1382 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 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, 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 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 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 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 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 discloses an agricultural product pesticide residue detection and analysis system and method based on big data, and relates to the field of hyperspectral analysis, and the method comprises the steps: obtaining hyperspectral data, environmental data and pesticide application parameters of the surface of a target agricultural product, space pixel coordinates in the hyperspectral data serve as sampling points, space-time alignment is conducted on the sampling points, the environment data and the pesticide application parameters, and a space-time correlated spectral feature matrix is generated; calculating the spectral gradient and noise disturbance of the quantum optimal feature set through a spectral gradient back propagation method, and generating an antagonism enhanced sample and target domain feature distribution; and associating the pesticide residue detection value and the standard exceeding early warning signal with the geographic coordinates to generate a pesticide residue distribution map and a traceability analysis result. According to the method, high-sensitivity detection of the pesticide residue concentration is realized through quantum feature optimization, adversarial domain adaptation training and space-time dynamic fusion technologies, the traceability matching accuracy is improved, and an accurate and efficient decision support tool is provided for agricultural safety supervision.

The invention provides a device for detecting pesticide residues in food, and relates to the technical field of food detection equipment, the device comprises a loading box, a sample crushing cylinder, an inner cylinder, a crushing mechanism, a freezing box, a first sampling bottle, a second sampling bottle and a rotating mechanism; the top of the loading box is covered with a cover body, and the sample crushing cylinder is fixedly connected to the inner side wall of the loading box; the inner cylinder body is arranged in the sample crushing cylinder, crushing mechanisms are arranged inside and outside the inner cylinder body, the sample crushing cylinder is used for sampling food skin, and the inner cylinder body is used for sampling the middle part in food; the freezing box is arranged in the loading box, is positioned on the lower side of the sample crushing cylinder, and is used for freezing a sample to be rechecked; the rotating mechanism drives the sample and the extraction solvent to be fully fused, so that the pesticide residue extraction is more sufficient, the accuracy of the detection result is improved, and the accurate separation and extraction mode is beneficial to more comprehensive and accurate evaluation of the pesticide residue condition of the food.

The invention relates to the technical field of agricultural planting, in particular to a method for preventing and treating watermelon fusarium wilt by using a green bactericide, which comprises the following steps: preparing the green bactericide which comprises the following components in percentage by mass: 18-25% of composite plant source active component consisting of water lettuce extract, enteromorpha polysaccharide and field horsetail flavone; 20%-30% of an antagonistic bacterium compound preparation, wherein the antagonistic bacterium compound preparation comprises bacillus pumilus and trichoderma; 38%-45% of a nano-carrier, wherein a chitosan-sodium alginate compound is used as an embedding material; and the balance of auxiliary components. Applying the green bactericide to watermelon plants; through component optimization and a synergistic effect, compared with the prior art, the composition has the remarkable advantages that the safety is high, all the components are from natural plants or microorganisms, no chemical pesticide residue exists, the composition is healthy to human bodies and environmentally friendly, and the problems of drug resistance and soil pollution caused by chemical agents can be avoided.

The invention relates to a pesticide residue detection test strip as well as a preparation method and application thereof, in particular to a rapid detection technology based on an enzyme inhibition method and a chitosan non-woven fabric carrier. The test strip can specifically detect organophosphorus and carbamate pesticides such as trichlorfon, malathion and phoxim through the enzyme activity inhibition principle, the detection limit of the pesticides such as phoxim is as low as 0.1 mg / kg, the developing intensity is in negative correlation with the pesticide concentration, and semi-quantitative judgment can be achieved. The preparation process is simple, the chitosan non-woven fabric is adopted as a fixed carrier, the material is environment-friendly and degradable, the enzyme activity recovery rate reaches 90%, the stability is excellent (the enzyme activity recovery rate can be preserved for 60 days at the temperature of 4 DEG C in a dark place), sample pretreatment is not needed in detection operation, a result can be obtained at the normal temperature for 15 min, the stability and repeatability are good, and the problems that a traditional instrument is high in detection cost and long in time consumption are solved; the method has a remarkable basic popularization value.

The invention discloses a pesticide residue quantitative analysis system based on Raman spectrum, and relates to the technical field of analytical chemistry and detection.The pesticide residue quantitative analysis system comprises an acquisition module, a processing module, an analysis module and a regulation and control module, the acquisition module is used for acquiring samples and Raman scattering signals, converting the Raman scattering signals into original spectrum data and transmitting the original spectrum data to the processing module; calibrating the original spectrum data, acquiring calibrated spectrum data, transmitting the calibrated spectrum data to an analysis module, comparing the calibrated spectrum data with a pesticide standard spectrum library, outputting a quantitative analysis result of pesticide residues, transmitting the quantitative analysis result to a regulation and control module, storing the pesticide standard spectrum library and the calibrated spectrum data, and correcting sample detection deviation by using the calibrated spectrum data. And through the regulation and control unit, full-process automatic operation from sample processing to result output is realized, the detection reliability is improved through spectrum compensation and characteristic peak comparison, efficient automation is realized through task scheduling and time sequence synchronization, and hardware fault tolerance, authority management and multi-scene adaptation capabilities are realized.

The invention belongs to the technical field of analytical chemistry and pesticide residue detection, and relates to a phosphate hydrolysis nano-enzyme sensing array-based p-nitrophenol pesticide identification and detection method, which comprises the following steps of: performing five groups of parallel data matrixes on each type of p-nitrophenol pesticide to distinguish single / five types of p-nitrophenol pesticides with different concentrations; establishing a discrimination model of different concentrations / types of pesticides by using the data matrix, and obtaining an HCA map and an LDA score map of the p-nitrophenol pesticides through hierarchical clustering analysis and linear discriminant analysis; after the absorbance value of the sample to be detected is measured, the sample to be detected can be identified and detected through comparison. According to the present invention, by using the inherent dynamic difference existing during the catalytic hydrolysis of different p-nitrophenol pesticides by CeO2, a plurality of reaction time points are selected to measure the absorbance change, the time-resolved sensing array fingerprint is constructed, and the type identification and the concentration quantification of a variety of p-nitrophenol pesticides and the mixture thereof are achieved by combining the pattern identification algorithm, such that the sensitivity is high, and the detection result is accurate. The measurable concentration range is 1-50 mu g / mL.

The invention belongs to the technical field of extraction and purification of plant components, and relates to a low-temperature freezing vacuum drying nano processing method for extracting anthocyanin from purple corn, in particular to a method for extracting anthocyanin from corn kernels, cores, straws, roots and stigmas of purple corn. The processing method comprises the steps of pretreatment, concentration, hydroxyl radical treatment, quick freezing, nano crushing and the like. A hydroxyl radical treatment method is adopted, so that harmful substances such as pesticide residues can be efficiently, quickly and thoroughly removed; the effect of extracting the anthocyanin from corn kernels, roots, cores, stems and stigma of purple corn by adopting a low-temperature freezing vacuum drying technology is remarkable, the purification rate is high, and the anthocyanin containing 99% of biological activity is achieved. The nanoscale anthocyanin is processed by adopting jet mill equipment and can be conveniently absorbed by a human body and widely applied to various fields.

The invention discloses a traditional Chinese medicine decoction piece full life cycle data management system and method, and relates to the field of data processing, and the method comprises the steps: collecting traditional Chinese medicine planting environment data through a sensor, and encrypting and storing the data through a block chain; in the processing link, the moisture evaporation condition is predicted through an LSTM neural network, and an early warning mechanism is set; in the processing process, the process is adjusted through a Raman spectrum; components and pesticide residues of the finished product are ensured to be qualified through mass spectrometric detection; the clinical end collects prescription and curative effect data optimization process; all data are synchronized through a block chain and a cross-chain protocol; through a data lake architecture and a graph neural network model, a planting area and a processing technology are optimized. The system has the advantages that through technologies such as a sensor, a block chain and an AI model, whole-process data tracing and optimization from planting to clinical use are realized, the quality and curative effect of a traditional Chinese medicine product are improved, and transparency and safety of the whole process are ensured.

The invention discloses a rapid detection device for pesticide residues in food and a use method of the rapid detection device, and relates to the technical field of food detection.The rapid detection device comprises a detector body and a box, a containing groove is formed in the inner side of the detector body, and one end of the detector body is fixedly connected with the box; one end of the detector main body is fixedly connected with a feeding mechanism, a cuvette is placed on the inner side of the feeding mechanism, and a pressing mechanism is arranged above a placing groove, so that the integrated design realizes full-process automation from sample pretreatment to detection, and effectively solves the problems of tedious operation and low efficiency of a traditional method; the overall detection efficiency is improved, and the method is particularly suitable for rapid screening of batch samples.

The invention relates to the technical field of facility agriculture, in particular to a green cultivation method for greenhouse water culture of Chinese chives. Through integration of intelligent greenhouse facilities, staged nutrient solution regulation and control, physical-biological-chemical synergistic disease and insect pest prevention and control and an annual production management technology, the problems of continuous cropping obstacles, high pesticide residues, winter dormancy and the like existing in traditional soil culture of Chinese chives are solved. According to the method, annual production of Chinese chives is achieved, the harvesting period of each crop is shortened to 20-30 days, compared with traditional soil culture, the annual yield is increased by 40% or above, the pesticide use amount is reduced by 95%, the pesticide residue percent of pass is 100%, and the product is free of heavy metal pollution, meets the green food standard and is suitable for green transformation and popularization of facility agriculture.

The invention relates to the technical field of pesticide residue detection, and discloses a method and a device for detecting the residual quantity of tetratolfenpyrad and metabolite thereof, and the method for detecting the residual quantity of tetratolfenpyrad and metabolite thereof comprises the following steps: step 1, dissolving a sample of tetratolfenpyrad and metabolite thereof in an extraction solvent; then injecting the extracting solution into a solvent bottle on a liquid chromatograph; and secondly, the inserting part is inserted into a bottle head at the top of the solvent bottle, the inserting part is in butt joint with a straight pipe fitting in the solvent bottle, a second magnetic ring on the inserting part is in magnetic connection with a first magnetic ring on the bottle head, then a first liquid conveying pipe on the inserting part is in butt joint with a butt joint part on the hollow block, and assembly of a section of pipeline structure is completed. Through segmented assembly of the liquid conveying pipeline, magnetic ring limiting and branch pipe fitting design, the pipeline connection stability is effectively enhanced, leakage is avoided, it is ensured that a chromatographic analysis experiment is continuously and accurately carried out, the experiment failure risk is reduced, and solvent waste and environmental pollution are reduced.

The invention relates to the technical field of vegetable pickling, and discloses a multi-layer vegetable pickling process based on a turnover structure, which comprises the following steps: S1, selecting fresh vegetables with moderate maturity and compact texture, and thoroughly cleaning silt, pesticide residues and impurities on the surfaces of the vegetables with flowing clear water; s2, performing root cutting, peeling and leaf removing treatment according to the characteristics of the vegetables, and airing to remove excessive water; and S3, putting the pretreated vegetables and the pickling liquid into vegetable pickling equipment. The multi-layer vegetable pickling process based on the overturning structure can effectively solve the problems that in the prior art, vegetables and pickling liquid are commonly placed in an open or covered pool body, the pickling process is completed by means of natural permeation, but the occupied area is large, the utilization rate is low, the requirement of large-scale production is difficult to meet, and meanwhile, the cost is low. A traditional device cannot achieve independent pickling of multiple varieties of vegetables, different varieties of vegetables need to share the same pickling pool, and flavor tainting is easily caused.

The invention discloses a method for evaluating pesticide residues in food based on big data, and relates to the technical field of data management.The method comprises the steps that firstly, a purification process is optimized according to purification process parameters of samples and laboratory environment states, and it is ensured that each sample is subjected to optimal pretreatment; then, automatically extracting signal characteristics of optical biosensing or immunoassay in a rapid detection stage, preliminarily marking suspicious positive, and switching to a high-resolution mode for confirmation; then, double spectrum scanning is carried out on the samples marked as positive, chromatographic conditions are subjected to graded optimization according to the values, and early warning is automatically given out for abnormity or drifting in the whole process; the self-adaptive mechanism from purification to detection to confirmation not only realizes omnibearing correction of matrix interference, but also remarkably improves sensitivity and specificity of pesticide residue assessment through continuous iteration of real-time feedback, thereby ensuring high efficiency and reliability of food safety monitoring.

The invention relates to the technical field of pesticide detection, in particular to a pesticide residue detection method and system based on artificial intelligence, and the method comprises the following steps: obtaining an image and normalizing brightness difference, constructing a gradient trend labeling region, extracting features to generate a label weight sequence, matching categories to calculate residue intensity, and outputting an identification result. According to the method, local brightness comparison is enhanced through difference value normalization of image region blocks, region attributes are labeled in combination with a multi-direction gradient change trend, the dynamic perception capability of deposition features is improved, a multi-dimensional feature system is constructed by fusing brightness, edge and density indexes, the screening precision of pesticide residue salient regions is enhanced, and the screening precision of the pesticide residue salient regions is improved. A label dependency sequence is constructed based on color difference path sorting, structural optimization of category judgment is achieved, the micro-difference recognition capacity is improved through texture and gray matrix double alignment, primary and secondary screening output is conducted according to the relation between pesticide residue intensity and a threshold value, the classification guidance and risk screening functions are achieved, and the capacity of adapting to supervision requirements is improved.

The invention relates to a synergistic composition for preventing and treating celery root rot. The tannic acid and fludioxonil in the synergistic composition disclosed by the invention show a relatively good synergistic effect in a compounding mass ratio range of (1: 50)-(50: 1). According to the synergistic composition disclosed by the invention, tannic acid with weak bacteriostatic activity on celery root rot and fludioxonil with excellent bacteriostatic activity are compounded for use, so that a remarkable synergistic effect is achieved, the prevention and treatment effect is far higher than that of a single agent, and the synergistic effect is achieved by reducing pesticide. Besides, according to the synergistic composition disclosed by the invention, the plant source tannic acid and the chemical bactericide fludioxonil are compounded for use, and the use amount of chemical agents is reduced, so that the resistance risk of the chemical bactericide is reduced, pesticide residues and ecological environment pollution are reduced, and the synergistic composition has a good application prospect.

The invention belongs to the technical field of pesticide preparations, and particularly relates to kresoxim-methyl drug-loaded microspheres as well as a preparation method and application thereof. The 9% pyraclostrobin drug-loaded microspheres (Pyr (at) COzein) are prepared by modifying a Zein-pesticide emulsifier 500 # complex with n-caprylic alcohol, and the n-caprylic alcohol has a synergistic effect on pyraclostrobin, so that not only can the defect of a sustained and controlled release preparation in the aspect of fast-acting sterilization be made up, but also the use dosage of pyraclostrobin can be reduced, the risk of pesticide residue can be reduced, and the drug-loaded microspheres have a good application prospect. The ecological safety is improved.

The invention discloses an analysis method for simultaneously determining residues of telolenol, pentnitrophenol, pentachlorophenol and poisonous phenol in grease. The method comprises the following specific steps: (1) dissolving a grease sample with dichloromethane, carrying out vortex uniform mixing and centrifugation with a sodium hydroxide solution, and taking supernatant; (2) adding formic acid, anhydrous magnesium sulfate, sodium chloride and acetonitrile into the supernatant, carrying out vortex centrifugation, taking the supernatant, freezing and filtering; (3) adding anhydrous magnesium sulfate, a 2% triethylamine solution, multi-walled carbon nanotubes and C18 powder into the filtrate for purification, and filtering after centrifugation to obtain a to-be-detected solution; (4) preparing a matrix blank solution from the negative blank grease through the same pretreatment, and preparing a standard solution with gradient concentration of 1-20ng / mL; and (5) performing quantitative analysis by adopting a matrix standard curve external standard method through an LC-MS / MS detection system. The method has the advantages of economy, simplicity, convenience, high accuracy and high sensitivity, is easy to popularize and use, and can meet the high-throughput detection requirement of phenolic pesticide residues in a complex grease matrix.

The invention discloses bacillus tequilensis 83IBC04-2 and application thereof, the bacillus tequilensis 83IBC04-2 comes from a deep sea hydrothermal zone and is preserved in China Center for Type Culture Collection on October 15, 2025, and the preservation number is CCTCC NO: M20252212. The compound has a remarkable antagonistic effect on a plurality of important pathogenic fungi such as tomato gray mold, tomato early blight, rice blast and wheat scab, can be used for preparing a broad-spectrum and high-efficiency biological bactericide, effectively reduces the use amount of chemical pesticides, and reduces pesticide residues and environmental pollution.

The invention relates to a diatomic catalyst for detecting carbosulfan pesticide residue and a preparation method, and belongs to the technical field of food safety detection. According to the invention, a reducing sulfide generated by breaking a nitrogen-sulfur (N-S) bond of carbosulfan under an acidic condition is used as a specific substance. The reductive sulfide not only can prevent bimetal active sites in the diatomic catalyst, but also consumes hydroxyl radicals generated by catalyzing hydrogen peroxide (H2O2), and the sensing switch enzyme formed by a dual enzyme inhibition mechanism causes the enzyme substrate 3, 4-dihydroxyl-3, 4-dione to be converted into the diatomic catalyst, so that the catalytic activity of the diatomic catalyst is improved. The blue color of the 3, 3 ', 5, 5'-tetramethyl benzidine (TMB) gradually becomes light along with the increase of the concentration of carbosulfan, sensitive visual detection is implemented, and after a blue product is read by using an ultraviolet spectrophotometer, the absorbance value generated by an ultraviolet absorption spectrum at 654 nm is linearly dependent on carbosulfan. The prepared diatomic catalyst effectively improves the detection sensitivity of the carbosulfan pesticide residue, and has good popularization and application prospects.

The invention discloses an organic ecological planting method for apples, and particularly relates to the field of fruit tree cultivation, and the method comprises the following specific planting steps: S1, soil foundation improvement; s2, annual fertilizer management; s3, flower and fruit management; s4, ecological prevention and control of diseases and pests; and S5, harvesting management. According to the method, through soil foundation improvement, annual fertilizer management, flower and fruit management, ecological pest control and harvesting management, no chemical fertilizer or pesticide is input, five purposes are achieved, and meanwhile it is guaranteed that soil is healthy, tree bodies are robust and fruits are high in quality; according to the method, microbial agents and bio-organic fertilizers are mainly adopted to replace chemical fertilizers, biological prevention and control are adopted to replace chemical pesticides, precise farming operation is combined, apple planting achieves zero pesticide residue, high quality and high yield, the method is suitable for high-end organic ecological apple production, the purposes of high yield, high quality, safety and high efficiency of apple planting are achieved, and the method has wide application and popularization prospects.

The invention provides a rice disease identification and monitoring method based on a KBnet network model, and the method comprises the steps: obtaining a current rice leaf image and the text description of the current rice leaf image, and carrying out the data preprocessing of the current rice leaf image and the text description of the current rice leaf image, and obtaining vectorized data, taking the vectorized data as input of the target model, and performing reasoning output by the target model to obtain a disease monitoring result of the current rice leaf image. Through automatic segmentation and identification of plant diseases and insect pests on rice leaves, KBNet can provide disease distribution information and help research and evaluation of crops and scientific management of plant diseases and insect pests. Therefore, excessive use of pesticides can be reduced, negative effects on the environment can be reduced, and pesticide residues are reduced.

The invention relates to the technical field of agricultural production, and discloses a salt-alkali-tolerant phosphorus-dissolving pesticide-reducing fungus YM-1 and application thereof in corn planting, the salt-alkali-tolerant phosphorus-dissolving pesticide-reducing fungus YM-1 is a purple blue bacterium, the preservation number of the strain is CGMCC No.42313, and the strain has multiple functions of resisting salt and alkali, dissolving insoluble phosphorus and degrading 2-methyl-4-chlorophenoxyacetic acid pesticide residues. The strain, a culture thereof or a microbial inoculum prepared from the strain are applied to agricultural production, and plant growth is promoted by exerting the composite biological function of the strain under saline-alkali stress. According to the invention, the synergistic effect of saline-alkaline tolerance, phosphate solubilization and pesticide degradation of the strain is utilized, so that the plant growth inhibition is relieved in the adverse situation, the soil phosphorus bioavailability is improved, the pesticide-polluted soil is repaired, and the plant seed germination, root development and plant growth are promoted. The strain has an important application prospect in the aspect of developing a microbial agent for saline-alkali soil improvement.