9367 results about "Crop" patented technology

The invention relates to the technical field of intelligent agriculture and ecological internet of things, in particular to an ecological irrigation decision dynamic optimization method and related equipment. Comprising the following steps: acquiring multi-source environment data, acquiring a soil profile humidity gradient in real time through a soil humidity sensor array, acquiring future rainfall probability distribution, a temperature change rate and a wind speed predicted value in combination with a weather forecast interface, and synchronously accessing a geographic information system to acquire terrain elevation and crop distribution data. The three technical bottlenecks of model dimension collapse, parameter estimation instability and optimization response lag in a traditional irrigation decision-making system are systematically solved by constructing a space-time coupling analysis framework and a closed-loop optimization mechanism of multi-source heterogeneous data.

Provided is an intelligent irrigation control method for rice fields based on a cloud service platform and a system thereof. Constructing a planting condition data set and a condition coefficient, monitoring the current water supply environment of crops, and constructing a water supply coefficient. If the water supply coefficient is lower than the water supply threshold, based on the current growth data of crops, a plurality of corresponding irrigation strategies are matched from the pre-constructed crop irrigation knowledge graph, and the target strategy is selected from a plurality of irrigation strategies; constructing an irrigation observation coefficient set, and constructing irrigation coefficient. If the irrigation coefficient is lower than the efficiency threshold, use the trained strategy optimization model to optimize the target strategy, and control the irrigation coefficient based on the optimized target strategy to complete the irrigation of crops in the irrigation area.

The invention discloses a water-saving control method for agricultural irrigation, a cloud platform and equipment, and relates to the technical field of irrigation water-saving control, and the method comprises the steps: determining the historical rainfall of the geographic position of a target farmland and the historical soil moisture content parameter of the target farmland; determining farmland water demand rule characteristics of the target farmland based on the historical rainfall and the historical soil moisture content parameters; constructing a farmland water-saving control model according to the farmland water demand rule characteristics; dividing a water demand area of the target farmland based on the crop information; based on the farmland water-saving control model and the soil moisture content parameter of each water demand area, obtaining a predicted irrigation value of each water demand area; acquiring a real-time irrigation value of each water demand area; and based on the predicted irrigation value and the real-time irrigation value, performing real-time regulation and control on a preset intelligent irrigation water valve in each water demand area. The problem that the utilization rate of water resources is low can be effectively solved.

The invention relates to the field of computer vision, in particular to a crop disease and insect pest real-time identification, prevention and control decision-making method and system based on multi-modal edge calculation. Comprising the following steps: acquiring an unmanned aerial vehicle multispectral image, an infrared thermal image and an NDVI index, performing preprocessing in combination with field Internet of Things sensing data, and generating a multi-modal feature input sequence; constructing a spectrum-environment fusion feature matrix on the basis of a Transform attention mechanism, and performing disease and pest detection by using a YOLOv7-Spectral model to generate a disease and pest distribution heat map; calculating a disease risk index (BRI) and a pesticide application priority map based on historical data; planning a pesticide application path of the unmanned aerial vehicle by adopting an ant colony optimization algorithm, and optimizing a pesticide application scheme in combination with wind speed and humidity parameters; the unmanned aerial vehicle performs precise pesticide application according to the optimized path and monitors the disease change trend; adjusting the model through pesticide application feedback data, and optimizing a disease and pest prevention and control strategy by adopting federal learning. According to the invention, the identification precision is improved, the pesticide use is reduced, and accurate, efficient and intelligent prevention and control are realized.

The invention provides a soybean planting fertilization management system, and relates to the technical field of management systems.Soil physicochemical properties, a meteorological microenvironment, a plant growth state and agricultural machinery operation data are collected in real time through a field sensor network and an unmanned aerial vehicle multispectral imaging technology, and a farmland dynamic portrait is constructed; the intelligent decision-making unit uses a deep learning model and a GLCM texture analysis technology to dynamically identify plant nutrient deficiency pathological characteristics, determines a nutrient deficiency type in combination with an element concentration threshold and triggers early warning; the scheme decision module dynamically optimizes the nitrogen-phosphorus-potassium ratio through a reinforcement learning algorithm, generates a field-level fertilization prescription map by using Kriging interpolation, recommends the optimal fertilization opportunity and dosage, and the precise execution module is linked with a variable fertilizer applicator to realize integrated precise application of water and fertilizer. The user interaction module supports remote monitoring and intervention of farmers through a GIS visual interface and a mobile terminal APP, can dynamically adapt to environmental changes and crop requirements, and significantly improves the nutrient utilization efficiency.

The invention relates to the technical field of automatic irrigation control, in particular to a multi-sensor fusion agricultural precise irrigation system which comprises a leaf area analysis module, an environment evaluation module, a task arrangement module, an execution regulation and control module and a feedback regulation module. According to the method, the crop leaf area index is introduced to be compared with the growth stage target value, the dynamic expression of the group moisture demand is realized, the environmental evapotranspiration influence factor is generated in combination with illumination and temperature disturbance parameters, the timeliness of water demand driving force identification is enhanced, and the task execution sequence is judged by adopting regional water pressure disturbance data; a multi-region irrigation resource allocation process is optimized, a water quantity and pressure difference double-factor adjustment mechanism is utilized, the rationality and response accuracy of time length allocation are improved, water supply state analysis and task target deviation comparison are performed, dynamic supplementary irrigation regulation and control are realized, the closed-loop capability and supplementary irrigation accuracy of irrigation period control are improved, and the irrigation period control efficiency is improved. And the stable allocation capability and the irrigation response efficiency of the irrigation system under the influence of multiple variables are enhanced.

The invention relates to the technical field of crop disease recognition, in particular to a crop early disease recognition method and system based on time sequence feature fusion, and the method comprises the steps: obtaining an RGB image and a temperature feature image of a target leaf, and collecting multispectral feature data through a spectral imaging device; based on the time sequence image sequence, generating a coordinate transformation matrix of the first frame image by using a PnP algorithm, and aligning subsequent images according to the coordinate transformation matrix; multi-modal features are extracted by using a deep neural network, and the deep neural network comprises a 3D convolutional layer and an adaptive attention mechanism and is used for extracting features of space and time dimensions; realizing cross-modal feature fusion through a graph convolutional network based on the spectral data and the multi-modal features; inputting the fused features into a classifier, and generating the disease category and the disease severity of the target leaf based on the output of the classifier; outputting the disease transmission risk and the early warning result, and capturing the tiny dynamic change in the disease development process.

The invention discloses an intelligent agricultural condition monitoring system and method for grain and oil crops, and relates to the technical field of agricultural condition monitoring. Farmland multi-source data are acquired and preprocessed in real time through a multi-source data acquisition module; multi-factor step-by-step fusion of farmland multi-source data is realized by using a D-S evidence theory through a multi-source data fusion module, and a multi-factor fusion evaluation index is formed; constructing a grain and oil crop environment-growth state comprehensive model based on LSTM through an environment state fusion module, and fusing the grain and oil crop environment-growth state comprehensive model with farmland multi-source data and multi-factor fusion evaluation indexes to obtain a farmland digital twin system; and a growth deviation index is analyzed and generated through the decision feedback optimization module and is fed back to the optimization system. The problems that in the prior art, data acquisition hysteresis is remarkable, multi-source heterogeneous information is isolated and difficult to fuse, and early warning fails due to lack of correlation between environment mutation and crop response are solved, and accurate monitoring and risk active intervention of grain and oil crops in the whole growth period are achieved.

The invention relates to the technical field of soil monitoring, in particular to a farmland irrigation management and control system based on the Internet of Things, and the system comprises a soil monitoring module, a crop water demand analysis module, an irrigation strategy making module, an irrigation execution control module, a water source management module, a fertilizer distribution module, an irrigation effect evaluation module and an adaptability adjustment module. According to the method, the crop water demand is analyzed by combining the decision tree, the water consumption is accurately calculated, the irrigation strategy optimizes the time and frequency based on the linear programming and simulated annealing method, the scheme adaptation degree is improved, the irrigation link adopts an automatic control and dynamic adjustment mechanism, the environment and crop demand changes are adapted in real time, and the irrigation efficiency is improved. Water source allocation ensures efficient distribution of multiple water sources on the basis of dynamic optimization, the type and quantity of fertilizer feeding are accurately controlled according to crop requirements, the irrigation effect is subjected to self-adaptive optimization of an irrigation plan after crop data evaluation, and accurate, flexible and efficient farmland resource management is integrally achieved.

A spatio-temporal variation monitoring method for SPAD of winter wheat, relating to the technical field of crop monitoring. The monitoring method comprises: a multispectral sensor of an unmanned aerial vehicle acquiring canopy images of winter wheat in a heading stage, a flowering stage, and a late grain-filling stage; extracting from multispectral images spectral features based on the canopy reflectance of winter wheat and texture features based on a graylevel co-occurrence matrix; using a feature selection policy to preferably select sensitive remote-sensing features; and applying a feature fusion policy and an SVR algorithm to construct a winter-wheat SPAD estimation model. Spectral features combining a near-infrared band with other bands can fully capture the spectral difference in SPAD of winter wheat in a reproductive growth stage, and texture features in a red-light band and the near-infrared band are relatively sensitive to the SPAD of winter wheat. The stability and estimation accuracy of an SPAD model constructed by applying a feature selection policy and a feature fusion policy are both superior to those of a model applying a feature policy alone and a model not applying any policy.

The invention provides an agricultural pest early warning method and system based on big data, and the method comprises the steps: firstly obtaining a crop leaf image set of a target farmland region from farmland image big data, and carrying out the leaf region segmentation of the crop leaf image set, so as to distinguish a healthy region from a potential lesion region; performing disease feature extraction on the potential lesion area image to generate a key disease feature set, performing abnormal state detection on the key disease feature set by using a pre-trained disease and pest recognition model, determining a disease and pest type and predicting a diffusion trend of the disease and pest type; and finally, based on the disease and insect pest type identification and the diffusion trend prediction data, generating a disease and insect pest early warning instruction containing geographic positioning information, and sending the instruction to a farmland management system to trigger prevention and control response operation, thereby realizing accurate monitoring and early warning of crop diseases and insect pests.

The present disclosure provides non-intergeneric remodeled microbes that are able to fix atmospheric nitrogen and deliver such to plants in a targeted, efficient, and environmentally sustainable manner. The utilization of the taught microbial products will enable farmers to realize more productive and predictable crop yields without the nutrient degradation, leaching, or toxic runoff associated with traditional synthetically derived nitrogen fertilizer. The remodeled microbes taught herein are able to be combined with leading agricultural chemistry and elite germplasm. Furthermore, the disclosure provides seed treatments comprising remodeled microbes.

Frog farming method and frog cage are provided. The method comprises: I, putting young frogs in the frog cage in spring; II, putting lure attracting mosquitoes into the frog cage into the frog cage in summer and autumn, thereafter the frogs feed on the mosquitoes; III, avoiding snakes entering the frog cage in the place where the snakes inhabit; IV, covering the frog cage with plastic film in winter, mounting an electric lamp in the frog cage, increasing the temperature of air in the frog cage to keep the frogs not hibernating and promote the frogs to grow; V, adding artificial lure if necessary. The frog cage includes a fixed frog cage and a vehicle-mounted frog cage, both including inner tiers, wherein a water basin, a terrace and a stairway communicating the upper and lower tiers are arranged on each tier, and the frog cage is covered with a mesh enclosure. The frog framing method and frog cage have the advantages that the frogs are farmed in multi-tier cages, lures are used outdoors to attract insects, pests are eliminated for crops, natural ecology is balanced, and food sources are also provided for human; cage framing of frogs is very low in cost; frogs farmed have high reproductive rate, high disease resistance and high feed conversion capacity.

Disclosed herein are engineered rhizobia having nif clusters that enable the fixation of nitrogen under free-living conditions, as well as ammonium and oxygen tolerant nitrogen fixation under free-living conditions. Also provided are methods for producing nitrogen for consumption by a cereal crop using these engineered rhizobia.

The invention relates to the technical field of agricultural intelligent decision making, and discloses an agricultural intelligent analysis and decision making method based on multi-modal data, which comprises a multi-modal agent, an agent collaborative decision making technology, an intelligent decision making method and a crop identification and diagnosis method. The multi-modal intelligent agent comprises an intelligent agent framework and multi-modal capability construction; the intelligent agent collaborative decision-making technology comprises intelligent agent ecological construction, a land parcel analysis method and a land parcel economy evaluation model; the intelligent decision-making method comprises the steps of agricultural knowledge graph construction, understanding and decision making. The crop identification and diagnosis method comprises vision-language model preprocessing and intelligent identification and diagnosis. According to the agricultural intelligent analysis and decision-making method based on the multi-modal data, the technical problem in traditional agricultural decision-making is solved through processing, analysis and technical innovation of the decision-making process of agricultural multi-source data, and accurate analysis and scientific decision-making support is provided for all stages of agricultural production before production, during production and after production.

The invention provides a high-standard farmland water and fertilizer integrated intelligent regulation and control method and system, and the method comprises the steps: obtaining terrain point cloud data and crop canopy water stress data of a high-standard farmland, generating an elevation gradient distribution feature set, and generating an elevation model according to the set; analyzing the crop canopy moisture stress data, and calculating a moisture stress index; performing spatial position matching on the terrain difference parameters and the micro-terrain difference parameters to generate a terrain compensation parameter set; establishing a time sequence optimization correlation model in the 5G edge computing node, and optimizing a pulse irrigation time sequence factor and a flow distribution weight by using the time sequence optimization correlation model according to the terrain compensation parameter set and the historical irrigation record so as to generate a multi-target irrigation control instruction; performing high-standard farmland water and fertilizer integrated intelligent regulation and control according to the multi-target irrigation control instruction and the water and fertilizer migration rate parameter corresponding to the microtopography difference parameter; water and fertilizer are accurately distributed according to needs, and the irrigation uniformity, the resource utilization efficiency and the crop yield are improved.

The invention discloses a farmland environment multi-parameter intelligent monitoring system and method based on Internet of Things, particularly relates to the technical field of agricultural Internet of Things monitoring, and is used for solving the problem of stress resistance evaluation distortion caused by neglecting crop physiological recovery hysteresis in the prior art. Collecting soil water content data in real time through an Internet of Things sensor, and extracting a soil water content change rate when a preset threshold value is reached; secondly, constructing a space-time gradient matrix based on different soil layer water deficit rates, and generating a stress cumulative intensity index through principal component analysis and information entropy fusion; then analyzing a coupling relationship between the soil moisture change rate and the stress accumulation effect in the historical stress event on physiological recovery delay to generate a recovery delay coefficient; the physiological state lag degree is evaluated by combining the stomatal conductance change rate ratio of the stress period and the recovery period and the recovery delay coefficient; and finally, dynamically prolonging the irrigation execution time according to the lag degree until the irrigation execution time is lower than a set tolerance value, thereby remarkably improving the reliability of crop stress resistance test data.

The invention relates to the field of agricultural robots, and discloses an agricultural spraying robot system with visual navigation, and the system comprises a multi-mode sensing module, a dynamic environment modeling module, a time sequence prediction module, a multi-target path planning module, a spraying control module, and a hardware acceleration unit. The method comprises the steps that a dynamic semantic map is constructed through multi-modal perception, agricultural semantics and an attention mechanism are fused to predict an obstacle trajectory, a safe path is generated in combination with multi-objective optimization, the spraying amount is adjusted based on crop density, and real-time operation is ensured by means of hardware acceleration. According to the method, a dynamic semantic map is constructed through multi-modal perception, agricultural semantics is combined to enhance trajectory prediction, a safe path is generated by utilizing multi-objective optimization, the spraying amount is adaptively adjusted according to the crop density, and real-time control is realized by adopting heterogeneous hardware acceleration; the defects of a traditional agricultural robot in the aspects of navigation precision, obstacle avoidance safety, resource utilization and response speed are overcome, and the intelligent level of complex farmland operation is improved.

The invention discloses a soil nutrient recycling method and system based on prediction driving, and belongs to the technical field of agricultural soil management.The method specifically comprises the steps that soil nutrient content data are obtained through sampling, and soil nutrient abnormal areas are preliminarily screened in combination with a geographic information system and a remote sensing technology; soil physicochemical properties are measured in the soil nutrient abnormal area, soil nutrient form distribution is measured through X-ray diffraction and near infrared spectroscopic analysis, and nutrient stability and release potential are evaluated; constructing a soil nutrient cyclic dynamic model based on multi-source data, and simulating nutrient evolution; according to simulation results, crop requirements, soil states and environmental factors, an accurate fertilization scheme is formulated; intelligent management and precise fertilization of soil nutrients are realized, and the fertilizer utilization rate and the agricultural production efficiency are improved.

The invention discloses an intelligent field management system based on an Internet of Things platform, and relates to the technical field of Internet of Things, and the intelligent field management system comprises a multi-source data fusion and analysis module, a waste recovery and resource utilization module, a knowledge graph and question and answer module, and a self-adaptive decision and regulation module. The multi-source data fusion and analysis module comprises a data acquisition sub-module and a data analysis and preprocessing sub-module; the data acquisition sub-module comprises a multi-source heterogeneous data acquisition unit and a real-time dynamic data acquisition unit; the multi-source heterogeneous data acquisition unit uses a wireless sensing technology, a GPS positioning technology, a sensor calibration algorithm and a data interpolation algorithm to acquire meteorological, soil, crop physiology and biological field data. By arranging the multi-source data fusion and analysis module, the problems that traditional agricultural data is difficult to obtain and not deep in analysis, precision agricultural development is difficult to support, and agricultural resources are wasted and crop yield and quality are unstable due to data quality problems can be solved.

The invention provides a soil sensor and fertilization execution mechanism linkage adjustment method of an intelligent grape deep fertilizer applicator, and relates to the technical field of fertilization execution mechanism linkage. Comprising the steps of performing weighted fusion based on obtained soil data to obtain soil comprehensive parameters, generating a fertilization amount instruction, a fertilization proportion instruction and a fertilization depth instruction through an adaptive optimization algorithm based on the obtained soil comprehensive parameters, and sending the obtained fertilization amount instruction, the fertilization proportion instruction and the fertilization depth instruction to a control module to be executed based on the obtained fertilization amount instruction, the obtained fertilization proportion instruction and the obtained fertilization depth instruction. Through a self-adaptive optimization algorithm, the technical scheme not only dynamically adjusts the fertilization amount, proportion and depth according to real-time soil data, but also can predict the soil change trend according to different environmental factors and perform optimization adjustment, and automatically supplements or reduces the fertilizer according to different soil requirements; therefore, the crops can obtain optimal nutrient supply in different growth stages, the fertilization effect is optimized, and the growth quality and yield of the crops are improved.

The invention relates to the technical field of land resource monitoring, in particular to a land resource dynamic monitoring and early warning method and system based on multi-source remote sensing data fusion, and the method comprises the steps: employing an unmanned plane to periodically collect optical images, SAR echoes and LiDAR point clouds, constructing a ground three-dimensional digital model, and carrying out the land parcel division; performing fusion to form a multi-dimensional feature vector, establishing an LSTM land parcel feature evolution model, and predicting a change rate interval of each feature in a current period based on a historical sequence; constructing a time sequence difference change detection algorithm, calculating a land parcel change rate, and screening potential abnormal land parcels by taking a prediction interval as an anomaly judgment threshold value; a double-branch convolutional neural network is adopted to identify crop states, growth stages and construction violation behaviors, abnormity is judged and determined, and confidence is given; spatial clustering is carried out on determined abnormal land parcels, accurate boundaries are obtained in combination with a three-dimensional model, multi-level early warning information is generated, and the decision-making efficiency and response speed of land resource monitoring are improved.

The invention specifically relates to the technical field of intelligent irrigation, and discloses a meteorological prediction-based irrigation dynamic regulation and control system, which comprises a data acquisition module, a data analysis module, a strategy generation module, a strategy execution module, a feedback regulation module and a man-machine interaction module, the data acquisition module integrates a water quantity acquisition terminal, a crop state acquisition terminal and a meteorological monitoring terminal, acquires multi-source irrigation decision data of a target irrigation farmland in real time, judges a water shortage risk level of the target irrigation farmland through the data analysis module, and generates an irrigation strategy through the strategy generation module; the strategy execution module executes an irrigation strategy through an electrically-driven intelligent water-saving valve, the feedback adjustment module dynamically adjusts the irrigation strategy based on sensor feedback data, and the man-machine interaction module provides a data visual interface, dynamically optimizes the irrigation strategy in combination with real-time meteorological changes and irrigation feedback, divides water shortage risk levels, and provides a water-saving management system. The utilization efficiency of water resources is effectively improved, and the applicability and reliability of the system are improved.

An adjustment system for guidance lines comprising a GNSS unit disposed on an agricultural vehicle, a memory comprising stored crop row locations, a processor in communication with the GNSS unit and the memory, the processor configured to apply lateral and / or longitudinal adjustments to guidance lines generated based on stored crop row locations from a prior operation, and an automatic steering system configured to command the agricultural vehicle to traverse the guidance lines.

The present disclosure provides non-intergeneric remodeled microbes that are able to fix atmospheric nitrogen and deliver such to plants in a targeted, efficient, and environmentally sustainable manner. The utilization of the taught microbial products will enable farmers to realize more productive and predictable crop yields without the nutrient degradation, leaching, or toxic runoff associated with traditional synthetically derived nitrogen fertilizer. The remodeled microbes taught herein are able to be combined with leading agricultural chemistry and elite germplasm. Furthermore, the disclosure provides seed treatments comprising remodeled microbes.

The invention provides a farmland soil moisture content prediction and irrigation method, relates to the technical field of irrigation, and aims to solve the technical problem of poor soil moisture content prediction and irrigation strategy effects in related technologies. The method comprises the following steps: acquiring multi-source data, wherein the multi-source data comprises one or more of soil sensor data, remote sensing data, meteorological data and crop parameters; acquiring a first soil moisture content based on the multi-source data, wherein the first soil moisture content is used for indicating the content of moisture in the current soil; predicting a second soil moisture content according to the first soil moisture content and the multi-source data, wherein the second soil moisture content is used for indicating the content of moisture in the soil in a certain period of time in the future; when the second soil moisture content is smaller than the preset soil moisture content, an irrigation instruction is generated based on the second soil moisture content and the target soil moisture content, the farmland irrigation method performs irrigation according to the irrigation instruction, and the irrigation instruction comprises irrigation single time and irrigation interval time.

The invention relates to the technical field of biology, in particular to a verticillium dahliae virulence gene, a verticillium dahliae virulence protein and application. The invention discloses a verticillium dahliae virulence gene VdPHO23like. The verticillium dahliae virulence gene VdPHO23like comprises a polynucleotide sequence for coding an amino acid sequence of SEQ ID NO: 3. According to the invention, expression of the gene is inhibited in a targeted manner through an RNA interference mediated gene silencing technology so as to weaken virulence of pathogenic bacteria, and an efficient, specific and environment-friendly comprehensive prevention and control strategy is provided for verticillium wilt of crops such as cotton.

The invention relates to a water and fertilizer management control method based on image data processing, and the method comprises the steps: collecting a visible light image, a multispectral image and a thermal infrared image of a crop, carrying out the feature extraction, calculating the plant morphological parameters, vegetation indexes and vegetation canopy temperature distribution of the crop, creating a three-dimensional physiological feature matrix, creating an image segmentation network model, and carrying out the feature extraction. Inputting the visible light image, the multispectral image, the thermal infrared image and the three-dimensional physiological feature matrix into an image segmentation network model to segment plant organs, quantifying organ-level phenotypic parameters, and establishing a water and fertilizer decision model based on reinforcement learning; and taking real-time data of the three-dimensional physiological feature matrix, the organ-level phenotypic parameters and the soil specified parameters as state input, taking a water and fertilizer proportioning scheme as an action space, and implementing a water and fertilizer management scheme through a dynamic optimization control strategy. The technical problems that data integration of a single sensor is insufficient, phenotype analysis precision is limited, and adaptability of a decision model is poor in a traditional water and fertilizer regulation scheme are solved.

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.