424 results about "Rice growth" patented technology

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 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 discloses bacillus velezensis capable of tolerating various environmental pollution and promoting growth and application of the bacillus velezensis. A plant endophyte bacillus velezensis L381 strain is identified in wild rice, and the strain is high in environmental adaptability, can grow in the environment with the temperature of 15-40 DEG C and the pH value of 5-8, and can tolerate the NaCl concentration of 4%; the rice growth promoting agent has a good growth promoting function, can secrete auxin IAA, solubilize phosphorus, dissolve potassium and secrete siderophores, and can effectively promote rice growth. Meanwhile, the strain has stress resistance to environmental stress such as heavy metals and antibiotics, and tetracycline can be degraded; the strain has a wide antibacterial spectrum, and can be used as a biocontrol bacterium for preventing and treating various plant diseases. The plant endophyte L381 can adapt to variable temperature conditions and variable soil environments in the natural environment, has very strong practical application potential, not only improves the functions of bacillus velezensis, but also widens the application range of the strain.

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 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 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 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.

The invention relates to a rice growth process three-dimensional scene construction method based on a crop growth model and a rendering engine, and the method comprises the following steps: obtaining plant density, distribution, leaf inclination angle distribution, a leaf spectrum, a canopy spectrum and substrate reflectivity through a field experiment, and constructing a single-plant rice 3D model; coupling a crop growth model, and describing vegetation canopy reflection characteristics through canopy structure parameters and leaf BRDF to simulate canopy reflection factors; respectively calculating a water body scattering phase function and a water surface BRDF according to the water body biological optical model and the wave water surface reflection model; generating a three-dimensional water body volume model through an AVRM model; blender software is combined with a LuxCoreRender rendering engine to couple incident radiation, vegetation canopy reflection factors, water optical attributes and other related input parameters, and a real rice scene is constructed. According to the method, the high-precision three-dimensional rice scene model is established, important technical support is provided for improving the precision and efficiency of rice remote sensing radiation modeling and further for agricultural remote sensing and wetland monitoring, and the method has popularization and application prospects.

The invention discloses a fertilization and topdressing intelligent decision-making system and method based on rice open space sensing data. The method comprises the following steps: acquiring agricultural condition data through a multi-source data acquisition module formed by an unmanned aerial vehicle multispectral camera, a ground sensor network and a meteorological station; generating a standardized data set by adopting a space-time fusion algorithm; the CNN-LSTM hybrid model is used for realizing rice growth prediction, disease, pest and weed identification and maturity evaluation; dynamically generating a base fertilizer / topdressing prescription map, a pesticide application scheme and an agricultural machine path plan in combination with an agricultural rule base; and the intelligent agricultural machine is linked through the Internet of Things interface to execute accurate operation. Millimeter-level sensing of the farmland state, variable control over fertilizer application and pesticide application and autonomous execution of agricultural machine operation are achieved, and compared with a traditional mode, the fertilizer utilization rate is increased by 35% or above, the pesticide application amount is reduced by 40%, and the comprehensive yield is increased by 15-20%.

The invention discloses a regulation and control method and system for rice production, relates to the technical field of agricultural production and intelligent control, is used for solving the problems of low production efficiency, uneven crop growth and unstable quality, and calculates a light-temperature matching coefficient through real-time monitoring of a root activity index and a leaf surface transpiration amount in combination with illumination intensity and canopy temperature difference. The nutrient absorption capacity of the rice is evaluated by detecting the rhizosphere conductivity change and the soil nutrient content, when the rice growth state is poor and the resource regulation capacity is high, an enhanced regulation and control mechanism is automatically entered, and regulation and control increasing levels are set according to the current rice canopy carbon dioxide exchange rate and the current air flow disturbance index; the resource supply coefficient is calculated and the regulation rate is determined through regulation and control level increase, so that the resource supply is optimized, and the agricultural production efficiency and the resource utilization rate are improved.

The invention discloses a rice growth period identification method and system based on edge calculation, and the method specifically comprises the following steps: S1, data collection: collecting various data related to a rice growth period through a multi-mode sensor disposed in a farmland, and the data comprises image data, meteorological data and soil data; according to the method, a deep learning model combining a multi-scale convolutional neural network (MSCNN) and a long short-term memory network (LSTM) is utilized, multi-source data such as images, weather and soil are fused, the rice growth period is recognized in real time, data processing is performed on edge nodes, data transmission time delay is reduced, and real-time recognition and feedback are achieved; by fusing data from different sources, more comprehensive rice growth information is obtained, and the accuracy of growth period identification is improved; and image features of different scales are extracted by adopting a multi-scale convolution kernel, and modeling is performed on time series data such as weather and the like in combination with an LSTM model, so that the generalization ability and the recognition precision of the model are further improved.

The invention provides a rice planting area identification method and device based on multi-temporal remote sensing data, equipment and a storage medium. The method comprises the following steps: acquiring the multi-temporal remote sensing data according to a rice phenological period; determining a spectral index sequence of the multi-temporal remote sensing data to construct a spectral index time sequence curve, and generating planting characteristic data in the target area according to a spectral change curve; determining a spectrum time sequence template matched with the rice growth rule according to the planting characteristic data, performing matching analysis on the planting mode of each land block, and marking a land block set of rice planting mode characteristics; performing cross comparison on the plot set and the rice planting reference sample to generate a rice planting mode sample set, and constructing a rice planting area identification model based on the rice planting mode sample set; and analyzing the remote sensing data of the land parcels to be identified in the research area according to the rice planting area identification model, and determining a rice planting area identification result. According to the scheme, the accuracy of rice identification can be effectively improved.

The invention discloses a fertilization recommendation method combined with a rice seedling raising fertilizer carrying technology in the technical field of agricultural production. The fertilization recommendation method comprises the following steps: S1, acquiring geology and seedling raising states; s2, presetting rice growth requirements; s3, calculating to obtain an additional nutrient amount; s4, dividing the growth cycle of the rice; s5, recommending the fertilization amount according to stages; according to the method, nutrient supply of seedling raising fertilizer is brought into a whole-growth-period fertilization plan, and accurate matching is achieved; on the premise that the yield is not affected, the fertilizing amount in the early and whole growth periods of rice is remarkably reduced, and the fertilizer utilization rate is increased; through dynamic matching of the early-stage fertilizer loading and supplying amount and the middle and later-stage fertilizer needing amount, resource waste caused by excessive fertilization is avoided, and the fertilizer cost is reduced; the nutrient proportion is scientifically distributed according to the nutrient absorption efficiency of each stage, and the fertilizer utilization rate is increased; the final yield can be improved; soil fertility indexes and historical data are combined, the profit and loss trend of soil nutrients is recognized, and cyclic utilization of the soil nutrients is achieved through dynamic matching of the sustained and controlled release fertilizer and the organic fertilizer.

The invention relates to a method for improving cadmium stress resistance of rice by using carbon quantum dots (CDs), which solves the technical problem that rice growth is affected due to the fact that rice is deeply poisoned by cadmium in the prior art by spraying the carbon quantum dots on leaf surfaces of the rice or irrigating the carbon quantum dots into root soil of the rice. CDs are used as a leaf surface resistance and control agent to treat cadmium-stressed rice, and it is found that on the premise that growth and development of the rice are not affected, CDs can improve the stress resistance of the rice by promoting photosynthesis of the rice and can stimulate a rice defense system to generate more antioxidant enzymes to cope with oxidative damage caused by Cd stress. Besides, CDs can also reduce the concentration of DTPA-Cd in soil and reduce accumulation of Cd on the overground part so as to further reduce the toxicity of Cd to rice, and meanwhile, the Cd content of the overground part of the rice is reduced so as to be beneficial to reducing accumulation of Cd in grains, so that CDs can be used as a leaf surface resistance and control agent to effectively relieve cadmium toxic rice, thereby promoting growth and development of the rice and improving the yield.

The invention relates to the field of artificial intelligence, in particular to a rice growth vigor dynamic monitoring method and system based on modal fusion and deep learning. The method comprises the following steps: for a plurality of target regions in the same administrative division, acquiring multi-dimensional agricultural data related to rice growth vigor in each target region; performing space-time fusion on the multi-dimensional agricultural data of the plurality of target areas to obtain a linkage monitoring map; performing rice growth prediction on each target area in the linkage monitoring map through a DSW Transform model to obtain an independent growth prediction result of each target area; a correlation weight is dynamically calculated through a deep learning model of a cross-modal attention mechanism in combination with geographic data, meteorological data and a linkage monitoring map, and rice growth prediction is performed on a rice block formed by linkage of each target area based on the correlation weight to obtain a comprehensive growth prediction result; and pushing rice growth information in each target area to the user according to the independent growth prediction result and the comprehensive growth prediction result.

The invention provides a rice variable rate fertilization method and device, electronic equipment and a storage medium, and relates to the technical field of agricultural intelligent information process.The method comprises the steps that non-rice plant interferents in a multispectral remote sensing image are removed, and a rice plant canopy mask is generated; based on the rice plant canopy mask, multi-dimensional features of the rice plant canopy are extracted and fused, and a fused growth feature vector is obtained; grading the rice growth vigor according to the fused growth vigor feature vector through a growth vigor grade prediction model to obtain a predicted growth vigor grade; performing standardization processing on the predicted growth vigor level to obtain a standardized growth vigor level; determining a nitrogen fertilizer adjustment factor based on the standardized growth vigor grade; and according to the nitrogen fertilizer adjustment factor, adjusting a standard nitrogen application amount to obtain a recommended nitrogen application amount. According to the invention, accurate recommendation of the nitrogen application amount in the rice planting process can be realized, so that accurate fertilization is realized.

The invention discloses a microbial agent and application thereof in preventing and treating rice blast and promoting rice growth, active ingredients of the microbial agent are formed by compounding cold bacillus with the preservation number of CGMCC (China General Microbiological Culture Collection Center) NO: 33492 and bacillus velezensis with the preservation number of CGMCC NO: 33493, and the microbial agent aims at solving the synergistic problem of preventing and treating rice blast and promoting rice growth. According to the complex microbial inoculant, through the synergistic interaction between the two strains, the metabolite of the complex microbial inoculant can completely inhibit the spore germination of magnaporthe oryzae; meanwhile, the fungicide can degrade cellulose, obviously promote the growth of plant height, fresh weight and dry weight of rice, and realize the integration of disease prevention and growth promotion. The invention further discloses a preparation method of the fungicide and an application method of the fungicide for preventing and treating rice blast and promoting growth in a root irrigation mode in the three-leaf stage of rice. The microbial agent disclosed by the invention has dual functions of efficiently preventing diseases and stably promoting growth, and an efficient and safe biological solution is provided for green and sustainable development of agriculture.

The invention discloses a rice growth period distinguishing method and an intelligent irrigation control device. Belongs to the technical field of crossing of intelligent agriculture and irrigation and water conservancy engineering and particularly relates to the technical field of rice growth period judgment and intelligent irrigation. The technical problem that an accurate and effective rice growth period judgment method for guiding intelligent rice irrigation is lacked at present is solved. The method specifically comprises the following steps: calculating a rice growth process index through an improved accumulated temperature model; and constructing a multi-factor coupling growth period judgment method, and judging the current rice growth period through the rice growth process index. The system comprises a sensing unit, a judgment and decision-making unit, an electric execution unit and an input and power supply unit, the judgment and decision-making unit receives the environment data of the rice field and the water layer depth data of the rice field, judges the current growth period of the rice according to a rice growth period judgment method, calculates the current water layer deviation and generates a control instruction to the electric execution unit.

The invention relates to the field of rice growth vigor monitoring, and discloses a rice phenological period growth vigor monitoring system and method based on an unmanned aerial vehicle, and the system comprises a rice data collection module which divides a rice field into a plurality of subareas, and carries out the image collection of each subarea through a sensor carried by the unmanned aerial vehicle; the rice image processing module is used for transmitting the collected image to a ground station and storing the collected image, and performing preprocessing operations of cutting, size adjustment and edge cutting on the collected image by using an image processing tool to obtain an image in a standard format; the rice data analysis module analyzes the preprocessed standard image, calculates a crop evaluation coefficient of each partition, and evaluates the growth vigor of the crops according to the crop evaluation coefficients; and the coping strategy module is used for judging whether the rice in the current subarea has the risk of agricultural disasters or not according to the evaluation result, and performing corresponding fertilization, irrigation or pest control according to the types of the agricultural disasters.

The invention relates to the technical field of agricultural intelligent prediction, and discloses a fertilization decision-making system fusing soil and rice growth vigor information data acquisition and analysis, which is characterized in that a microcontroller controls an irrigation valve to apply an irrigation pulse with a preset duration, and a soil humidity sensor is used to monitor a time interval when moisture falls back from a peak value to a baseline; according to the method, irrigation behaviors are reconstructed into an active diagnostic tool, traditional chemical index detection is replaced by physical process dynamic monitoring, lag adjustment is converted into a pre-prediction mode, meanwhile, acoustic verification and a differential correction mechanism are combined, and the crop transpiration rate is predicted. And an anti-environmental interference accurate decision-making system is constructed on the basis of low-cost hardware.

The invention provides an efficient reduction remediation method for cadmium pollution of rice field soil, and relates to the technical field of soil remediation. The efficient decrement remediation method for rice field soil cadmium pollution comprises the steps that high-cadmium-absorption-accumulation rice is planted in a target rice field; after the cadmium high-absorption accumulation rice turns green in the seedling stage, a deep water management process is conducted on the target rice field till the cadmium high-absorption accumulation rice is mature; the deep water management process comprises the step of repeatedly and periodically carrying out dry-wet alternate treatment and field drying on the target rice field; and harvesting the rice with high cadmium absorption and accumulation and separating from the field. According to the remediation method, on the premise that the structure and attributes of the rice field are not changed, according to the water requirement rule of the rice growth cycle, deep water fine management (water management cycle, frequency, strength and parameter control) is adopted, soil cadmium is activated, rice high absorption and accumulation of cadmium are enhanced, and efficient decrement remediation of soil cadmium pollution of the rice field is achieved.

The invention belongs to the technical field of agriculture, and particularly relates to a method for promoting growth and development of rice based on illumination and nitrogen. One of the purposes of the invention is to provide a method for promoting growth and development of rice based on illumination and nitrogen. The method comprises the following steps: providing cultivation conditions of illumination intensity of 600-800 [mu] mol.m <-2 >. S <-1 > and nitrogen concentration of 5.51-8.11 mM for rice in a tillering stage of the rice. By accurately applying a specific combination of illumination intensity and nitrogen concentration in the tillering stage of rice, efficient loading and long-distance transportation of photosynthetic products (especially cane sugar and the like) in phloem are promoted, and the photosynthetic products are guided to be more effectively and directionally distributed to developed library organs (such as fruits, grains or root systems); therefore, the transfer and circulation efficiency of carbon-nitrogen substances in plants is improved synergistically on the whole, the waste phenomenon of nitrogen redundancy or local lack and ineffective accumulation of photosynthetic products is reduced, and finally, an important physiological regulation basis is provided for remarkably improving the yield and quality of crops.

The invention discloses an application of an OsST1 gene in regulating and controlling the salt tolerance of rice. Belongs to the technical field of plant genetic engineering. The invention provides the application of the OsST1 gene in regulation and control of the salt tolerance of the rice, the effect of the OsST1 gene in the salt tolerance of the rice is proved for the first time, and research finds that overexpression of the OsST1 gene can improve the salt tolerance of a rice plant and improve the survival rate of the rice plant under salt stress, which indicates that the OsST1 gene has the effect of regulating and controlling the salt tolerance of the plant, and the application of the OsST1 gene in the regulation and control of the salt tolerance of the rice plant. The adverse effect of soil salinization on rice growth in practical application is solved, and meanwhile, biological function verification of the gene also has important reference significance for rice salt tolerance molecular mechanism research.

The invention discloses a rice yield estimation method for optimizing an RF model through an improved whale optimization algorithm, and belongs to the technical field of rice yield estimation. The problem that in the prior art, a traditional rice yield prediction method is low in prediction result precision is solved. The method comprises the following steps: acquiring multispectral image data of rice in different growth periods by using an unmanned aerial vehicle, and measuring and calculating rice growth parameters and average yield after a rice mature period; performing normalization preprocessing on the multispectral image data, and constructing a sample data set; the whale optimization algorithm is improved, the improved whale optimization algorithm is constructed to optimize the RF model, model parameters are set, a WOA-RF model is obtained, the sample data set is input into the WOA-RF model for training and screening to obtain an optimal rice yield estimation model, and the rice yield of the target area is estimated. According to the method, the unmanned aerial vehicle remote sensing image and the rice growth parameters are combined, the rice yield estimation precision is effectively improved, and the method can be applied to rice yield prediction.

The invention relates to the field of intelligent agriculture, in particular to a high-salt-tolerance rice planting management system based on image recognition, which comprises a data acquisition module, an image processing module, a growth condition detection module and an analysis management module, through the computer vision technology, close planting degree guidance and saline-alkaline degree control are intelligently provided for high-salt-tolerance rice planting, the management efficiency is improved, and the rice yield and quality are improved; according to the method, a Transformer coding method based on a window mechanism and global optimization is adopted, the recognition accuracy of the growth state of a rice plant is improved, the accuracy of analysis of the relationship between the growth condition of the rice and the salt and alkali content and the close planting degree of the rice is improved, and accurate guidance is provided for increasing the yield and quality of the rice; according to the method, a UNet network based on Mama block optimization is adopted, the influence of a field background on rice image feature extraction is removed, and the accuracy of rice growth condition-saline and alkaline content-rice close planting degree relation analysis is improved.

The invention discloses a rice early recognition method and device based on remote sensing data and a time sequence model. The method comprises the following steps: S1, acquiring multi-source remote sensing data and auxiliary data; s2, preprocessing the multi-source remote sensing data and the auxiliary data; s3, constructing a sample set based on the preprocessed multi-source remote sensing data and auxiliary data; s4, constructing a DualMama model, and training the DualMama model on the basis of the sample set; and S5, inputting the multi-source remote sensing data and the auxiliary data obtained in the step S1 into the trained DualMama model to obtain a rice early recognition distribution diagram. According to the method, through the proposed DualMama model, the utilization efficiency of multi-source remote sensing data is remarkably improved, the problem of inconsistent feature dimensions is solved, the early recognition precision of the rice is greatly improved under the complex hill terrain, the F1 score of the rice category can reach 90% within two months before the rice growth period, and accurate early recognition of the rice is achieved.

The invention provides a rice yield growth season high-precision prediction method based on a total primary productivity remote sensing product. The method comprises the following steps: firstly, calculating rice total primary productivity GPP and a normalized difference red-edge vegetation index NDRE in a rice growth season on a remote sensing cloud platform based on a time sequence Sentinel-2 image; then, respectively carrying out correlation analysis on the yield by utilizing GPP and NDRE of monthly-scale rice; performing harmonic fitting on the time sequence GPP based on a harmonic analysis technology, and determining an optimal time window in a growth season for rice yield prediction at an interval of 10 days; the rice yield is estimated by utilizing transfer learning and GPP and NDRE of the optimal time window, so that the advantages of GPP in yield estimation are clear; and an inter-annual GPP correction method suitable for different ecological points is constructed, so that the yield estimation precision of independent years is improved. According to the method, the yield of the rice in the field can be accurately estimated in seasons in a regional range, and accurate management of the rice can be promoted by farmers and agricultural technology promoters.

The invention discloses an application of an AvRRM1 gene in regulation and control of endophytic fungi teratosis to promote rice growth, and belongs to the technical field of gene engineering. The nucleotide sequence of the AvRRM1 gene is shown as SEQ ID NO.1. The application comprises the following steps: down-regulating expression of the AvRRM1 gene in the teratodontia by utilizing a biological technical means, and improving the growth promotion effect of the teratodontia on rice. The invention discloses the gene AvRRM1 for regulating and controlling rice growth in rice endophytic fungi teratosis for the first time, functional verification on the gene shows that the strain has more obvious growth promoting property on rice by knocking out the gene AvRRM1, and the research result has huge application potential in sustainable agriculture. The invention provides a feasible scheme for promoting the growth of the rice and increasing the yield of the rice.

The invention relates to a rice character parameter inversion method combining an optical-microwave collaborative model and a variational auto-encoder. The method comprises the following steps: acquiring multiband optical reflectivity, a microwave backscattering coefficient and observation geometric information of a research area; inputting the data into a multi-mode encoder, and encoding the data into probability distribution of a potential space; an optical and microwave physical model is used as a differentiable decoder, potential variables sampled from a potential space are received, and remote sensing data are simulated and generated; a self-supervised learning normal form is adopted, and end-to-end training is carried out on the whole frame by minimizing a composite loss function; a trained encoder is used as a rapid inversion model, vegetation biological physicochemical parameters are extracted from new remote sensing data, rapid inversion of the parameters is achieved, and uncertainty of the parameters is quantified. The rapid and accurate vegetation character parameter inversion method is established by fusing multi-modal data, a physical model and a deep learning technology, important reference is provided for rice growth monitoring and precision agriculture, and the method has popularization prospects.