16268 results about "Agronomy" patented technology

The invention provides a soil fertility quality analysis method and system based on corn planting cultivated land, and relates to the technical field of soil quality analysis and evaluation.The soil fertility quality analysis method comprises the steps that a visible-near infrared spectrum image of a soil sample is collected, reflectivity data of a key wave band is extracted from the spectrum image, and a soil health index and a nutrient balance index are calculated; calculating a soil fertility quality comprehensive score for comprehensively evaluating the soil fertility according to the soil health index, the nutrient balance index and the soil moisture content; comparing the comprehensive score of the soil fertility quality with a preset threshold value, and evaluating the soil fertility; a machine learning model is introduced, historical soil data is trained and predicted, a quality analysis model is constructed based on a random forest algorithm, the trained quality analysis model is put into use, and new data is continuously learned to dynamically adjust and update, so that the soil detection process is simplified, and the soil fertility change can be monitored in real time. And agricultural managers are helped to make scientific decisions.

The embodiment of the invention provides a soil nutrient spectrum detection and regulation method and system, and belongs to the technical field of soil nutrient spectrum detection and regulation. The method comprises the following steps: collecting soil multispectral original data and environmental data, generating a calibration matrix and a nonlinear correction parameter, executing optical reflectivity conversion, and synchronously fusing a time domain reflectometry dielectric constant and a spectral moisture index to obtain fused moisture content; selecting a nutrient diagnosis characteristic wave band pair to obtain a moisture inhibition type spectral index; and predicting the soil nutrient content by using a pre-trained transfer learning model to obtain a soil nutrient content prediction value, and performing fertilization regulation and control based on the fertilization decision risk coefficient. According to the invention, extreme soil moisture measurement deviation is solved through a soil type adaptive moisture fusion technology; through multi-source risk quantitative decision and PID self-adaptive regulation and control, the fertilization amount can be dynamically optimized, execution safety is guaranteed, and a detection-decision-execution precision agriculture closed loop is formed.

Disclosed herein are methods, compositions, and kits for determining the presence or absence of a target polypeptide (e.g., a polypeptide involved in nitrogen fixation) in a sample (e.g., a sample of soil from the rhizosphere of a non-leguminous plant).

The invention discloses a soil organic matter prediction mapping method, device and equipment based on deep learning and a medium, and relates to the field of digital soil mapping, and the method comprises the steps: obtaining a remote sensing image of target region soil, and setting a preset number of soil sampling points; comprehensively acquiring environmental data indexes such as terrain factors, vegetation factors, climate factors and land utilization factors; constructing three-dimensional environment tensor data in a preset range of each sampling point, and inputting the three-dimensional environment tensor data into the soil organic matter content prediction model to obtain an organic matter content prediction value set of the soil sampling points in the target area; and generating a spatial distribution prediction map of the soil organic matter content of the target area by combining the prediction value set and the spatial coordinates of the soil sampling points, thereby improving the accuracy of soil organic matter prediction and the mapping efficiency, and providing technical support for soil resource management and agricultural sustainable development.

The invention relates to the field of agricultural fertilization, in particular to a soil carbon nitrogen ratio fused straw returning fertilization optimization method, which comprises the following steps of: performing standardization processing on farmland static characteristic data to obtain a farmland standardization characteristic vector and a characteristic standardization threshold value; performing coupling analysis on the straw returning amount and the soil carbon-nitrogen ratio in the farmland standardized feature vector set to obtain risk factors; performing path gradient integration on the risk potential energy field function to obtain a symmetric path cost gain factor; performing path cost gain correction on the Euclidean distance to obtain adaptive distance measurement; and fuzzy C-means clustering is performed on the farmland standardized feature vectors through adaptive distance measurement to obtain differentiated fertilization management partitions, so that the problem of inaccurate fertilization decision caused by incapability of identifying key feature nonlinear coupling risks in existing management partitions based on Euclidean distance is solved.

According to the disclosure, there is provided seed and plants of the corn variety designated CV955045. The disclosure thus relates to the plants, seeds, and tissue cultures of the variety CV955045, and to methods for producing a corn plant produced by crossing a corn plant of variety CV955045 with itself or with another corn plant, such as a plant of another variety. The disclosure further relates to corn seeds and plants produced by crossing plants of variety CV955045 with plants of another variety, such as another inbred line. The disclosure further relates to the inbred and hybrid genetic complements of plants of variety CV955045.

The invention relates to the field of soil spectrum analysis, in particular to a farmland soil environment detection method and system. The method comprises the following steps: firstly, analyzing fluctuation characteristics of near infrared spectrum curves of a plurality of soil samples in a preset window of a target wavelength to obtain undisturbed degrees of the near infrared spectrum curves in the target wavelength; fitting spectral data points of the near infrared spectral curve, and obtaining feature retention degrees of the extreme points on the fitted curve according to the difference of fitting absorbance of adjacent extreme points on the fitted curve, the absorbance of each wavelength and the difference of the fitting absorbance and the wavelength distribution of the extreme points on each fitted curve; filtering the spectral data points of the target curve based on the non-interference degree and the feature retention degree to obtain a filtering curve; according to the method, the interference information in the near infrared spectrum curve is removed, the existing key information is reserved as much as possible, and the precision of farmland soil environment detection is improved.

A rapid monitoring and discrimination method for drought conditions in summer maize based on chlorophyll content includes: 1) Obtaining multi-spectral imagery through UAV multi-payload low-altitude remote sensing technology and measuring chlorophyll content on the ground. Additionally, calculating vegetation indices including Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Renormalized Difference Vegetation Index (RENDVI). 2) Selecting vegetation indices and constructing regression equations with measured chlorophyll content during different growth stages. The regression equation with the highest correlation for each growth stage is chosen as the optimal model equation for that particular stage. 3) Using the optimal model equations to retrieve chlorophyll content for each period and determining thresholds for chlorophyll content across different drought levels through calibration. 4) Calculating the required vegetation indices from real-time multi-spectral imagery of the field under test, retrieving chlorophyll content, and comparing it with the established thresholds to assess the real-time drought level.

The invention relates to a mangrove forest vegetation carbon sink monitoring and metering method based on an unmanned aerial vehicle and an AI technology, and the method comprises the steps: constructing a mangrove forest recognition model of a U-Net improved model based on a PyTorch deep learning framework through the unmanned aerial vehicle AI technology, achieving the intelligent recognition and discrimination of various mangrove forest vegetations, and the area and number of regions thereof, and achieving the accuracy of 90% or above; through regular aerial photography monitoring of an unmanned aerial vehicle, in combination with growth parameters measured on site and variable parameters derived from aerial photography data of the unmanned aerial vehicle, an optimal fitting model of mangrove forest height crown breadth, overground biomass and unit leaf area based on aerial survey of the unmanned aerial vehicle is constructed, and the carbon reserve and carbon sequestration amount of mangrove forest region vegetation are accurately calculated. And comprehensively evaluating the mangrove forest carbon sequestration capability in the monitoring area. According to the application, intelligent, digital and comprehensive monitoring and evaluation of the mangrove forest wetland vegetation carbon sequestration capability are realized.

According to the disclosure, there is provided seed and plants of the corn variety designated CV960136. The disclosure thus relates to the plants, seeds, and tissue cultures of the variety CV960136, and to methods for producing a corn plant produced by crossing a corn plant of variety CV960136 with itself or with another corn plant, such as a plant of another variety. The disclosure further relates to corn seeds and plants produced by crossing plants of variety CV960136 with plants of another variety, such as another inbred line. The disclosure further relates to the inbred and hybrid genetic complements of plants of variety CV960136.

The invention discloses a microbiological culture scheme customization recommendation method and system based on soil data analysis, and relates to the technical field of microbiological culture plants.The method comprises the steps that soil samples at different positions and depths are collected, soil characteristic data are analyzed, and data processing is carried out; microbial community information is obtained through soil DNA extraction, PCR amplification and high-throughput sequencing, and distribution and functions of microorganisms are evaluated in combination with data preprocessing and diversity analysis; selecting proper microbial strains according to soil characteristics and microbial community analysis results; a microbial culture scheme is combined with agricultural management measures; the conditions of soil and microorganisms are monitored in real time, and a microorganism culture scheme is dynamically adjusted based on a feedback mechanism; through field trials and advanced performance evaluation indexes, the effect of the microbial culture scheme is verified for a long time. The method has high adaptability and sustainability, and customized microorganism management schemes can be provided in different agricultural environments.

According to the disclosure, there is provided seed and plants of the com variety designated CV644310. The disclosure thus relates to the plants, seeds, and tissue cultures of the variety CV644310, and to methods for producing a corn plant produced by crossing a corn plant of variety CV644310 with itself or with another corn plant, such as a plant of another variety. The disclosure further relates to corn seeds and plants produced by crossing plants of variety CV644310 with plants of another variety, such as another inbred line. The disclosure further relates to the inbred and hybrid genetic complements of plants of variety CV644310.

The compounds represented by formula (I) of this invention exhibit excellent activity against a variety of pests and mites in agriculture or other fields. Furthermore, these compounds achieve good control effects at very low doses, and therefore can be used to prepare insecticides and / or acaricides.

The invention provides a vehicle-mounted soil nutrient detection method and device based on soil spectral data classification, and relates to the technical field of soil detection.The method comprises the steps that soil near infrared spectral data of soil of a current site in a farmland area to be detected is collected through vehicle-mounted equipment; then obtaining first spectrum difference information and second spectrum difference information based on the soil near infrared spectrum data; further determining the similarity degree of the near infrared spectrum data of the soil; dividing the soil near infrared spectrum data into a plurality of different soil near infrared spectrum data subsets through a spectral clustering algorithm, and performing soil nutrient detection through a farmland soil nutrient detection model. According to the vehicle-mounted soil nutrient detection method based on soil spectral data classification, the total nitrogen content and the organic matter content of the soil can be accurately output in real time, and the detection effect of soil nutrient detection is improved.

The invention relates to the technical field of farmland fertilization management, and discloses an intelligent fertilization management method and system based on machine learning. The method comprises the steps that a soil parameter set and an environment parameter set of a target farmland are collected, soil parameters comprise soil humidity, nitrogen phosphorus and potassium content and pH value, and environment parameters comprise illumination intensity, temperature and rainfall; constructing a soil nutrient dynamic change model according to historical data, and predicting a soil nutrient consumption trend in a future preset period; generating an initial fertilization scheme based on the nutrient consumption trend and the crop growth stage characteristics; monitoring the growth state of crops in real time by using a multi-mode sensor, and obtaining a leaf surface color index, a stem height and a fruit development progress to form a growth state data set; and inputting the growth state data set and the initial fertilization scheme into a fertilization decision model, and comparing growth state deviation to adjust the nutrient distribution ratio to generate an optimized fertilization scheme.

The invention provides a soil nutrient detection method based on a gamma-ray energy spectrum and an XGboost algorithm, and the method comprises the steps: constructing a training sample for soil nutrient detection, the training sample comprises radioactive element data, and the label of the training sample is soil nutrient data with the same soil point location as the radioactive element data; the training sample is input into an XGboost model, the XGboost model extracts features of the training sample and predicts soil nutrients of the training sample, the soil nutrients serve as nutrient predicted values, a loss function is constructed according to the nutrient predicted values and labels of the training sample, the XGboost model is trained, and a soil nutrient detection model is obtained; acquiring soil radioactive elements of to-be-detected soil through a gamma-ray energy spectrometer, and inputting the soil radioactive elements into the soil nutrient detection model to obtain a nutrient detection result of the to-be-detected soil.

The invention discloses application of a citrus CsDREB gene in citrus plant height regulation and control and dwarf variety cultivation, and belongs to the technical field of genetic engineering breeding. The invention provides a gene-CsDREB gene which has a remarkable regulation effect on the plant height of citrus, overexpression of the CsDREB gene can remarkably reduce the plant height and internode length of citrus, compared with a wild type, the plant height of a transgenic plant can be averagely reduced by 67.4%, and the higher the relative expression quantity is, the more remarkable the reduction of the plant height is; the CsDREB gene provided by the invention can be used as a candidate gene for breeding and / or breeding dwarf citrus varieties by adopting a genetic engineering method, so that the labor cost of fruit farmers is reduced, the production efficiency is improved, and experimental evidence is provided for clarifying the molecular mechanism of dwarf citrus phenotypes.

The invention relates to bacillus velezensis and application thereof in preparation of a multifunctional straw decomposition agent, and belongs to the technical field of microorganisms. The bacillus velezensis has the functions of producing cellulase, producing xylanase, degrading abamectin, decomposing inorganic phosphorus and degrading straws; when straw is returned to the field, the multifunctional straw decomposition agent provided by the invention not only can accelerate the decomposition speed of straw, decompose abamectin residues in soil and improve the soil ecology, but also can promote the growth of pepper plants and enhance the stress resistance of the pepper plants, and has a good comprehensive application effect.

The invention discloses a corn germination image segmentation method based on an elite adaptive rime algorithm. Relates to the technical field of agricultural seed detection and image processing, in particular to the technical field of corn germination image segmentation based on an elite adaptive rime algorithm. According to the method, a dual-adaptive weight mechanism and an elite reselection strategy are introduced into a rime optimization algorithm, the convergence capability of the algorithm is enhanced, and multi-threshold segmentation is carried out on the corn kernel germination image in combination with the Kapur entropy. And the image segmentation precision is effectively improved. The method comprises the following steps: acquiring a corn germination image data set; drawing a two-dimensional histogram, and inputting the two-dimensional histogram into a Kapur entropy function to obtain an objective function fobj; an elite solution module is initialized; a dual-adaptive weight mechanism is added; a soft rime strategy and a hard rime strategy are improved; updating the elite solution after the rime search strategy module; and the target function fobj is input into a rime improvement algorithm, and an optimal threshold value is obtained.

The invention relates to the field of computer vision and agricultural automation, and discloses a grape cluster young fruit lightweight detection method based on improved YOLOv8n, and the method comprises the following steps: obtaining grape cluster young fruit image data, and generating a grape cluster young fruit data set; improvement is carried out based on a YOLOv8n model, and the method comprises the steps that a C2f-Faster module is adopted to replace a C2f module to reduce the floating point calculation amount and the number of parameters, depth separable convolution is used to reduce the complexity of the model, a context anchor attention module is introduced to enhance the extraction ability of remote context information, and a Wi-I oU loss function is adopted to improve the generalization ability of the model. Training and optimizing the improved model; and carrying out real-time detection on young fruits of the grape cluster by utilizing the trained model. According to the method, the detection precision of the young fruits of the grape clusters is effectively improved, the model complexity is reduced, efficient and lightweight real-time detection is realized, and the method is suitable for an automatic grape fruit thinning system.

The invention provides a black soil cultivated land organic carbon degradation diagnosis method based on geographic big data and AI, and the method comprises the steps: constructing a black soil area cultivated land organic carbon degradation potential influence factor data set and a diagnosis model, judging the spatial correlation of potential influence factors, training the diagnosis model based on the data set, and obtaining a diagnosis result of the black soil area cultivated land organic carbon degradation. And performing black soil area cultivated land organic carbon degradation influence factor identification based on the trained diagnosis model, and performing organic carbon degradation influence factor action mechanism analysis based on an identification result. According to the method, by utilizing high resolution and high timeliness of geographic space big data and cloud computing, multiple types of potential factors such as climate, soil, terrain and agricultural activities which influence organic carbon change of the cultivated land in the black soil area can be quickly calculated on a large-area scale, and manpower, material resources and time costs caused by large-area data collection and analysis are avoided; by using an artificial intelligence algorithm, the main control factor of organic carbon degradation of the cultivated land in the black soil area is accurately identified from multiple potential factors, and the current degradation situation can be objectively evaluated.

The invention discloses a complex microbial inoculant for coupling straw decomposition and soil carbon sequestration and a preparation method thereof, the complex microbial inoculant can rapidly promote degradation of lignocellulose of straw in soil by compounding bacillus subtilis, bacillus licheniformis, pseudomonas aeruginosa and the like, provides a basic material energy source for activity of soil microorganisms, and has the advantages of simple preparation process and low cost. Meanwhile, corresponding microorganisms achieve long-term storage of organic carbon through humus stabilization and mineral substance combined carbon (MAOC) stabilization, the storage efficiency of the organic carbon is remarkably improved through nano Fe3O4, and under the synergistic effect of all strains in the complex microbial inoculant, the carbon loss of straw returning can be effectively reduced, the carbon immobilization capacity of soil is improved, and the soil quality is improved. The organic carbon content in the soil is increased.

According to the disclosure, there is provided seed and plants of the corn variety designated CV962284. The disclosure thus relates to the plants, seeds, and tissue cultures of the variety CV962284, and to methods for producing a corn plant produced by crossing a corn plant of variety CV962284 with itself or with another corn plant, such as a plant of another variety. The disclosure further relates to corn seeds and plants produced by crossing plants of variety CV962284 with plants of another variety, such as another inbred line. The disclosure further relates to the inbred and hybrid genetic complements of plants of variety CV962284.

According to the disclosure, there is provided seed and plants of the corn variety designated CV978472. The disclosure thus relates to the plants, seeds, and tissue cultures of the variety CV978472, and to methods for producing a corn plant produced by crossing a corn plant of variety CV978472 with itself or with another corn plant, such as a plant of another variety. The disclosure further relates to corn seeds and plants produced by crossing plants of variety CV978472 with plants of another variety, such as another inbred line. The disclosure further relates to the inbred and hybrid genetic complements of plants of variety CV978472.

The invention relates to a seeding device and an air pressure type high-speed precision seed-metering device and a seed cleaning device and method thereof, the seeding device comprises the air pressure type high-speed precision seed-metering device, the seed-metering device comprises the seed cleaning device, the seed cleaning device comprises a seed cleaning mechanism, the seed cleaning mechanism comprises a seed cleaning rotating arm and a plurality of elastic wheels, one end of the seed cleaning rotating arm is pivoted with a shell of the seed-metering device, and the other end of the seed cleaning rotating arm is pivoted with the shell of the seed-metering device; the plurality of elastic wheels are sequentially mounted at the other end of the seed cleaning rotating arm, and the distances between the edges of the plurality of elastic wheels and the centers of the seed metering holes are respectively adjustable and are sequentially increased or decreased; the seed cleaning adjusting mechanism comprises a rotating motor and an adjusting arm, the rotating motor is mounted in the seed-metering device shell, and two ends of the adjusting arm are respectively connected with the seed cleaning rotating arm and a transmission shaft of the rotating motor; wherein the rotating motor is connected with the controller, and the controller controls the rotating motor to drive the adjusting arm to drive the seed cleaning rotating arm to adjust the distance between the edge of the elastic wheel and the center of a seed metering hole of the seed metering disc in real time according to a detected seed feeding signal so as to ensure normal seed metering. The invention further correspondingly provides a seed cleaning method.

The invention discloses talaromyces purpurogenus, a preparation method and application in prevention and treatment of corn stalk rot, and provides a novel strain, namely talaromyces purpurogenus Jnf41, greenhouse control effect experiments show that the talaromyces purpurogenus Jnf41 can inhibit the scab area and the scab length of the corn stalk rot in greenhouse control effect determination, and the talaromyces purpurogenus Jnf41 can be used for preventing and treating the corn stalk rot. Infection of the corn stalk rot can be effectively inhibited, pathogenic bacteria are not prone to generating drug resistance, green prevention and control are implemented in the prevention and control process, and the biological preparation for resisting the corn stalk rot is prepared and has important significance on green prevention and control of the corn stalk rot.