116 results about "Disease occurrence" patented technology

The invention belongs to the technical field of aquaculture automation control, and particularly discloses an aquaculture environment intelligent disease early warning system and a storage medium, the system comprises a data acquisition layer, a data transmission layer, a processing platform layer and an application service layer; the data acquisition layer comprises an environment monitoring unit, a water quality sensor group, portable detection equipment and a data acquisition module; the data transmission layer comprises a wireless transmission module and an edge computing node; the processing platform layer comprises a machine learning prediction model and an environment factor correlation analysis model; the application service layer comprises an early warning information push module and a prevention and control scheme generation module. According to the intelligent disease early warning system for the aquaculture environment and the storage medium, a correlation model of environmental factors and disease occurrence is innovatively established, the early warning accuracy is 85% or above by adopting a machine learning technology, comprehensive monitoring and real-time early warning in the aquaculture process can be achieved, and accurate and efficient disease prevention and control are supported.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention discloses a novel tsRNA high-sensitivity detection kit based on CRISPR (clustered regularly interspaced short palindromic repeats) / Cas13a-Cas14a, and belongs to the field of tumor marker detection. The tsRNA (tRNA-derived small RNA) is a diagnostic marker which is found recently and has great potential. In order to realize the clinical transformation of tsRNA as soon as possible, the tsRNA detection kit is developed on the basis of nuclease P1 combined with CRISPR / Cas13a-Cas14a linkage dual systems, and comprehensive, ultra-sensitive, high-specificity, low-cost and rapid tsRNA detection is realized through multiple signal amplification. Compared with the traditional technology, the sensitivity of the method is improved by 105-107 times, 1% of tsRNA in the mixture can be detected, and the method has more advantages in clinical application. Therefore, a new scheme is provided for application of the tsRNA in clinical diagnosis of diseases, and meanwhile, a reliable technology is provided for mechanism research of the tsRNA in occurrence and development of the diseases.

The invention relates to the technical field of pest control, in particular to a pest outbreak process mining risk prediction method and system based on multi-source factors. The method comprises the following steps: measuring regional meteorological data, regional crop soil fertility degree and regional disease and pest occurrence data corresponding to crop disease and pest monitoring points in real time; the corresponding pesticide spraying usage amount is obtained through the crop disease and insect pest monitoring points, and regional occurrence density accounting and disease and insect pest outbreak mining analysis are carried out on regional disease and insect pest occurrence data based on the pesticide spraying usage amount. Obtaining a corresponding regional pest and disease outbreak characteristic mode under the meteorological condition and a correlation characteristic relation between regional soil fertility and pest and disease outbreak; and performing multi-source factor risk prediction and control decision support analysis on the crop disease and pest monitoring points to generate a disease and pest outbreak area control suggestion scheme. According to the invention, scientific and accurate disease and pest risk early warning can be provided for agricultural production.

The invention relates to a grape powdery mildew initial attack time simulation method, and aims to establish a grape powdery mildew early warning model by combining meteorological data and a grape growth cycle, and optimize and accurately predict the initial attack date of grape powdery mildew. According to the method, meteorological data including the air temperature, the relative humidity, the rainfall, the wind speed and the wetting duration and the germination day of grapes are used as input variables. According to the method, a simulated annealing algorithm is adopted to optimize key parameters in the model, optimized parameter values are substituted into the model, and the model is driven to start to simulate the occurrence time of the first grape powdery mildew disease. According to the method, the initial time of disease occurrence can be accurately pre-warned, timely pesticide application is realized in the key period of prevention and control, and the influence on the environment is reduced.

The invention provides a disease identification method and device for a dirty ballast bed in a spring melting period, and relates to the technical field of ballast bed disease identification, and the method specifically comprises the steps: building a discontinuous ligand cementation contact model based on a discrete element simulation method, and introducing a temperature-rigidity coupling factor and a temperature-strength coupling factor into the model; the method comprises the following steps: acquiring mechanical response data of a dirty ballast under the action of different temperature gradients, dirty contents and dynamic loads through a partition cascade temperature control large triaxial experimental device, and constructing a critical dynamic strain calculation model by using the mechanical response data based on a plastic stability theory; a ballast bed structure simulation model is established by combining the cemented contact model and the critical dynamic strain calculation model, a ballast bed microscopic contact behavior under the dynamic-melting coupling effect in the spring melting period is simulated, and simulation data are obtained; according to the method, correlation analysis is carried out on simulation data to generate a disease assessment index of the ballast bed, so that the disease occurrence risk is assessed, and the risk dynamic assessment of the dirty ballast bed in the spring melting period is realized.

The invention relates to the field of agricultural management, and particularly discloses a smart agriculture integrated management method and system based on the Internet of Things, and the method comprises the steps: carrying out the coding of multi-source time sequence environment data, such as weather and soil, through a recurrent neural network, and extracting an environment vector representing the potential risk of disease occurrence; meanwhile, a current visual feature map of the crop is extracted from the unmanned aerial vehicle remote sensing image by using a convolutional neural network. Furthermore, topological correlation analysis and saliency re-calibration are carried out on local semantics in the visual feature map through a graph neural network and an attention mechanism by taking an environment vector as guidance, so that a deeply coupled crop vision-environment state saliency fusion vector is generated. The fusion vector not only contains apparent diseases of crops, but also contains environmental causes causing the diseases. And finally, joint prediction of disease probability and severity level is carried out based on the fusion vector, so that closed-loop management from data acquisition to accurate and graded early warning is realized.

The invention discloses a corn and wheat water and fertilizer adaptive control method and system, and relates to the technical field of water and fertilizer control. S2, disease monitoring and diagnosis; s3, nutrient demand analysis; s4, adjusting the irrigation amount; and S5, growth data feedback. According to the invention, through disease monitoring and diagnosis, whether the crops in the planting area have diseases and the types and severity of the diseases can be found in time, through nutrient demand analysis, the crop nutrient demands of the disease area and the non-disease area can be obtained respectively, and according to the disease occurrence condition and the nutrient demands of the crops, the crop quality is improved. Real-time partition accurate regulation and control are achieved, more accurate self-adaptive water and fertilizer irrigation amount regulation is achieved, a suitable growth environment is provided for crops, the influence of diseases on crop growth is relieved, recovery and growth of the crops are guaranteed, and therefore the yield and quality of the crops are improved.

The invention discloses a disease cause identification method based on causal learning. The method comprises the following steps: preprocessing an observable medical data set; calculating a fitting relationship between each to-be-inspected variable and the data set by using a Bayesian marginal likelihood probability, and separating causal and confounding factors in the disease influence factors; an iterative causal relationship generation algorithm is utilized, the fitting degree of the causal relationship is iteratively judged on the basis of a Bayesian scoring criterion, and causal influence factors of disease outcome are described in a causal graph form into a direct level and an indirect level; for the discovered causal relationship, a conditional expectation expression is modeled based on a back door criterion, a front door criterion and a tool variable criterion, and a causal effect value is estimated based on a linear regression model, so that the influence degree of the cause of the disease on the outcome of the disease is quantified. According to the method, a causal learning method is used for researching high-risk influence factors possibly causing diseases, the causal effect value and the change trend of the direct influence factors and the target result are determined, and the cause of related diseases can be known.

The present application provides a method for recommending examination items based on a Bayesian network and an auxiliary decision-making system. The method includes: obtaining a first conditional probability distribution of the examined items for the target disease, a second conditional probability distribution of the examined items for the to-be-examined items, and a third conditional probability distribution of the examined items for the to-be-examined items based on the target disease according to the disease occurrence probability grading corresponding to the examined items and the disease occurrence probability grading corresponding to the to-be-examined items; calculating a fourth conditional probability distribution of the to-be-examined items for the target disease based on the examined items according to the first conditional probability distribution, the second conditional probability distribution, and the third conditional probability distribution; and selecting the first priority recommended examination items from the to-be-examined items according to the fourth conditional probability distribution and the confidence level corresponding to the to-be-examined items. By adopting the above method, the present application solves the problem that the existing disease screening methods fail to fully utilize the known patient diagnosis data, resulting in inaccurate recommendation results.

This invention discloses an image recognition-based method for assessing grape powdery mildew. By introducing standardized sample preparation and uniform inoculation methods, this invention reduces the interference of randomness and spatial heterogeneity of natural field infection on the assessment results, making the disease occurrence process more controllable among different materials, thereby improving the stability and comparability of phenotypic data. Simultaneously, image recognition and machine learning technologies are used to automatically identify and calculate the area of ​​powdery mildew-infected regions, avoiding the reliance on the experience of assessors in traditional manual visual grading. This enables a continuous quantitative description of the degree of powdery mildew infection, improving the consistency and accuracy of the assessment results. This invention constructs a large-scale image analysis workflow that can be used for batch processing, significantly improving the efficiency of disease assessment, reducing the workload of manual investigation, and making this method applicable to applications requiring the processing of large numbers of samples, such as germplasm resource evaluation and breeding material screening.

The application provides a crop disease risk assessment method and related device, and relates to the field of crop disease prevention and control, which comprises the following steps: obtaining elevation data, meteorological data and vegetation data in each period; selecting target meteorological data and target vegetation data with a correlation less than a preset correlation threshold from the meteorological data and the vegetation data in each period to obtain target meteorological data and target vegetation data in each period; determining disease occurrence risk data in each period according to the elevation data, the target meteorological data and the target vegetation data in each period; and determining disease occurrence risk data occurring on target crops in each period according to the disease occurrence risk data in each period and distribution data of the target crops in each period. Since the elevation data, the meteorological data and the vegetation data have an impact on disease distribution, the disease risk is assessed based on the elevation data, the meteorological data and the vegetation data, so that the accuracy of the evaluation result is improved.

The application discloses a solanaceae family seed automatic disinfection working method based on dynamic adjustment of humidity and heat, relates to the technical field of seed automatic disinfection, and is used for solving the problem that precise regulation and control based on physiological state is difficult to realize, and the probability of disease occurrence is increased. The seed coat state and conductivity of solanaceae family seeds after disinfection are detected, the proportion of a highlighted area is calculated, and the ethanol concentration is collected. The humidity response characteristics are analyzed through the proportion of the highlighted area and the ethanol concentration. The number of cracked seeds is counted, and the temperature response characteristics are analyzed in combination with the conductivity. Whether a ventilation processing mechanism is started is comprehensively judged. After ventilation, a regulation and control time is set. The respiration intensity and the tray water film thickness are monitored in real time. The humidity and heat states are classified according to the respiration change trend and the water film thickness. Cooling or drying channels are selected according to the classification results. The dynamic monitoring and regulation and control of the humidity and heat states after disinfection are realized. The ventilation channels are automatically selected through multi-dimensional sensing. The disinfection environment is optimized. The seed quality and storage stability are effectively improved.

According to the invention, atrial fibrillation and hypertension are combined, and a novel HFpEF double-strike animal model is developed. Specifically, the animal model disclosed by the invention can stably develop to a mild structure and functional disease stage within three weeks of double strike, reaches an end stage within two subsequent weeks and gradually dies, and the animal model is highly overlapped with the pathophysiological phenotype of the existing human HFpEF. The animal model stably and rapidly develops in a staged manner, is easy to implement, and provides a valuable tool for molecular mechanism research and drug development of disease occurrence and development.

This invention provides an early warning system for critical care nursing in internal medicine based on multi-parameter monitoring, belonging to the field of early warning system technology. It includes: a vital sign monitoring module for real-time collection of the patient's vital sign factors, taking the average value of the amplitude of each real-time vital sign factor as a benchmark reference value; a similarity early warning analysis module for establishing a mathematical model of the patient's self-monitoring, calculating the early warning similarity index corresponding to each real-time vital sign factor; calculating a reliability index by comparing the total short-term change value with the overall average change value of each real-time vital sign factor; and an early warning decision module for establishing two mechanisms to determine early warnings. This invention extracts various parameters from the patient's past multiple disease occurrence points and calculates the average value, and collects the median value of the disease occurrence point with the largest interval as a reference benchmark value, forming a unique benchmark reference value; thus, the abnormality judgment is entirely based on individual physical differences and historical response patterns, solving the problem of misjudgment due to uniform standards caused by physical differences.

The utility model relates to the technical field of biological experiment devices, in particular to a mouse rearing cage which comprises a cage frame body, a plurality of cross beam sets, connecting pieces, mouse cages and a ventilation mechanism, the ventilation mechanism is arranged on the cage frame body, all the cross beam sets are arranged on the cage frame body in the vertical direction, and the connecting pieces are arranged on the cross beam sets. The mouse cage is hung on the two first cross beams of one cross beam set, the number of the connecting pieces is multiple, the two ends of each first cross beam are fixedly connected with the two connecting pieces in a one-to-one correspondence mode, a plurality of clamping grooves are formed in the cage frame body, and the connecting pieces are detachably and fixedly connected with the clamping grooves through bolts. The cage frame is easy to assemble and disassemble due to the modular design, the ventilation mechanism provides a good environment for laboratory mice, and the disease incidence rate is reduced; and the applicability of the cage frame is improved by the adjustable cross beam group.

The utility model discloses an ecological agriculture breeding device in the technical field of ecological agriculture, which comprises a first support frame, a first breeding cage and a second breeding cage are welded and mounted on the first support frame, and the first breeding cage is positioned right above the second breeding cage; a second supporting frame and a third supporting frame are welded and installed right in front of the first supporting frame, the second supporting frame is located right on the left of the third supporting frame, a first fixing block is installed right above the second supporting frame in a fastening mode, and a second fixing block is installed right above the third supporting frame in a fastening mode. A limiting scraper is installed between the fixing blocks, meanwhile, a motor is installed above the supporting frame to provide power for the conveying belt, and a collecting box is placed below the scraper, so that the purposes of efficiently cleaning excrement below the breeding cage and collecting the excrement in a centralized mode are achieved, and the effects of keeping the breeding environment clean and sanitary, reducing the disease occurrence risk and improving the overall breeding efficiency are achieved.

The invention discloses a method for distinguishing different gene manipulated cells by in-vivo fluorescence labeling, which combines Cre / loxP mediated homologous chromosome recombination, a binary expression system and a CRISPR / Cas9 mediated gene mutation technology. And constructing a technology, namely in-vivo universal genetic chimeric analysis (iVGMA), which can be used for simultaneously carrying out differential marking on cells subjected to gene overexpression, gene knockout and simultaneous gene overexpression and gene knockout by utilizing different colors. According to the invention, accurate marking of gene manipulation (knockout or overexpression) is realized through different fluorescence colors in a whole genome range and on a single cell level, and research and application of researching genetic chimeric cell and molecular mechanisms and disease generation and evolution mechanisms, improving drug screening effectiveness and the like are facilitated.

The application discloses an application of a prostate hyperplasia related marker in preparation of a product for detecting prostate hyperplasia, and the prostate hyperplasia related marker is at least one selected from MINP1, ACTBL and APOC4 proteins. In the technical scheme, MINP1, ACTBL and APOC4 are combined as a detection marker for prostate hyperplasia (BPH), so that the complementarity of the three in different stages of diseases can be fully exerted, and the comprehensive performance of the detection product is obviously improved. MINP1 and ACTBL participate in disease progression by regulating prostate cell proliferation and apoptosis, and APOC4 can present significant expression changes in blood cells of early BPH patients, so that BPH disease screening can be accurately performed. When the three are applied in cooperation, early screening of BPH can be realized, and the activity and severity of the lesion can be evaluated, so that a detection system covering the whole process of disease occurrence and development is formed, the limitation of a single marker in the diagnosis dimension is effectively made up, and the comprehensiveness and accuracy of BPH detection are greatly improved.

The invention discloses a specific PCR (Polymerase Chain Reaction) detection primer IKF5 / IKR4 of Karst colletotrichum gloeosporioides and application of the specific PCR detection primer IKF5 / IKR4, the specific detection primers IKF5: 5 '-GTAGGCCGTCCCCTGAAAAG-3' and IKR4: 5 '-CCAAGCAGCTTGAGGGTT-3' on the basis of ITS (International Transcription Sequence) sequences, and the sensitivity of the specific detection primers IKF5: 5 '-GTAGGCCGTCCCTGAAAAG-3' and IKR4: 5 '-CCAAGCAGCTTGAGGGTT-3' is 100 pg. Mu L <-1 >. Research results show that the pair of primers can efficiently and specifically detect the Karst colletotrichum in the mango leaves, and a reliable technical tool is provided for early diagnosis of mango anthracnose, analysis of a disease occurrence rule and construction of a precise prevention and control strategy.

The invention relates to the technical field of big data analysis and decision support, in particular to a smart agriculture big data analysis and decision support platform and an implementation method thereof, and the platform comprises an agricultural data collection unit which is used for collecting farmland data and collective fund, asset and resource information in real time; and the agricultural data analysis unit obtains the occurrence rate of diseases and insect pests through a nonlinear algorithm according to the farmland data and the rural collective fund, asset and resource information collected by the agricultural data acquisition unit, and obtains the early warning judgment of the rural collective fund, asset and resource information data through a linear regression algorithm. When a big data analysis and decision platform is researched, a pest and disease occurrence rate model, a crop yield prediction model and a collective fund, asset and resource data judgment model are respectively constructed, and the pest and disease occurrence rate is introduced into the crop yield prediction model to further obtain a crop yield prediction result.

Provided, according to one embodiment of the present disclosure, are an artificial intelligence-based disease prediction method performed by a computing device, a program, and a device. The method may comprise: obtaining a biosignal of a subject to be predicted; generating physiological information of the subject on the basis of the obtained biosignal using a pre-trained first neural network model; and generating prediction information regarding disease occurrence in the subject on the basis of the obtained biosignal and the generated physiological information using a pre-trained second neural network model.

The invention relates to the field of material analytical chemistry and biochemistry, and provides a continuous batch pretreatment device and method for proteomics, glycoproteomics and phosphorylated proteomics samples. According to the continuous batch pretreatment device for the proteomics, glycoproteomics and phosphorylated proteomics samples, under the assistance of the filler, the whole process of protein denaturation, aggregation capture, enzymolysis and complete glycopeptide and phosphorylated peptide enrichment in biological samples can be achieved. Compared with a free solution method conventionally used in proteomics, the sample pretreatment method based on the device has the advantages that the sample treatment time can be effectively shortened, and continuous analysis of mass proteome, glycoprotein group and phosphorylated proteome of trace protein samples can be realized; the method is expected to be widely applied to the aspects of disease occurrence mechanisms, drug action mechanisms, clinical detection, biomarker discovery and the like.

This invention relates to the field of poultry farming technology and discloses a method for reducing antibiotic use in chicken farming. This invention solves the problems of poor hygiene in the farming environment, high chicken disease rates, and the need for excessive antibiotic feeding. The method employs a small, frequent feeding approach, adding astragalus polysaccharide, phytase, fish meal, and meat and bone meal to the feed to enhance the chicken's immune function and reduce disease occurrence during the rearing process. Simultaneously, during feeding, the bottom plate is lowered, and an electric telescopic rod A and scraper are used to clean the manure on the baffle. Water is sprayed and air is blown onto the wire mesh frame during movement to remove impurities. When the feed reaches the bottom, a connecting pipe is used to change the water flow direction to clean the scraper. During ventilation, a reciprocating cleaning brush is used to remove down adhering to the mesh, preventing pathogens carried by the down from causing disease. Ultimately, this method ensures hygiene in the farming environment and reduces antibiotic use.

The invention provides a disease risk positioning module and a disease risk positioning method, and the disease risk positioning module is mainly used for carrying out multiple times of grouping and iteration on a sample data set formed by collected physiological data of a plurality of users through a grouping algorithm, generating a disease risk grading table when a grouping result meets a convergence condition, and positioning the disease risk according to the disease risk grading table. Wherein the disease risk grading table can be used for subsequent disease risk grading of the user, calculation of risk scores of the physiological parameters of the user corresponding to the grading result and calculation of the prediction probability of disease occurrence of the user corresponding to the grading result.

The present application relates to the technical field of induced pluripotent stem cell, and particularly relates to mesoderm differentiation specific human induced pluripotent stem cell and application thereof. The present application provides human induced pluripotent stem cell BC-hiPSC-ME-20, which is preserved in China General Microbiological Culture Collection Center, and the preservation number is CGMCC No.46536. The cell strain has multi-lineage differentiation potential and high-efficiency mesoderm directional differentiation capacity, has stable pluripotency and excellent passage stability, and has higher safety, and has a good application prospect in disease model construction, disease occurrence and development mechanism research, drug screening and cell therapy.

The invention discloses a specific PCR (Polymerase Chain Reaction) detection primer TKF10 / TKR10 of Karst colletotrichum gloeosporioides and an application of the specific PCR detection primer TKF10 / Wherein the sensitivity of primers TKF10: 5 '-GCGGGGCTAACTCTGAAT-3' and TKR10: 5 '-CGCCAGAGATGTTTGCCTG-3' based on a TUB2 (beta-tubulin) gene is 1 ng * [mu] L <-1 >, and the sensitivity of the primers TKF10: 5 '-GCGGGGCTAACTCTGAAT-3' and TKR10: 5 '-CGCCAGAGATGTTTGCCTG-3' based on the TUB2 (beta-tubulin) gene is 1 ng * [ Research results show that the pair of primers can efficiently and specifically detect the Karst colletotrichum in the mango leaves, and a reliable technical tool is provided for early diagnosis of mango anthracnose, analysis of a disease occurrence rule and construction of a precise prevention and control strategy.

This invention discloses a method for assessing shrimp health status based on multi-source information fusion, which relates to the field of data processing technology. The method includes the following steps: acquiring and preprocessing shrimp images at different growth stages; generating training images using artificial labels; extracting features such as shrimp outline length, area, and shell contrast; establishing a target recognition and feature extraction model based on the training images; identifying shrimp in the monitored aquaculture environment and extracting their behavioral characteristic parameters after training; calculating the behavioral assessment influence coefficient and growth assessment influence coefficient; and obtaining water source characteristic parameters to calculate the disease incidence influence coefficient; finally, generating a comprehensive health status assessment index, comparing it with a health judgment threshold, and outputting the health status judgment result. This achieves effective assessment of shrimp health status, real-time monitoring, and early warning, helping farmers take timely measures to prevent disease occurrence and improve shrimp survival rate and economic benefits.

The invention relates to the technical field of agricultural meteorological disaster monitoring and early warning, and discloses a potato late blight ensemble forecasting method and system based on weather forecast. According to the method, firstly, the error characteristics of historical weather forecast in a farmland microclimate station are analyzed to obtain localized error statistical characteristic information, on this basis, a random disturbance method of error variance following is adopted to perform scientific disturbance on real-time forecast to obtain multi-member ensemble forecast, the multi-member ensemble forecast is input into a preset potato late blight model, and a potato late blight model is obtained. According to the method, multiple groups of disease occurrence and development forecast results are obtained, probability forecast and deterministic products containing multiple percentile forecast are generated through release, and according to a preset optimal decision rule, an optimal set percentile forecast is selected from a series of deterministic products and is popularized and applied. According to the method, the problems that a traditional early warning method is insufficient in advance, single in determinacy prediction error conduction and lack of risk quantification capability are solved, and the accuracy and perspectiveness of disease prediction are remarkably improved.

This invention discloses a method for regulating early maturity of citrus fruits based on environmental data processing under plastic film mulching, specifically relating to the fields of agricultural data processing and technology extension services. It addresses the problem that existing early maturity regulation models, due to their singular optimization objective, neglect the microecological changes caused by plastic film mulching, leading to increased pest and disease risks. By acquiring soil temperature and humidity data after plastic film mulching, and based on a pre-established correlation model, the method determines the pathogen inhibition function imbalance index and carbon and nitrogen cycle disturbance intensity, characterizing soil microecological health, and assesses the risk level of pest and disease occurrence. Combined with sensitivity analysis results of the original agricultural operation plan, key agricultural operation parameters are adjusted in a targeted manner. The generated comprehensive regulation plan is serialized into a distributable and executable agricultural technology extension service package and promoted for implementation. This achieves end-to-end data processing from environmental data to microecological risk assessment to customized agronomic plan generation and services, improving the accuracy of agricultural technology extension.