1974 results about "Insecticide residue" patented technology

The food material cleaning tank comprises an outer tank and a draining tank detachably arranged in the outer tank, the bottom of the draining tank is provided with an aeration channel capable of being communicated with an external air source, and the inner bottom wall of the draining tank is provided with aeration holes which are communicated with the aeration channel and used for blowing air into the draining tank. An air guide pipe with an upward air outlet end is arranged below the draining groove, and an assembly opening for the upper end of the air guide pipe to be detachably inserted into so as to be communicated with the aeration channel is formed in the outer bottom wall of the draining groove. The aeration holes in the inner bottom wall of the draining tank directly blow air into the draining tank, and the blown air cannot be blocked by the bottom wall of the draining tank, so that boiling turbulent flow is formed in the draining tank, pesticide residues and dirt on the surface of an object to be cleaned are powerfully stripped, and the cleaning effect is improved; the aeration channel at the bottom of the draining tank is detachably connected with the air guide pipe at the bottom of the outer tank, so that whether the air channel is communicated or not can be selected according to needs.

The invention relates to bacillus velezensis SMB1 and application thereof in prevention and treatment of plant diseases, and belongs to the technical field of biological prevention and treatment of plant diseases. The strain is preserved in the China Center for Type Culture Collection on November 27, 2024, the preservation address is Luojia mountain, Luojia mountain, eight road, Wuchang District, Wuhan City, Hubei Province, and the preservation number is CCTCC NO: M20242647. The bacillus velezensis SMB1 provided by the invention has a remarkable bacteriostatic effect on plant pathogenic fungi, plant pathogenic oomycetes, plant pathogenic bacteria and animal pathogenic bacteria, has an extremely good broad-spectrum bacteriostatic function, has a great application potential in biological control of plant diseases, can reduce the use of chemical pesticides, and has a good application prospect. The pesticide residue in agricultural products is reduced, the environment is protected, and the urgent demand on pollution-free agricultural product production at present is met.

The invention discloses a campus smart canteen management platform based on AI and IOT. The campus intelligent canteen management platform comprises a global sensing system, an intelligent decision support system, a credible tracing system, a safety and supervision system and a user experience optimization system. The global sensing system is used for collecting multi-modal data and carrying out data fusion and preprocessing on the multi-modal data; the intelligent decision support system is used for performing multifunctional analysis on the fused and preprocessed multi-modal data to obtain a management decision; the credible traceability system is used for recording digital traceability information, storing reserved food samples and detecting pesticide residues in real time; the safety and supervision system is used for providing food reserved sample management, food quality event monitoring and analysis, supervision record management and parent applet functions; and the user experience optimization system is used for providing health inspection, health monitoring and personalized suggestions, identity verification and payment modes. In this way, the overall level of canteen management is improved.

The invention relates to a full-automatic QuECHERS experimental device, and relates to the technical field of sample pretreatment, the full-automatic QuECHERS experimental device comprises an extraction unit and a pipetting unit, the extraction unit comprises: a first tube rack module comprising a plurality of test tube racks; a carrying mechanical arm; the first cap screwing module comprises a tube cap clamping and lifting assembly acting on a tube cap, a tube body clamping and rotating assembly acting on a tube body and a first sliding rail assembly for conveying a test tube to a next station; the sample adding module comprises a homogeneous proton adding module and a salt pouring module; the second cap screwing module comprises a pipe body clamping and rotating assembly, a pipe cap clamping and lifting assembly and a second sliding rail assembly for realizing communication and conveying of the extraction unit and the pipetting unit; the invention discloses a pipetting mechanical arm and a gun head frame. The device is provided with the extraction unit capable of synchronously performing extraction operation and the pipetting unit capable of performing pipetting operation, so that the extraction operation and the pipetting operation are synchronously performed during pesticide residue inspection, and the experimental efficiency and the applicability of the device are effectively improved.

The invention provides a pesticide residue detection method and device combined with mass spectrometry, and relates to the technical field of pesticide residue detection.The method comprises the steps that a sample image is recognized, and a sample type and sample morphology characteristics are determined; matching and determining a pretreatment process and a sample interference matrix; detecting according to a pretreatment process; inputting the sample type, the sample morphology characteristics and the detection data set into a pesticide component analysis channel for identification, and outputting a pesticide residue component set; and performing interference correction on the plurality of pesticide concentrations according to the experimental equipment attribute characteristics, the interference matrix type set and the matrix content set, outputting a plurality of corrected pesticide concentrations, and generating a pesticide residue detection report. The technical problem that the accuracy and reliability of pesticide residue detection are poor due to the fact that matrix interference of different types of products cannot be handled in a traditional method can be solved, the interference problem of pesticide residue detection under a complex matrix can be effectively solved, and the accuracy and reliability of pesticide residue detection are remarkably improved.

The invention relates to the technical field of spectral analysis, in particular to an intelligent spectral pesticide residue detection system which comprises a spectral data acquisition module, a spectral feature analysis module, a pesticide spectral feature classification module, a spectral matching weight optimization module and a pesticide residue concentration analysis module. In the invention, by collecting spectral signals of a pesticide sample in different wavelength ranges and combining time, space and frequency dimension information, the comprehensiveness of data collection is ensured, spectral data is analyzed in multiple dimensions, mutation features are extracted, associated frequency bands are identified, angle deviation is eliminated, and the accuracy and reliability of spectral features are improved; classification is performed according to spectral distribution differences and change rates, detection resolution is improved, spectrum matching rules are optimized, high-contribution wavebands are screened, detection accuracy is improved, concentration normalization analysis is combined, key feature contribution values are adjusted, misjudgment risks are reduced, optical paths are optimized, optical loss is reduced, signal acquisition stability is improved, and adaptability of a detection system is enhanced.

The invention discloses a traditional Chinese medicine pesticide residue detection method and system based on artificial intelligence and a medium, particularly relates to the technical field of image processing and intelligent detection, and is used for solving the problem that the capability of distinguishing complex surface textures and pesticide residue areas of traditional Chinese medicines is insufficient. The method comprises the following steps: extracting local gradient direction distribution features of a high-resolution image, and combining gradient magnitude clustering processing to generate a gradient feature map of a texture edge contour; carrying out multi-scale fusion on the gradient features and the color channel data, and constructing a multi-channel feature map representing textures, colors and spatial distribution; analyzing quantitative feature relevance based on covariance of a high-order color moment and a directional entropy, and dividing normal textures and abnormal residual regions through a mahalanobis distance classifier; and performing spectral band matching degree evaluation on the abnormal region, fusing geometric morphological characteristics and spectral scores, inputting the fused geometric morphological characteristics and spectral scores into a pre-trained classification model, and generating a pesticide residue detection result by using a nonlinear correlation decision, thereby realizing accurate distinguishing between the natural texture on the surface of the traditional Chinese medicinal material and the pesticide residue.

The invention discloses a copper-based nano-enzyme, a preparation method thereof and application of the copper-based nano-enzyme in organophosphorus pesticide detection, and belongs to the technical field of pesticide residue detection. The copper-based nano-enzyme has a netted-like structure, a high specific surface area and many active sites, the valence state of a copper element is zero-valence Cu, Cu < + > and Cu < 2 + >, the copper-based nano-enzyme has high peroxidase-like activity and laccase-like activity, the detection limit is low, and glyphosate can be sensitively detected; and glyphosate, glufosinate-ammonium and isazofos can be well distinguished from six organophosphorus pesticides with medium concentration, and the method has high selectivity. The nano enzyme provided by the invention is prepared from ammonium citrate and copper nitrate in an N, N-dimethylformamide (DMF) solution by using a solvothermal method; the preparation process of the nano enzyme is simple.

The invention provides a red ginseng processing method capable of increasing the content of rare ginsenoside and reducing pesticide residues, and relates to the technical field of medicinal material processing, the red ginseng processing method comprises the following steps: A, steaming fresh ginseng by using atomized probiotic fermentation liquor; b, drying the steamed fresh ginseng to obtain red ginseng; the pH (Potential of Hydrogen) of the probiotic fermentation liquor is 3.0 to 4.0; the OD600 value of the probiotic fermentation liquor is 1.2-2.5, the probiotic fermentation liquor is obtained through probiotic fermentation, the fermentation liquor with the pH value being 3.0-4.0 is adopted as an acid catalyst to replace purified water, in the ginseng steaming process, steaming is conducted through the atomized probiotic fermentation liquor, conversion of ginsenoside is promoted, and meanwhile organophosphorus pesticide residues in ginseng are degraded. The method is safe, efficient, green and natural, and loss of nutritional ingredients and functional ingredients caused by cooking is avoided. The technical problems that red ginseng processed and prepared in the prior art is low in rare saponin content and poor in effect and pesticide residues exist are solved.

The invention provides a flexible HP-ZIF-67 (at) Au surface enhanced Raman scattering substrate for multi-phase detection of volatile organic gas and pesticide residues, and a preparation method and application thereof, and belongs to the technical field of volatile organic gas and pesticide residue detection. The preparation method comprises the following steps: preparing a polystyrene microsphere template by taking polyvinylpyrrolidone, styrene and potassium persulfate as raw materials, then adding 2-methylimidazole and cobalt nitrate to obtain HP-ZIF-67 particles, dispersing the HP-ZIF-67 particles on an aluminum foil to obtain an HP-ZIF-67 substrate, and continuing to diffuse and deposit a gold nanoparticle layer to obtain the flexible HP-ZIF-67 (at) Au surface enhanced Raman scattering substrate. The flexible HP-ZIF-67 (at) Au surface enhanced Raman scattering substrate provided by the invention has the advantages of stable process, high sensitivity and multiphase detection, and a novel composite material is provided for volatile organic gas and pesticide residue detection.

The invention discloses a pesticide residue prediction method based on an LSTM variant, and relates to the technical field of pesticide residue detection and prediction.The method comprises the steps that time sequence data such as temperature, humidity, soil pH value, rainfall and pesticide application concentration are collected through a sensor, and normalization, missing value filling and sliding window slicing treatment are conducted; an optimized LSTM variant model is adopted for modeling, and an output activation function is removed to enhance the capacity of capturing nonlinear changes of pesticide residues. The model is combined with a dynamic threshold adjustment mechanism, adapts to characteristics and residue standards of different pesticides, and can be deployed on edge equipment in real time to realize real-time alarm when residues exceed the standard. Compared with the prior art, the method has the remarkable advantages in the aspects of prediction precision, response speed and adaptability, and is suitable for pesticide residue monitoring in various agricultural scenes such as rice fields and orchards.

The invention belongs to the technical field of pesticide residue detection, and particularly relates to a DebNet intelligent classification algorithm based on fluorescence method pesticide residue detection, which comprises the following steps: detecting a pesticide sample by using a fluorescence spectrometer to obtain one-dimensional spectral data of the pesticide sample; preprocessing the spectral data; performing data enhancement on the preprocessed spectral data, wherein an enhancement mode comprises linear interpolation, noise injection, spectrum translation and spectrum cutting; building a hybrid neural network model comprising an input layer, a convolutional layer, a pooling layer, an LSTM layer, a self-attention module, a full connection layer and an output layer, namely a DebNet model; training a DebNet model based on the enhanced spectral data, wherein the training process comprises forward propagation and back propagation; and utilizing the trained DebNet model to classify and identify the spectral data of a to-be-detected sample. The method can improve the classification accuracy, reduce the sample requirements, simplify the data processing flow, and reduce the detection cost.

The invention discloses a fresh-keeping production device and method for prefabricated vegetables, and relates to the technical field of prefabricated vegetable processing.The fresh-keeping production device comprises a main body, a conveying mechanism is arranged at the bottom end in the main body, a washing mechanism is arranged at the position, above the conveying mechanism, in the main body, and the conveying mechanism comprises a conveying belt and two sets of conveying rollers; the conveying belt is wound around the outer sides of the two sets of conveying rollers, and drainage holes are formed in the surface of the conveying belt. Through the cooperation of reverse operation and reciprocating brushing of the brushing mechanism, pressurized water flow of the flushing mechanism and the bubble effect of the bubble generation mechanism, vegetables are cleaned in multiple directions, various dirt and pesticide residues are effectively removed, the cleaning effect is remarkably improved, it is guaranteed that raw materials of the prefabricated vegetables are clean, preservation is facilitated, and the transmission mechanism reasonably distributes and converts power; and the nozzle of the flushing mechanism performs multi-angle flushing, and water recycling is realized through a backflow box and a filter plate, so that water is saved, secondary pollution of sludge is avoided, the cost is reduced, and the device is environment-friendly.

The invention, which relates to the technical field of agricultural intelligent equipment, discloses an agricultural plant protection unmanned aerial vehicle sowing control system comprising a sowing flow field dynamic modeling and prediction module, an attitude regulation and control module, a speed matching module, a path compensation module, a visual monitoring module and a control strategy module. A flow field dynamic mapping model based on the flight height, the downward pressing airflow speed and the particle size and density of the scattered particles is constructed, particle drop point deviation is predicted in combination with flight real-time data, and a pneumatic coupling risk area is identified. Through flow field modeling prediction and attitude adjustment, trajectory compensation, speed control and visual feedback, adaptive regulation and control in the sowing process are realized, air scattering and nonlinear collapse are effectively prevented, seedling burning, germination failure and pesticide residue risks caused by over-dense sowing are avoided, the operation uniformity and the agricultural effect are improved, and the method has practical value and popularization prospects.

The invention discloses design and application of a colorimetric-catalytic signal enhancement combined graphene oxide nano-enzyme rapid pesticide residue detection platform, and belongs to the technical field of immunochromatography detection. Graphene (GO) is used as a substrate, 30 nm Pt nanoparticles are adsorbed on the surface of the substrate to provide a colorimetric signal, and gaps of Pt NPs are filled with 5 nm AuIr nanoparticles to enhance the catalytic performance of the substrate, so that the colorimetric / catalytic dual-signal enhanced nano-enzyme is prepared. The GO (at) Pt30-AuIr nano-enzyme is synergistically assembled with Pt NPs and AuIr NPs by utilizing the large specific surface area, excellent stability and dispersibility of GO, so that the catalytic performance of the system is remarkably improved. The method has the beneficial effects that a GO-Pt30-AuIr-LFA-based detection system can realize high-sensitivity dual detection on heavy metal cadmium ions (Cd < 2 + >) and pesticide residue imidacloprid (IMI) within 20 minutes, and the detection limit reaches the level of pg / mL. The detection method provided by the research shows significant application potential in the aspect of rapid screening of small molecules in a complex matrix, and a new technical approach is provided for food safety monitoring.

The invention relates to the technical field of logistics monitoring, in particular to a logistics service digital management system which comprises an environment sensor, an electronic nose and a visual sensor which are all arranged in a refrigeration logistics transportation device for transporting fruits and vegetables. The acquisition module is used for acquiring environment data of refrigerated fruits and vegetables at an environment acquisition frequency, acquiring smell data of the fruits and vegetables at an electronic nose acquisition frequency and acquiring surface images of the fruits and vegetables at a visual acquisition frequency; the state judgment module is used for identifying internal and external rotting states, pesticide residue states and appearance abnormal states of the fruits and vegetables; the frequency adjusting module is used for adjusting the environment acquisition frequency, the electronic nose acquisition frequency and the visual acquisition frequency; and the block chain storage module is used for storing the environment data, the smell data and the surface image which are acquired for multiple times in an account book of a block chain. According to the invention, the collection frequency of the sensor and the data volume stored in the account book of the block chain are controlled through the judgment results of various fruit and vegetable states, and the high processing speed of the block chain is realized.

The invention relates to the technical field of food detection, and discloses a food pesticide residue safety detection analysis method based on machine learning, which comprises the steps of food sample collection, extract pretreatment, two-dimensional liquid chromatography-high resolution mass spectrometry combined technology analysis, mass spectrum acquisition, mass spectrum numerical data conversion, data correction and machine learning model establishment. The method comprises the following steps: identifying and classifying pesticide residue components, extracting a pretreatment extract, analyzing a to-be-detected sample, acquiring a mass spectrum, converting the mass spectrum into to-be-detected mass spectrum numerical data, correcting the data, inputting a model, identifying and classifying pesticide residues, quantitatively analyzing, comparing food safety standards, judging conformity and generating a detection report. The invention aims to improve the accuracy, sensitivity and efficiency of pesticide residue detection. Meanwhile, through technical correction and optimization, the problems of environmental interference, data deviation and the like faced by an existing method are effectively solved.

The invention discloses an intelligent identification and management system and method for key commodities of a vegetable basket, and relates to the technical field of computer processing, and the system comprises a multi-modal data collection and preprocessing module which is used for collecting commodity images, temperature and humidity curves, vibration frequencies and electronic quality inspection reports through Internet of Things equipment, the block chain technology is adopted to encrypt and store production batches and detection values; and the intelligent labeling and label generation module is used for performing instance segmentation on the commodity image, analyzing semantic information of the quality inspection report and generating a multi-level quality label. According to the invention, through a multi-modal data fusion and dynamic association analysis technology, image recognition, sensor data and text analysis are integrated, an intelligent supervision system covering a production-retail full link is constructed, and on the basis of an adversarial training mechanism and an edge cloud collaborative architecture, the production-retail full link is completely supervised. The real-time identification capability of complicated illegal behaviors such as water-injected meat, pesticide residue exceeding and label tampering is obviously improved, and the whole-process safety guarantee of livelihood commodities is enhanced.

The invention discloses a soil pesticide residue degradation treatment device, and relates to the field of soil treatment, the soil pesticide residue degradation treatment device comprises: a mobile frame, the mobile frame is provided with a placing area and an accommodating cavity; the lifting mechanism comprises a lifting seat and an electric cylinder connected with the lifting seat; the multiple soil loosening and pesticide spraying mechanisms are rotationally installed at the bottom of the lifting base side by side, each soil loosening and pesticide spraying mechanism comprises a rotating disc, a pesticide spraying pipe is fixedly embedded in the middle of each rotating disc, a rotating connector is connected to the top end of each pesticide spraying pipe, and four plate bases surround the pesticide spraying pipes; a plurality of soil loosening assemblies are installed on the side, away from the pesticide spraying pipe, of the plate base from top to bottom at equal intervals. Hardened soil is subjected to crushing and loosening treatment through the multiple soil loosening and pesticide spraying mechanisms, meanwhile, degradation pesticide liquid is sprayed through the multiple soil loosening and pesticide spraying mechanisms, the degradation pesticide liquid can effectively make contact with the loose soil, permeation of the degradation pesticide liquid is facilitated, and the degradation treatment effect is improved.

The invention relates to the technical field of agricultural biomass pesticides, in particular to a preparation method of a lignin-based slow-release nano pesticide. According to the method, the cationic surface active agent-corn straw lignin nano pesticide with pH and enzyme double responsiveness is prepared through self-assembly. According to the material, cetyltrimethylammonium bromide (CTAB) is adopted as a cationic surface active agent to be prepared into modified lignin with corn straw, and the cationic surface active agent-corn straw lignin nano pesticide is further formed through a self-assembly method. The preparation method has the advantages of being simple in process, low in cost, environmentally friendly and the like, the obtained slow-release nano pesticide product is excellent in ultraviolet shielding performance, slow-release performance and insecticidal activity, pesticide can be accurately conveyed in a target spot mode, the possibility of pesticide residues and environmental pollution is remarkably reduced, and sustainable development of agriculture is promoted.

The invention belongs to the technical field of image processing, and particularly relates to an intelligent agricultural product pesticide residue detection method and system based on a spectrum technology, and the method comprises the steps: obtaining the hyperspectral reflectivity according to the original hyperspectrum, dark current and whiteboard image data; calculating an illumination confidence factor by using the average spectral intensity; performing linear scaling on the obtained standard biological matrix background spectrum according to the illumination confidence factor to obtain a local background estimated value and obtain a net residual spectrum; generating a pesticide residue distribution thermodynamic diagram by combining the cosine similarity of the net residual spectrum and the target pesticide standard fingerprint spectrum and the noise suppression weight based on the illumination confidence factor; and pesticide residue detection is carried out according to the statistical characteristics of the connected region of the suspected defect mask. Through adaptive background deduction and noise suppression, the problems of uneven curved surface illumination and strong background interference are solved, and the detection accuracy is improved.

The invention provides a plasmodiophora brassicae effector Pb78, and the nucleotide sequence of the plasmodiophora brassicae effector Pb78 is shown The invention also provides an application of host-induced silencing of the plasmodiophora brassicae effector Pb78 in prevention and treatment of plasmodiophora brassicae infection, and also provides a plasmodiophora brassicae prevention and treatment liquid and a preparation method of the plasmodiophora brassicae prevention and treatment liquid. The invention verifies that host-induced plasmodiophora brassicae effector Pb78 silencing can effectively inhibit plasmodiophora brassicae infection and symptom formation of cruciferae plants. The preparation and the treatment method disclosed by the invention are suitable for various cruciferae crops and vegetables which can be infected by plasmodiophora brassicae, and are wider in applicability; the inhibition efficiency on clubroot is higher than that of medicament treatment, and the compound has the advantage of no pesticide residue and is high in safety; due to the adoption of a seed treatment mode, the method is particularly suitable for preventing and treating clubroot susceptible seedlings.

The invention relates to the technical field of pesticide residue detection, and discloses an all-in-one fluorescent quantitative test strip management system and method based on a block chain, and the method comprises the steps: obtaining pesticide residue detection data of an all-in-one fluorescent quantitative test strip, carrying out the chaining storage of the detection data, and constructing a pesticide residue toxicity knowledge graph; comparing the detection data with the knowledge graph, and determining the original risk level of the pesticide; in combination with the region and time information, other detection data of the same region and the same time period in the block chain are obtained, clustering analysis is executed, and a pesticide risk reference of the region is determined; dynamically calibrating the original risk level based on the risk reference to generate a calibrated risk level; setting an intelligent contract on the block chain, and automatically triggering a corresponding execution strategy according to the calibrated risk level; the pesticide residue risk can be objectively and dynamically evaluated, intelligent management of pesticide residues is achieved, and the food safety guarantee level of agricultural products is greatly improved.

A pesticide residue extraction solution adapted for a rapid extraction method of stock solution of agricultural products and a pesticide residue detection method in agricultural products. The pesticide residue extraction solution includes acidic solution and acetone solution. The rapid extraction method includes homogenizing the agricultural product sample, to obtain a homogenized sample; mixing the pesticide residue extraction solution with the homogenized sample to form a sample solution; and subsequently using a column filled with mixed powder to filter out the water and impurities from the sample solution, thereby obtaining a stock solution of the agricultural product. The method for detecting agricultural product pesticide residues includes using the rapid extraction method to obtain the stock solution of agricultural product, and to directly input the stock solution of the agricultural product into a liquid chromatography tandem mass spectrometer or a gas chromatography tandem mass spectrometer for pesticide residue detection.

The invention relates to the technical field of agricultural product detection, in particular to an agricultural product pesticide residue detection method and system. According to the method, the spatial orientation characteristics of molecular three-dimensional conformation parameters are introduced, a symmetry index set is established, matching calculation is carried out in combination with temperature distribution in chromatographic column operation parameters and the sample injection direction, accurate linkage of structural information and a chromatographic path is achieved, and through coupling analysis of response starting time and chromatographic regulation and control parameters, the accuracy of the chromatographic regulation and control parameters is improved. A detection time window taking a target protein signal as a reference is set, a response enhancement fragment with a significant rising rate is screened out in combination with a signal change trend in the window, and a target signal interval is obtained through weighted fitting, so that the specificity and pertinence of detection data are effectively improved, and the detection accuracy is improved through comparison of peak position time and response data. According to the scheme, accurate labeling of pesticide types in a complex sample is achieved, and the method has high stability and judgment accuracy under the scene that the sample heterogeneity is high and signal interference is complex.

The invention discloses a pesticide residue scavenging agent. The feed additive is prepared from the following components in parts by weight: 90-110 parts of white hyacinth beans, 9-11 parts of papain, 90-110 parts of liquorice, 45-55 parts of folium isatidis, 25-35 parts of perilla leaves, 25-35 parts of fructus forsythiae, 25-35 parts of honeysuckle, 15-25 parts of rhizoma smilacis glabrae, 15-25 parts of ginkgo leaves, 4-6 parts of soybean protease, 9-11 parts of glutathione, 5-7 parts of amino-oligosaccharin, 9-11 parts of amylase and 7-9 parts of walnut peptide. The invention also discloses a preparation method of the pesticide residue scavenger. The method has the following outstanding effects: the method is easy to operate, low in cost, popular in price and remarkable in effect, and can reach the national safety agricultural product indexes.

The invention relates to the technical field of agriculture, in particular to a Raman spectrum and deep learning-based pesticide residue detection method and electronic equipment. The method comprises the following steps: acquiring Raman spectrum data of a sample to be detected; inputting the Raman spectrum data into a pre-trained qualitative analysis model, and outputting the category probability of the pesticide components in the to-be-detected sample; inputting the Raman spectrum data into a pre-trained quantitative analysis model, and outputting the content proportion of the pesticide components in the to-be-detected sample; wherein the qualitative analysis model realizes conjoint analysis of spectral global features and local features based on a multi-modal fusion deep learning network; the quantitative analysis model realizes spectral decomposition and concentration regression based on a deep learning architecture of multi-scale feature fusion. The detection defects of an enzyme inhibition method and a chromatography method are overcome, the problem that the potential mode of the Raman spectrum cannot be accurately and stably found through machine learning is solved, and higher detection precision and higher detection speed are achieved.

The invention relates to a soil pesticide residue detection method, system, equipment and medium, and relates to the technical field of intelligent detection.The method comprises the following steps that a to-be-detected soil sample is subjected to ultrasonic-assisted extraction, and a pesticide extracting solution is obtained; separating and detecting the extracting solution by using a liquid chromatograph to obtain a pesticide chromatographic peak, and performing integral calculation on concentration data; calculating the adsorption capacity based on the concentration data, generating a pesticide distribution diagram, and obtaining a pollution range diagram through spatial interpolation; determining a pesticide degradation rate according to the pollution range map, obtaining half-life period data, and further calculating an exposure dose; residual toxicity effect analysis is carried out based on the exposure dose, a toxicity effect score is obtained, a corresponding soil remediation scheme is formulated according to the score, and the technical problem that pollution risk assessment is lagged due to the lack of pesticide adsorption behavior in soil in the traditional technology is solved.

The invention relates to the field of astragalus mongholicus planting, in particular to an astragalus mongholicus rhizosphere flora ecological regulation and control disease prevention system based on nitrogen-phosphorus-potassium combined application, which comprises a nitrogen-phosphorus-potassium combined application module, a rhizosphere flora monitoring module, a flora regulation and control module, a pest and disease damage early warning module and a prevention and control measure module, the nitrogen-phosphorus-potassium combined application module comprises a fertility detection unit, a growth stage evaluation unit, a fertilization scheme making unit and an intelligent fertilization equipment control unit; the fertility detection unit comprises a soil nutrient rapid tester, through mutual cooperation of all the modules, the quality of astragalus membranaceus is improved, use of chemical pesticides is reduced, pesticide residues are reduced, the safety and quality of medicinal materials are guaranteed, ecological balance is enhanced, growth of rhizosphere effective microbial communities is promoted, stability and health of a soil ecological system are maintained, and the prevention and treatment cost is reduced; through precise fertilization and early warning, unnecessary control investment is reduced, sustainable development is achieved, green and sustainable development of astragalus membranaceus planting is achieved, and the ecological environment is protected.

The invention relates to the technical field of pesticides, and discloses a weeding composition for preventing and controlling alternanthera philoxeroides in a paddy field as well as a preparation method and application of the weeding composition. The weeding composition is prepared from three main effective components and an auxiliary agent, and the mass percent of each component is as follows: 6.00 to 8.00 percent of fluroxypyr, 14.00 to 18.00 percent of prometryn, 40.00 to 60.00 percent of vanillic acid, 5.00 to 13.00 percent of the auxiliary agent and the balance of deionized water. The weeding composition is applied in a stem and leaf spraying mode, that is, 30-45 days after rice transplanting, 100 g of the weeding composition is mixed with 30 Kg of water per mu to be evenly sprayed to the surfaces of plants at a time, the plant control effect and the biomass control effect on alternanthera philoxeroides in a rice field are remarkably better than those of three single dosages of fluroxypyr, prometryn and vanillic acid 30 days and 60 days after the weeding composition is applied, and the weeding composition is safe to rice and free of pesticide residues. The yield of the rice is increased by more than 10.00%, and the composition has the advantages of safety, high efficiency and long lasting period.