6679 results about "Oryza sativa" patented technology

The invention relates to the technical field of crop yield analysis, in particular to a crop yield prediction method based on a time sequence remote sensing image, and the method comprises the steps: obtaining an optical remote sensing image and a synthetic aperture radar image in a crop growth period, and constructing a time sequence image through complementary information fusion and under-cloud information recovery; the problems of cloud interference and data missing are solved, a multi-scale time regulation and control convolution module is used for processing a time sequence image, multi-scale time sequence features are extracted, feature fusion is carried out in combination with attention difference jump connection, and a rice distribution map is obtained; determining a rice planting area based on the rice distribution diagram, selecting an optimal assimilation time period in the rice planting area, and assimilating the leaf area index subjected to remote sensing inversion and a crop growth model simulation result to generate a simulated yield after assimilation; based on the multi-scale time sequence characteristics, a parameter dynamic response matrix related to crop growth model parameters is established, and residual error correction is performed on assimilated simulation yield, so that the rice yield prediction precision is improved.

The invention relates to the technical field of machine vision technology and agricultural disease and insect pest diagnosis, in particular to a rice disease and insect pest identification method and system based on the machine vision technology, and the method comprises the following steps: S1, obtaining the polarization direction distribution of reflected light on the surface of a leaf, and generating a polarized light compensation image; s2, performing feature layer fusion to generate a multispectral fusion image; s3, based on the polarization phase difference value of each pixel point in the multispectral fusion image, segmenting an independent leaf area; s4, performing multi-dimensional feature extraction on the segmented leaf area; s5, performing Euclidean distance calculation, and determining a nearest neighbor matching result; and S6, according to a feature similarity threshold value in a matching result, determining the disease and pest species. According to the invention, through fusion of multi-source spectral information and structured feature comparison, high-precision identification of rice diseases and insect pests and scab space positioning are realized, and the intelligent level and practicability of disease diagnosis are improved.

The present invention relates to the technical field of pesticides, and discloses a biological agent-based rice sheath blight disease efficient control agent, which comprises an active flora, a carrier, an auxiliary agent and a disease resistance enhancing factor, the active flora is used for combining humidity, pH and temperature response type materials so as to make the bacterial agent dynamically released, and the disease resistance enhancing factor is used for controlling the disease resistance of the rice sheath blight disease. The carrier is used for enabling the active ingredients to be slowly released in a field environment and keep stability, the auxiliary additive is used for improving the activity, adhesiveness and soil adaptability of the fungicide, and the disease-resistant enhancing factor is used for expanding and improving the adaptability of the control agent. The ratio of the active flora to the carrier to the auxiliary additive to the disease-resistant enhancing factor is 10: 50: 13: 27. Through the synergistic effect of various functional strains and the combination of the dynamic release carrier, the occurrence and propagation of the rice sheath blight disease are effectively inhibited, the prevention and treatment rate can reach 75-85%, and the prevention and treatment effect is obviously higher than that of a traditional chemical pesticide or a single fungicide.

The present application relates to a kind of deletion mutant nucleic acid and its application in anti-herbicide.The deletion mutation occurs in the promoter of rice OsHPPD Gene, the length of deletion sequence is at least 10 bp, and the A in the start codon ATG of gene is 0, at least the bases located in the upstream of start codon ATG from 2041 to 2032 are deleted. OsHPPD The present application finds that by deleting part of the sequence in the promoter of rice OsHPPD Gene, rice can obtain resistance to HPPD inhibitor herbicides, which is of great application value for ensuring agricultural production safety and improving the efficiency of herbicide use.

The invention discloses an artificial intelligence rice water and fertilizer real-time monitoring method and system, and relates to the technical field of agricultural intelligent decision making, and the method comprises the steps: inputting a farmland feature data set into a soil thermodynamic diagram generation model, carrying out the high-resolution reconstruction of the farmland feature data set through a GAN adversarial network, and generating a whole-field high-precision soil thermodynamic diagram; based on the whole-field high-precision soil thermodynamic diagram, a collision relation between the fertilization amount and historical farming data is detected according to an FCL causal algorithm, a preliminary causal diagram is generated, and a causal diagram structure of the fertilization amount and the historical farming data is constructed by adopting a multiple linear regression method; based on a causal diagram structure, the multi-order causal effect of the fertilization amount, the soil parameters and the historical yield is analyzed through a dynamic allocation algorithm, and a water and fertilizer regulation and control strategy is formulated in combination with a multi-objective optimization algorithm. According to the method, the soil thermodynamic diagram generation model is constructed, so that the fuzzy problem of the edge of the field and the salinization area is solved, a high-fidelity soil space state substrate is provided for water and fertilizer regulation and control, and invalid irrigation is reduced.

The invention discloses a sky-ground integrated rice growth vigor monitoring method, and relates to the technical field of remote sensing, and the method comprises the steps: carrying out the modeling of each growth stage of rice, and forming a rice growth model diagram; collecting data to form a rice remote sensing big data image library; training a deep learning feature extractor, and extracting the features of the rice image; estimating rice growth based on remote sensing image big data; estimating rice growth based on the enhanced vegetation index; and carrying out multi-scale cross validation on the accurate identification degree of the rice growth vigor, and finally forming a set of sky-ground integrated rice growth vigor monitoring method. According to the method, a deep learning algorithm and a vegetation enhancement index method are adopted, the growth vigor estimation is performed on remote sensing data of different sources, and the rice growth vigor estimation result is determined through cross validation, so that high-precision rice growth state monitoring is realized; the three remote sensing technologies complement one another to form a comprehensive and multi-angle rice growth monitoring method, and real-time and efficient farmland monitoring management can be achieved.

The invention belongs to the technical field of microorganisms, particularly relates to rice endophytic bacillus velezensis AN6 and application thereof, and particularly provides bacillus velezensis AN6 which is classified and named as bacillus velezensis and has the preservation number of CCTCC (China Center for Type Culture Collection) NO: M 20231886. The invention provides a novel rice endophytic bacillus velezensis strain AN6, through separation, screening, identification and experimental verification, it is found that the strain shows broad-spectrum resistance to various phytopathogens including rice bacterial leaf blight, rice blast and the like, the strain has the capacity of inhibiting various phytopathogens and has the capacity of adsorbing heavy metal, and the strain has the advantages of being capable of inhibiting various phytopathogens and capable of being used as a novel rice endophytic bacillus velezensis strain AN6. The method is of great significance to biological control of rice diseases and development of green agriculture.

The invention relates to the technical field of crop saline-alkaline tolerance analysis, in particular to a rice saline-alkaline tolerance prediction method and system based on big data analysis, and the method comprises the following steps: collecting soil environment data, rice physiological phenotype data and historical stress response data, and constructing a multi-dimensional data cube by using a space-time alignment engine; quantization feature analysis is adopted to carry out quantum probability modeling on Na < + > / K < + > ion transmembrane transport and PSII photosynthetic exciton transfer features, and a quantum probability cloud chart of rice physiological response is generated. Based on a geodesic line domain adaptation model of geodesic line mapping, a soil conductivity thermodynamic diagram and a quantum probability cloud chart are fused, a rice saline-alkaline tolerance adaptability prediction model is established, finally, a three-dimensional saline-alkaline tolerance performance prediction map is output, and the adaptability of rice varieties in specific saline-alkaline land is visually displayed. According to the method, the regional generalization ability of saline-alkali tolerant varieties is effectively improved, the limitation of a traditional statistical model is broken through, and a scientific basis is provided for saline-alkali tolerant rice breeding and cultivation and saline-alkali soil agricultural optimization management.

The invention relates to the field of rice gene engineering, and particularly provides OsSULTR2; the invention also discloses application of the 2 protein and the coding gene thereof in regulating and controlling the salt tolerance of rice. The protein meets the following conditions: B1) a protein with an amino acid sequence of SEQ ID NO.1; and B2) a fusion protein with the same function obtained by connecting a tag to the N end and / or C end of B1). The method is used for detecting OsSULTR2; the salt stress phenotype identification in the seedling stage is carried out on the transgenic rice with the gene knockout 1, 2, and the result shows that when the gene segment is deleted, the salt stress tolerance of the rice is improved, and the function and the application way of the gene are proved. Therefore, the OsSULTR2 of the present invention; the 2 protein and the coding gene thereof can regulate and control the salt tolerance of rice, and have important significance for cultivating salt-tolerant transgenic rice.

The invention relates to the technical field of intelligent irrigation, in particular to an agricultural water-saving irrigation intelligent regulation and control system which comprises a growth stage recognition module, a moisture monitoring module, a meteorological data synchronization module, an irrigation strategy scheduling module and a feedback correction module. According to the method, high-precision identification of crop stage states, improvement of stage judgment accuracy, screening of key growth period blocks, focusing of regulation and control targets, improvement of resource utilization efficiency, layered acquisition and trend comparison of humidity data and construction of a deep and shallow layer dynamic response structure are realized through time sequence analysis of rice key growth parameter amplification; the method comprises the following steps of: enhancing moisture change resolution, synchronously fitting a humidity rhythm and weather prediction, improving regulation and control perspectiveness, eliminating a rainfall interference window, combining with a water demand gear, realizing block precise irrigation, feeding back a regulation and control result through double indexes of humidity and leaf chromatic aberration, constructing set mapping, forming a closed-loop regulation and control mechanism, and enhancing adaptability and execution precision.

The invention relates to a transplanting position quality evaluation method based on image analysis and geometric modeling, in particular to a transplanting position quality evaluation method based on image analysis and geometric modeling. The invention aims to solve the problems of low precision, single evaluation index and lack of space positioning capability of the existing unmanned aerial vehicle remote sensing image rice transplanting position estimation. The method comprises the steps that S1, an unmanned aerial vehicle collects an RGB image data set of unmarked farmland seedling positions; s2, obtaining an RGB image data set marked with farmland seedling positions; s3, obtaining a pre-trained encoder; s4, obtaining a trained farmland rice seedling target detection model; s5, inputting to-be-detected RGB image data into the trained farmland rice seedling target detection model, and outputting farmland seedling positions in the to-be-detected RGB image data; s6, obtaining the transplanting spacing, judging whether seedlings are missing or not, and if not, ending; if not, estimating a seedling missing position; s7, the seedling missing position in the step S6 is converted into a geographic coordinate system. The method is used in the field of transplanting position estimation.

The invention discloses a rice field irrigation drainage strategy dynamic optimization method and system. The method comprises the steps of collecting environmental parameters of a target rice field in a current decision period; determining a to-be-optimized target of the target rice field, and constructing a multi-target optimization model in combination with the environmental parameters; performing dynamic planning solution on the multi-objective optimization model by adopting an intelligent algorithm to obtain a real-time optimized irrigation drainage strategy of the current decision period; implementing a real-time optimized irrigation drainage strategy of the current decision period; monitoring whether the final period of the rice growth period is reached when the current decision-making period is ended or not: otherwise, acquiring actual environment parameters when the decision-making period is ended, correcting the state vector at the moment, updating the initial state vector of the next decision-making period of the target rice field through the transfer function, and repeating the solving and implementation process of the irrigation and drainage strategy optimized in real time; if yes, the circulation is ended, and the optimal irrigation drainage strategy in the whole growth period of the target rice field is obtained.

The invention discloses a rice nitrogen response regulation and control network analysis and breeding target identification system and method based on multi-omics data. According to the system, organic combination of regulation and control network construction based on single or multiple varieties of materials, key transcription factor recognition and accurate positioning of regulation and control areas where transcription factors play roles is achieved through an expression-chromatin accessibility correlation research method, and cis-trans effect distinguishing of the regulation and control areas is achieved through a deep learning model. The method comprises the following steps: carrying out nitrogen starvation pretreatment on rice, then carrying out nitrogen resupply, collecting a root sample, and carrying out ATAC-seq and RNA-seq sequencing; an eCAAS method is adopted to construct a regulation and control network, and key transcription factors are identified and accurately positioned; the chromatin accessibility difference of different varieties is predicted through a deep learning model, the cis-action effect and the trans-action effect are distinguished, an upstream transcription factor target is provided for genes dominated by the trans-effect, and haplotype and editable regulatory region targets available for direct breeding are provided for genes dominated by the cis-effect.

The invention provides an intelligent agricultural management method and system based on image processing, and the method comprises the steps: carrying out the rice-fish symbiotic region network deployment of a rice-fish symbiotic region, carrying out the multi-source data collection of rice, fish and environment based on the rice-fish symbiotic region network, obtaining the rice-fish symbiotic ecological data, carrying out the data preprocessing of the rice-fish symbiotic ecological data, and carrying out the data processing of the rice-fish symbiotic ecological data. Constructing a rice-fish data heterogeneous graph based on the rice-fish symbiotic ecological data after data preprocessing, constructing a rice-fish attention network model, importing the rice-fish data heterogeneous graph into the rice-fish attention network model, performing cross-domain feature fusion on the rice-fish symbiotic ecological data based on the rice-fish attention network model, and obtaining rice-fish associated data. And then performing rice-fish causal reasoning based on the rice-fish association data, generating a rice-fish association knowledge graph, establishing a rice-fish decision model, importing the rice-fish association knowledge graph into the rice-fish decision model, and generating a rice-fish regulation and control decision. Through the method, an accurate and efficient intelligent dynamic regulation and control decision is provided for a user.

The invention relates to the field of biotechnology and plant breeding, and particularly provides a method for fixing rice heterosis by using a DMP gene. Aiming at the problems of low maturing rate and complex operation of the existing apomixis system, the haploid induction functions of the rice OsDMP8 and OsDMP13 genes are verified for the first time, the OsPAIR1, OsREC8, OsOSD1 and OsDMP genes are synchronously knocked out through a CRISPR / Cas9 technology, a'MiMe-OsDMP 'four-gene editing system is constructed, and a new thought is provided for construction of the rice apomixis system. A new solution is provided for fixing heterosis of rice apomixis, the efficiency and quality of agricultural production can be greatly improved, and the method has high economic value and wide application prospects.

The invention relates to a deleted mutant nucleic acid and application thereof in herbicide resistance. The deletion mutation occurs in a promoter of a rice OsHPPD gene, the length of a sequence of a deleted nucleic acid is at least 25 bp, A in an initiation codon ATG of the OsHPPD gene is taken as a 0 site, and at least bases at 2299 to 2275 sites on the upstream of the initiation codon ATG are deleted. According to the application disclosed by the invention, the rice can obtain resistance to HPPD inhibitor herbicides by deleting part of sequences in the promoter of the rice OsHPPD gene, so that the application has great application values in guaranteeing agricultural production safety and improving the use efficiency of the herbicides.

The invention discloses a lightweight rice leaf disease identification method based on YOLO target detection. The method comprises the following steps: S1, inputting a rice leaf disease image; s2, carrying out super-resolution reconstruction on the image by using an improved Real-ESRGAN algorithm; s3, inputting the picture into an improved YOLOv8n target detection algorithm; s4, obtaining disease spot category and position information; and S5, visualizing the information on the image. The invention relates to the technical field of target detection, and has the beneficial effects that image super-resolution reconstruction is carried out aiming at the problems that a rice leaf scab target is relatively small and an image acquired in real time is relatively fuzzy, so that the resolution of the small target is improved, and the definition and texture features are improved. On the basis of a super-division model Real-ESRGAN, a group of residual dense modules containing five layers of cavity convolution layers are designed to help the network to acquire receptive fields and information of different scales.

The invention discloses a method for efficiently screening saline-alkaline tolerant rice. According to the method, controllable indoor saline-alkali environment simulation conditions are constructed, various salts are mixed in proportion to prepare solutions with different salinity gradients, the pH value is adjusted, and temperature, humidity and illumination parameters are accurately controlled; the method comprises the following steps: disinfecting rice seeds, carrying out a germination test, transplanting rice seedlings, and regularly measuring growth indexes; in the treatment process, physiological indexes such as malondialdehyde content, antioxidant enzyme activity, proline content and the like and the expression level of salt and alkali tolerance related genes are detected, key indexes are screened by applying statistics and multivariate analysis methods, and an evaluation system is established for comprehensive score screening. The method can quickly perform saline-alkali stress treatment on a large number of rice seeds or seedlings, is not limited by natural environment, shortens the screening period, can accurately simulate different saline-alkali soil environments, scientifically evaluates the saline-alkali tolerance of rice from multiple dimensions, and provides an efficient and reliable technical means for breeding saline-alkali tolerant rice varieties.

The invention provides a rice growth model construction method and system based on salt stress, and relates to the technical field of growth model.The rice growth model construction method comprises the steps that firstly, monitoring points are arranged in a planting sample area, and soil conductivity data of different depths are obtained through a multi-depth soil conductivity sensor and a Kriging interpolation method; collecting root system density, a multispectral remote sensing image and a leaf area index, and calculating a canopy salt stress index; measuring the concentration of related ions to obtain a sodium adsorption ratio, and establishing a salt stress coefficient by combining the soil conductivity and the irrigation volume; fusing canopy and soil stress indexes to generate a stress comprehensive index, and introducing a salinity feedback item to represent the crop compensation capability; and finally, constructing a hybrid machine learning model by adopting a convolutional neural network and a long-short-term memory network to realize rice growth dynamic prediction. According to the method, multi-dimensional salinity stress quantification of a soil-crop system is realized, and decision support is provided for salinization treatment in precision agriculture.

The invention discloses a remote monitoring and management rice seedling raising irrigation plant protection system, computer equipment and a medium, and relates to the technical field of rice seedling raising irrigation, the remote monitoring and management rice seedling raising irrigation plant protection system comprises a sensing acquisition module, an edge processing module, a data fusion module, a prediction modeling module, an execution control module, a user interaction module and an energy safety component; the sensing acquisition module is used for acquiring soil humidity, temperature, pH value, air temperature and humidity, illumination intensity, wind speed and direction, rainfall and crop image information through multiple types of sensors deployed in a rice seedling raising field, and outputting original environment data; the edge processing module is connected with the sensing acquisition module and is used for preprocessing the original environment data, generating preprocessed data and uploading the preprocessed data to a cloud server through an NB-IoT protocol; and the data fusion module is deployed in the cloud server and is used for receiving the preprocessed data from the edge processing module.

Disclosed is a rice and wheat nitrogen nutrition multispectral diagnosis method for precise fertilization by unmanned aerial vehicles (UAVs), including: step 1, acquiring multispectral UAV remote sensing images and collecting agronomic parameters of rice and wheat, and establishing a nitrogen nutrition spectral diagnosis model; and step 2, calculating a critical nitrogen concentration in aboveground parts of rice and wheat based on a critical nitrogen concentration dilution model for amount of dry matter, and further obtaining a critical nitrogen uptake; calculating an actual nitrogen uptake in the aboveground parts of rice and wheat based on the nitrogen nutrition spectral diagnosis model; and obtaining a recommended nitrogen topdressing amount for rice and wheat during corresponding stages based on the critical nitrogen uptake and the actual nitrogen uptake. The present disclosure ensures stable yields of rice and wheat while reducing input of chemical fertilizers and labor, presenting a promising application prospect.

The invention provides a rice endophyte for promoting rice growth and reducing cadmium as well as a preparation method and application of the rice endophyte, and relates to the field of environmental microbiology and agricultural microorganisms. The rice endophyte is stenotrophomonas, is named as Stentrophomonas sp.R5, is preserved in the China Center for Type Culture Collection, the address is Wuhan University, Wuhan, China, the postcode is 430072, the preservation number is CCTCC NO: M 20231758, and the preservation date is September 20, 2023. The rice endophyte not only has the capabilities of producing IAA, dissolving phosphorus, producing siderophores and the like for promoting the growth and development of rice, but also has an efficient inhibition effect on absorption and transportation of cadmium in the rice, particularly, the cadmium reduction amplitude of mature rice grains reaches up to 57.69%, and the rice endophyte has little influence on microbial communities in a soil environment, and has a good application prospect. Excessive interference is not caused, and conversion of available nutrients in the soil is promoted.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention relates to the technical field of target detection, in particular to a rice disease and insect pest target detection algorithm based on Mamba and YOLOv8, which comprises the following steps: acquiring a rice field RGB image and preprocessing to generate an abnormal image; inputting the abnormal image into a MedVSSM module, extracting global long-distance dependency features through a Mama state space model, extracting local features such as a polypide contour and scab texture in combination with CNN, and fusing and outputting multi-scale features; performing cross-layer semantic enhancement and path aggregation through a C2F-MedVSSM module, and generating an optimized feature map through dynamic weight adjustment; and the YOLOv8 module outputs a target detection frame, a category and confidence, and a final detection result is obtained after non-maximum suppression processing. According to the method, the Mama global modeling capability and the CNN local feature extraction advantages are fused, feature complementation is realized through a dynamic weight mechanism, the detection precision under the scenes of complex illumination, leaf shielding and the like is effectively improved, and the method has a practical application value for rice disease and pest prevention and control.

The invention belongs to the technical field of pathogenic microorganism detection, and particularly relates to a sequence combination for detecting cochliobolus miyabeanus based on RPA-CRISPR / Cas12b and application of the sequence combination. The CRISPR / Cas system contained in the composition comprises Cas12b and sgRNA, and the CRISPR / Cas system can be cooperatively used with a specific reaction system. The rapid detection method comprises the following steps: sample pretreatment: treating a sample to extract nucleic acid; performing amplification reaction: performing RPA amplification reaction on the extracted nucleic acid by using the composition, and applying a nucleic acid amplification product to CRISPR / Cas system detection; result interpretation: reading and judging a detection result through a quality control line and a detection line. The composition and the kit provided by the invention are specially used for detecting the cochliobolus miyabeanus. The method is simple and convenient to operate, rapid in detection, short in time consumption, low in cost and suitable for on-site rapid detection.

The invention discloses a crop agronomic parameter and yield estimation method and system coupling a crop growth model and remote sensing data, and relates to the technical field of agronomic parameter and yield estimation.The method comprises the steps that crop growth data are collected, a CERES-Rice model is constructed according to the crop growth data, and rice agronomic parameters and yield data are output; a PROSAIL canopy reflection model is constructed; hyperspectral data are output through a PROSAIL canopy reflection model, and parameter inversion optimization is carried out in combination with remote sensing data; and outputting final rice agronomic parameters and yield data according to the model after inversion optimization. The remote sensing radiation transmission model and the crop growth model are adopted to monitor agricultural parameters and yield of crops, the adjustment of parameters directly influencing crop states shared between ecological physiology and the radiation transmission model is emphasized, the availability of a hyperspectral instrument is increased, narrow-band spectral reflectivity data is collected, and the measurement accuracy is improved. And the accuracy of agricultural parameter and yield monitoring is improved.

The invention relates to a deleted mutant nucleic acid and application thereof in herbicide resistance. The deletion mutation occurs in a promoter of the rice OsHPPD gene, the length of a deletion sequence is at least 10 bp, A in an initiation codon ATG of the OsHPPD gene is taken as a 0 site, and at least 2041-2032 sites of basic groups located on the upstream of the initiation codon ATG are deleted. According to the application disclosed by the invention, the rice can obtain resistance to HPPD inhibitor herbicides by deleting part of sequences in the promoter of the rice OsHPPD gene, so that the application has great application values in guaranteeing agricultural production safety and improving the use efficiency of the herbicides.

The invention discloses a rice leaf tip small target detection and counting method based on global information enhancement, and the method comprises the steps: firstly building a field rice leaf tip image data set as a basis, and constructing an improved YOLO-DCL model; the core innovation of the method lies in that a global context information enhancement module, an inverted residual-cascade grouping attention module and a re-parameterization shared convolution detection head are introduced, key feature expression is enhanced and detection efficiency is optimized through effective fusion of global information, the recognition precision and processing speed of the model on a tiny rice leaf tip target are significantly improved, and the method is suitable for large-scale popularization and application. After training parameters are set, an optimization model is trained, then system performance verification and module optimization are carried out on the model, and a version with the optimal rice leaf tip detection performance is screened out. The technical bottlenecks that small target leaf tips are prone to missing detection and false detection and insufficient in real-time performance under the complex farmland background are effectively overcome, and reliable and efficient technical support is provided for rice growth early-stage yield prediction.