8425 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 invention discloses a rice plant segmentation method and system based on view angle transformation and dense point clouds, and the method comprises the steps: scanning a rice field through employing an unmanned plane laser radar system, and obtaining the dense point clouds of a rice canopy; performing angle transformation processing on the dense point cloud by adopting an overlooking angle to obtain a two-dimensional image of the dense point cloud at the overlooking angle; calculating a homography matrix from the dense point cloud to the two-dimensional image, and establishing an accurate mapping relation between the three-dimensional point cloud and the two-dimensional image; for the two-dimensional image, carrying out segmentation marking on the rice plant by adopting a region marking method so as to realize accurate segmentation and individual marking of the rice plant on a two-dimensional plane; and endowing each point in the point cloud with a corresponding region mark number by using a mapping relationship between the three-dimensional point cloud and the two-dimensional image, and further obtaining a rice plant segmentation result of the whole dense point cloud. According to the method, high-precision rice plant segmentation is realized, and more reliable and efficient technical support is provided for precision agricultural management and phenotype analysis of rice.

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 relates to the technical field of agricultural irrigation, in particular to a rice water-saving intelligent irrigation decision-making method and system, and the method comprises the following steps: determining the basic information of a target rice field, and obtaining a rice intelligent water-saving irrigation path by using a random landmark method and an adaptive heuristic optimization algorithm; the water demand of rice in the target rice field is obtained in real time, and then a water demand prediction model is constructed based on CEEMD and LSTM; predicting the water demand of the target rice field by using a water demand prediction model, and obtaining rice water-saving irrigation parameters of each irrigation point on the rice intelligent water-saving irrigation path in combination with rainfall information and a rice growth stage; according to the rice water-saving irrigation parameters, the irrigation system is used for completing irrigation of the target rice field. According to the method, the accuracy and the intelligent degree of rice irrigation decision making can be improved, the complexity of rice irrigation decision making and the dependence on expert experience are reduced, and more optional schemes are provided for rice irrigation control.

The invention discloses a rice spectrum stress index construction method and system, computer equipment and a storage medium, and relates to the technical field of rice stress classification, and the method comprises the steps: regularly shooting different groups of rice leaves to obtain original hyperspectral images, and carrying out black and white correction; extracting regions of interest from rice leaves under different stresses, calculating average spectral data and performing SG smoothing filter processing; using a generative adversarial network to extract 50 characteristic wave bands with the highest contribution degree; inputting the average spectral data corresponding to the characteristic wave band as model input data into a random forest classification model to obtain a model classification result; and meanwhile, screening the first five characteristic wave bands and corresponding weights to construct spectral indexes, obtaining stress indexes, dividing stress categories, and outputting stress reasons of the rice. According to the method, the nonlinear relationship in the data is more efficiently captured, the classification model performance is improved, the calculation efficiency is high, the adaptability is high, the dimensionality of the hyperspectral data is greatly reduced, and the model calculation complexity is effectively reduced.

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 provides a rice seed vigor rapid detection method based on near infrared spectrum, and belongs to the technical field of spectral analysis. The method comprises the following steps: performing different degrees of aging treatment on rice seeds in a high-temperature and high-humidity environment, taking unaged rice seeds as a control group, collecting spectral data of the rice seeds subjected to different degrees of aging treatment, and preprocessing the data; taking out the seeds of each treatment group, and measuring physiological indexes including germination rate, germination potential and the like; based on the preprocessed spectral data, a data enhancement method is adopted to fully embody internal characteristics and category differences of sample data; a Conv1D-GRU framework is applied, the model can capture spatial features and time sequence information in spectral data at the same time, a full-connection layer predicts extracted comprehensive features by means of vitality, a classification result is output through a softmax layer, a hybrid optimization population search algorithm is used for further optimizing the performance of the model, and the performance of the model is improved. Therefore, the activity of the rice seeds can be rapidly detected according to different aging degrees of the rice seeds.

The invention discloses a rice disease and insect pest early warning method and system based on image recognition, and particularly relates to the technical field of insect pest recognition, different areas of a rice field are comprehensively covered through multi-source high-resolution image collection, it is ensured that the image quality reaches a preset threshold value, median filtering and histogram equalization are adopted in the image preprocessing step, and the image quality is improved. Based on a geographic information system and a Kriging interpolation method, a spatial heterogeneity model of rice field disease transmission is established, a deep convolutional neural network is used for carrying out feature extraction on the image, the type, position and severity of diseases and pests are accurately recognized, the extracted features are combined with the spatial heterogeneity model, and the accuracy of the disease and pest detection is improved. A spatial autocorrelation analysis method is adopted to deeply evaluate the distribution mode of diseases in the rice field, and a support vector machine algorithm is applied to perform classification evaluation on the disease and pest risk, so that the problems of early warning lag and missing report caused by disease spatial heterogeneity are effectively solved, and the accuracy and timeliness of early warning are improved.

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 belongs to the technical field of agricultural disease monitoring, prevention and control, and particularly relates to a rice blast monitoring and diagnosing method based on a mooring hot-air balloon and an unmanned aerial vehicle system. By constructing a dynamic observation platform of a tethered hot-air balloon and an unmanned aerial vehicle group, multi-variety, multi-temporal and multi-dimensional rice blast diseased plant multi-spectral / hyperspectral data and multi-information such as geography, soil, climate and field management are synchronously obtained, and a spectral database is constructed. An early diagnosis inversion model is constructed based on spectral characteristics of diseased rice plants and data characteristics of an unmanned aerial vehicle and a captive hot-air balloon by using self-supervised learning and deep learning technologies. According to the method, the cooperative advantages of the tethered hot-air balloon and the unmanned aerial vehicle are fully played, comprehensive acquisition and deep fusion of data are realized, the accuracy and timeliness of rice blast monitoring and diagnosis are improved, powerful technical support is provided for healthy development of the rice industry, and agricultural intelligence and sustainable development are assisted.

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.

According to the barley HvPHR1 gene and the application thereof in the aspect of increasing the phosphorus content of barley, rice PHR2 homologous protein serves as bait, homologous comparison means is utilized, it is found that two PHR proteins HvPHR1 and HvPHR2 exist in barley, and it is found that the content of inorganic phosphorus in barley leaves can be remarkably increased through overexpression of the HvPHR1 gene in an HvPHR1 overexpression strain with the normal phosphorus concentration being 200 micrometers NH4H2PO4 and an HvPHR1 overexpression strain with the low phosphorus concentration being 10 micrometers NH4H2PO4 hydroponic culture for three weeks and a wild type control (GP). In conclusion, the function of the HvPHR1 gene in barley low phosphorus stress is defined, and a theoretical basis is provided for cultivation of new phosphorus-efficient barley varieties.

The invention provides a rice disease identification method and system based on a CNN-CBAM-BiGRU combined model. According to the method, richer feature combinations are obtained by improving the CNN, the influence of dimension reduction on data is weakened by improving a CBAM attention mechanism, and information loss is avoided through BiGRU. The method can effectively overcome the defects that a traditional convolutional neural network lacks global attention to the context and a recurrent neural network lacks local attention. In an experiment, through comparison with classical models such as VGG16, LeNet, AlexNet, GoogleNet V1 and RestNet 50, a result shows that the recognition accuracy of the CNN-CBAM-BiGRU combined model provided by the invention on the four diseases is as high as 98.03%, and a reliable recognition method is provided for rice disease recognition research.

The invention discloses a microbial agent and a method for repairing cadmium-polluted rice field soil by using the microbial agent, relates to the technical field of repairing cadmium pollution of rice field soil, and solves the problem that the cadmium metal enrichment capacity of duckweed is limited due to the fact that the duckweed has certain endurance capacity to cadmium stress. The microbial agent comprises bacillus subtilis ATCC6633 and pseudomonas aeruginosa ATCC27853, and the mass ratio of the bacillus subtilis ATCC6633 to the pseudomonas aeruginosa ATCC27853 is 2: 1, and the method comprises the following steps: step 1, spreading the microbial agent on the surface layer of cadmium-polluted rice field soil, then mixing the microbial agent with the rice field soil, raking the rice field soil, adjusting the water-holding capacity of the rice field soil, applying a base fertilizer, and irrigating the rice field. According to the method, the cadmium tolerance of the duckweed can be promoted, so that the enrichment capacity of the duckweed on cadmium is promoted, the remediation capacity of the duckweed on cadmium-polluted soil is improved, the cadmium content in the soil can be reduced, and the content of metal cadmium in rice is also reduced through a competitive cadmium absorption way.

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 cold region saline-alkali soil rice growth assessment method and system based on image data fusion, and relates to the technical field of rice growth monitoring. According to the main technical scheme, the method comprises the steps of obtaining multi-source fusion image data, environment monitoring data and actually measured growth indexes corresponding to different growth stages of rice in a cold region saline-alkali soil region in a historical period; performing deep feature extraction on the multi-source fusion image data to obtain key image features; performing deep feature extraction on the environment monitoring data to obtain key environment features; and respectively processing the key image features and the key environment features corresponding to different growth stages in a historical period by using an attention mechanism, and constructing a growth evaluation model corresponding to the rice in the cold region saline-alkali soil region based on the processed key image features, the processed key environment features and actually measured growth indexes. The main purpose is to improve the accuracy of rice growth evaluation of the cold region saline-alkali soil.

The invention discloses a gene OsSK8 for regulating and controlling disease resistance of rice as well as an encoding protein and application of the gene OsSK8, the gene is the gene OsSK8, and the sequence of the gene OsSK8 is shown as SEQ ID NO. 1 or SEQ ID NO. 2. A novel rice sheath blight and bacterial blight resistance regulation gene OsSK8 is identified, the OsSK8 gene is subjected to overexpression and knockout, and after resistance identification, it is found that the overexpression of the OsSK8 gene can enhance the resistance of rice to sheath blight and bacterial blight, and the knockout of the gene can reduce the resistance of rice to sheath blight and bacterial blight. The OsSK8 gene positively regulates the resistance level of rice to sheath blight and bacterial blight, so that a new thought and direction are provided for breeding rice varieties with higher disease resistance, and meanwhile, a solid foundation is laid for further exploration in the field of rice disease-resistant breeding.

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 discloses application of an OsNLP3 gene, and relates to the technical field of plant genetic engineering, and an amino acid sequence coded by the OsNLP3 gene is shown as SEQ ID NO.2. By constructing an OsNLP3 gene overexpression plant and comparing the OsNLP3 gene overexpression plant with a wild type of the OsNLP3 gene overexpression plant, the increase of the single gene expression quantity of the OsNLP3 gene overexpression plant causes the significant increase of the total protein content of the rice, the invariable characters of the grain length, the grain width, the yield and the like, the genetic effect is significant, and the OsNLP3 gene overexpression plant has huge application potential and prospects for the improvement of the characters of rice varieties and provides a new gene resource for

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.