that integrates GPS navigation and robotic arms, such as an unmanned sampling vehicle.

[0035] In some embodiments, the sampling device is controlled to move along the movement path, and after reaching the coordinate position of each sampling point, a standardized sample collection operation is performed to obtain the original sample. Specifically, this can be achieved in the following ways: Start the sampling equipment and automatically navigate to each sampling point along the generated movement path, for example, using an RTK-GPS positioning system with an accuracy of <10cm to correct deviations in real time; Upon arrival at the sampling point, appropriate sampling tools and depths are selected based on the key agricultural information, and standardized sampling operations are performed, including collecting samples of a fixed mass and recording environmental parameters. For example, in leafy vegetable areas, shallow cuttings (0-5cm, 50g / point) are used, and temperature / humidity is recorded (using IoT sensors). The collected raw samples are classified and stored, and labeled by sampling point or sub-region to obtain an independent set of raw samples. For example, each point is stored in an independent cold storage bag, and the label contains coordinates, time and agricultural information ID.

[0036] It should be noted that the standardized operations in this application comply with the GB / T 32198-2015 standard to ensure sample freshness (cold chain <2h after collection), which will not be elaborated here.

[0037] In step 106, all the collected original samples are preprocessed in a standardized manner to form a mixed sample, and the mixed sample is identified, packaged and recorded for laboratory analysis of pesticide residues.

[0038] Pretreatment refers to operations such as cleaning and mixing to form samples suitable for laboratory use.

[0039] In some embodiments, the normalization preprocessing of all collected original samples to form a mixed sample, and the identification, packaging and recording of the mixed sample specifically include: The collected raw samples were stratified and grouped according to the primary sampling unit or residual risk level to avoid indiscriminate mixing of all samples. For example, high-risk leafy vegetable units were grouped separately (10 points mixed 300g), and low-risk root and stem units were grouped together (3 points mixed 150g). Perform standardized pretreatment on each group, including washing, cutting and isomass mixing, to form a mixed sample of the corresponding group, ensuring that high residual samples are not diluted, for example, isomass sampling (50g per point), and uniform mixing to avoid contamination; Each pooled sample is uniquely identified, vacuum-sealed, and its grouping information, sampling point origin, and preprocessing details are recorded for laboratory analysis, such as QR code labels linking to database records (including risk weights and CVs).

[0040] In another aspect, in some embodiments, this application provides a system for detecting pesticide residues in agricultural products, including a sampling unit, with reference to... Figure 2 The figure is a schematic diagram of the structure of a sampling unit according to some embodiments of this application. The sampling unit 200 includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to acquire basic information about the agricultural product area to be tested. The basic information includes the boundary information of the area, the internal planting structure difference information, and at least one key agricultural information that affects the distribution of pesticide residues. Processing module 202, in this application, is mainly used to divide the area of ​​the agricultural product to be tested into several primary sampling units of equal area based on the boundary information, and dynamically determine the number of sampling points in each primary sampling unit according to the key agricultural information, and use a random algorithm to generate the position coordinates of the corresponding number of sampling points to form a total sampling point set. The processing module 202 described in this application is also used to verify and adjust the total set of sampling points according to the internal planting structure difference information, so as to ensure that the sampling point density in the sub-region of different planting structures matches its area ratio. In addition, in a specific implementation, the processing module 202 is also used to generate a movement path that allows the sampling device to sequentially traverse all sampling points based on the position coordinates of each sampling point in the total set of sampling points after verification and adjustment. In addition, in a specific implementation, the processing module 202 is also used to control the sampling device to move along the moving path, and after arriving at the coordinate position of each sampling point, to perform a standardized sample collection operation to obtain the original sample; The execution module 203 in this application is mainly used to perform standardized preprocessing on all collected original samples to form a mixed sample, and to identify, package and record the mixed sample for laboratory analysis of pesticide residues.

[0041] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described sampling method for pesticide residues in agricultural products.

[0042] In some embodiments, reference Figure 3The figure is a schematic diagram of the structure of a computer device for implementing a method for sampling pesticide residues in agricultural products according to some embodiments of this application. The methods in the above embodiments can be implemented through... Figure 3 The computer device shown is used to implement this, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0043] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more execution methods for controlling the sampling of pesticide residues in agricultural products in this application.

[0044] The communication bus 302 may include a path for transmitting information between the aforementioned components.

[0045] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0046] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the sampling point density adjustment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0047] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0048] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0049] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0050] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for sampling pesticide residues in agricultural products.

[0051] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0052] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for sampling pesticide residues in agricultural products, characterized in that, The method includes the following steps: Obtain basic information about the agricultural product area to be tested. The basic information includes the boundary information of the area, the internal planting structure difference information, and at least one key agricultural condition information that affects the distribution of pesticide residues. Based on the boundary information, the area of ​​agricultural products to be tested is divided into several primary sampling units of equal area. Within each primary sampling unit, the number of sampling points is dynamically determined according to the key agricultural information. A random algorithm is used to generate the location coordinates of the corresponding number of sampling points to form a total set of sampling points. The total set of sampling points is verified and adjusted based on the internal planting structure difference information to ensure that the sampling point density in the sub-regions of different planting structures matches their area proportion. Based on the position coordinates of each sampling point in the total set of sampling points after verification and adjustment, a movement path is generated that allows the sampling device to sequentially traverse all sampling points. The sampling device is controlled to move along the moving path, and after reaching the coordinate position of each sampling point, a standardized sample collection operation is performed to obtain the original sample; All collected raw samples were preprocessed in a standardized manner to form mixed samples, which were then labeled, packaged, and recorded for laboratory analysis of pesticide residues.

2. The method according to claim 1, characterized in that, Based on the boundary information, the area of ​​agricultural products to be tested is divided into several primary sampling units. Within each primary sampling unit, the number of sampling points is dynamically determined according to the key agricultural information. A random algorithm is used to generate the location coordinates of the corresponding number of sampling points, forming a total set of sampling points. Specifically, this includes: Based on the key agricultural information, assess the homogeneity of agricultural conditions within the area of ​​the agricultural product to be tested, and divide the sub-areas with consistent agricultural conditions into primary sampling units; Within each primary sampling unit, the residual variation degree is calculated based on the key agricultural information, and the number of sampling points is dynamically determined, with more sampling points allocated to units with a high residual variation degree. A hierarchical random algorithm is used to generate the location coordinates of sampling points, ensuring that the sampling points meet the minimum spacing constraint and avoid non-planting areas, thus forming a total set of sampling points.

3. The method according to claim 1, characterized in that, The total set of sampling points is verified and adjusted based on the internal planting structure difference information to ensure that the sampling point density in sub-regions of different planting structures matches their area proportion. Specifically, this includes: The residual risk weight of each sub-region is assessed based on the internal planting structure differences and key agricultural information, with the weight of high-risk sub-regions being higher than that of low-risk sub-regions. The total set of sampling points is verified, and the sampling point density is adjusted to match the residual risk weight, with the sampling point density of high-risk sub-regions being increased accordingly. Ensure that the adjusted sampling point distribution covers all planting structure sub-regions and complies with risk-oriented principles.

4. The method according to claim 1, characterized in that, Based on the position coordinates of each sampling point in the adjusted total sampling point set, the movement path generated to allow the sampling device to sequentially traverse all sampling points specifically includes: Extract the location coordinates from the total set of sampling points after verification and adjustment, and combine them with the terrain and accessibility information of the agricultural product area to be tested; The path optimization algorithm is used to calculate the shortest or optimal movement path of the sampling device, ensuring that the path avoids obstacles and minimizes the movement time. The output shows the generated movement path, including the order of each sampling point and the expected movement distance.

5. The method according to claim 1, characterized in that, Controlling the sampling device to move along the movement path, and performing standardized sample collection operations upon reaching the coordinates of each sampling point to obtain the original sample specifically includes: Start the sampling equipment and automatically navigate to each sampling point along the generated movement path; Upon arrival at the sampling point, appropriate sampling tools and depths are selected based on the key agricultural information, and standardized sampling operations are performed, including collecting samples of a fixed mass and recording environmental parameters. The collected raw samples are classified and stored, and labeled by sampling point or sub-region to obtain an independent set of raw samples.

6. The method according to claim 1, characterized in that, All collected raw samples undergo standardized preprocessing to form a mixed sample, and the mixed sample is then identified, packaged, and recorded. Specifically, this includes: The collected raw samples were stratified and grouped according to primary sampling units or residual risk levels to avoid indiscriminate mixing of all samples; Standardized preprocessing is performed on each group, including washing, cutting and equal-mass mixing, to form a mixed sample of the corresponding group, ensuring that high-residue samples are not diluted; Each pooled sample was uniquely identified, vacuum-sealed, and its grouping information, sampling point origin, and pretreatment details were recorded for laboratory analysis.

7. The method according to claim 1, characterized in that, Obtain basic information about the agricultural product area to be tested. This basic information includes the area's boundary information, internal planting structure differences, and at least one key agricultural condition information affecting pesticide residue distribution. Specifically, this includes: Obtain the boundary information and internal planting structure differences of the area through a GIS system or on-site survey; Collect key agricultural information that influences pesticide residue distribution, including soil type, application history, crop growth stage, and irrigation method; Integrate basic information to form a structured dataset for subsequent sampling unit division and risk assessment.

8. A system for detecting pesticide residues in agricultural products, comprising a sampling unit, characterized in that, The sampling unit specifically includes: The acquisition module is used to acquire basic information about the agricultural product area to be tested. The basic information includes the boundary information of the area, the internal planting structure difference information, and at least one key agricultural condition information that affects the distribution of pesticide residues. The processing module is used to divide the area of ​​the agricultural product to be tested into several primary sampling units of equal area based on the boundary information, and dynamically determine the number of sampling points in each primary sampling unit according to the key agricultural information, and use a random algorithm to generate the position coordinates of the corresponding number of sampling points to form a total set of sampling points. The processing module is also used to verify and adjust the total set of sampling points based on the internal planting structure difference information to ensure that the sampling point density in the sub-regions of different planting structures matches its area ratio. The processing module is also used to generate a movement path that allows the sampling device to sequentially traverse all sampling points based on the position coordinates of each sampling point in the total set of sampling points after verification and adjustment. The processing module is also used to control the sampling device to move along the moving path, and after arriving at the coordinate position of each sampling point, to perform a standardized sample collection operation to obtain the original sample; The execution module is used to perform standardized preprocessing on all collected raw samples to form a mixed sample, and to identify, package and record the mixed sample for laboratory analysis of pesticide residues.

9. A computer device comprising a memory and a processor, the memory storing code, characterized in that, The processor is configured to acquire the code and execute the sampling method for pesticide residues in agricultural products according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the sampling method for pesticide residues in agricultural products as described in any one of claims 1 to 7.

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