78 results about "Winter wheat" patented technology

The invention discloses a winter wheat LAI and SPAD estimation method based on a lightweight semi-supervised model, and relates to the technical field of agricultural remote sensing monitoring, and the method comprises the steps: obtaining multispectral image data of a winter wheat key growth period, and carrying out the preprocessing of the multispectral image data to generate a standardized multichannel vegetation index image; the method comprises the following steps: constructing a lightweight semi-supervised model MCVI-SANet, and carrying out self-supervised training on the MCVI-SANet by adopting a semi-supervised training strategy driven by VICReg; and inputting the multi-channel vegetation index image into the trained MCVI-SANet, and outputting quantitative estimation results of the LAI and SPAD of the winter wheat. Through combination of a saturation perception mechanism and semi-supervised learning, estimation deviation caused by dense canopy vegetation index saturation and data noise is effectively eliminated, and the estimation precision and generalization ability in a complex agricultural scene are improved while the lightweight deployment characteristic of the model is ensured.

The invention discloses a winter wheat yield forecasting method based on a random forest regression model, and relates to the technical field of winter wheat yield prediction.The winter wheat yield forecasting method comprises the steps of multi-source data integration, dynamic feature screening, random forest model optimization and yield prediction and verified.The multi-source data integration provides input data for dynamic feature screening; high-quality features screened by the dynamic features are used as training features of a random forest model optimization module, and a result of random forest model optimization is used as final yield prediction and verification of yield prediction and verification. According to the method, by designing multi-source data fusion and automatic model optimization, the problems that a traditional method is single in data source, difficult to capture complex agronomic relations and insufficient in model generalization ability are solved, high-precision and high-stability winter wheat yield forecasting is achieved, and powerful support is provided for agricultural production decision making.

The invention discloses a wheat yield remote sensing prediction method combining phenological parameters, which comprises the following steps: S1, performing field observation in a key growth period of winter wheat, synchronously collecting canopy hyperspectral reflectivity data, leaf area index (LAI) and SPAD value of each observation sample point, and recording wheat grain yield of the corresponding sample point; s2, performing noise reduction preprocessing on the acquired canopy hyperspectral data, and extracting sensitive spectral parameters by combining principal component analysis (PCA) and correlation analysis methods; s3, taking the sensitive spectrum parameters, LAI and SPAD values as independent variables, taking the wheat grain yield as a dependent variable, and adopting partial least squares regression (PLSR) to construct a multivariable yield prediction model; and S4, performing wheat yield prediction on an independent test sample or regional scale remote sensing data by using the trained model. The method overcomes the defect that only yield sensitive spectrum parameters are used for predicting the effect, and accurate estimation of the model is achieved.

The invention relates to the technical field of agricultural intelligent equipment, in particular to intelligent monitoring and weeding equipment for gramineous weeds in a winter wheat field, and aims to solve the technical problem of high missing detection rate and false detection rate of a traditional visual identification method caused by similar forms of winter wheat and gramineous weeds in a seedling stage. The equipment comprises an autonomous mobile platform, a multi-mode sensing assembly, a data processing and decision-making unit and a targeted clearing execution mechanism. The multi-mode sensing assembly integrates a hyperspectral imaging module, a three-dimensional shape measurement module and a polarization characteristic analysis module, and pixel-level data registration is achieved in combination with the cooperative illumination system. The data processing unit performs high-confidence classification decision on the multi-source features through a hierarchical Bayesian fusion model; and the targeted clearing mechanism adopts a laser galvanometer system to carry out non-contact precise clearing on the identified weed growing points. Intelligent identification and efficient physical weeding of weeds in the winter wheat field are achieved, and the method has the advantages of being free of chemical residues and high in operation continuity.

The invention belongs to the field of agricultural computer electronics, and particularly discloses a winter wheat nitrogen nutrition diagnosis and nitrogen demand estimation method based on hyperspectral data, which comprises the following steps: acquiring winter wheat canopy spectral data by adopting an ASD FieldSpec4 hyperspectral spectrometer, and measuring the nitrogen concentration and biomass of winter wheat leaves at the same time; and estimating the nitrogen concentration and biomass in combination with a one-dimensional convolutional neural network model. And based on the estimation result, calculating the nitrogen nutrition index by using a critical nitrogen dilution curve, and establishing a nitrogen demand estimation model comprehensively considering the number of days after sowing, the nitrogen fertilizer recovery efficiency and the nitrogen nutrition index. The method provides powerful support for accurate management of the winter wheat nitrogen fertilizer.

The invention relates to a winter wheat spring low temperature disaster climate risk evaluation method. The method comprises the steps of obtaining multi-source data of a target time period in a target area; according to the multi-source data, the winter wheat spring development period is divided into different development period periods, and according to the meteorological data, the time minimum temperature and the process cold accumulation capacity of the different development period periods are determined; determining the disaster degree of the winter wheat in different developmental periods according to the minimum temperature and the process cold accumulation capacity in different developmental periods, and determining the occurrence probability of the winter wheat spring low-temperature disaster according to the disaster degree; according to the crop data, determining the winter wheat average yield reduction rate and the winter wheat yield reduction rate variation coefficient, and according to the winter wheat spring low-temperature disaster occurrence probability, the winter wheat average yield reduction rate and the winter wheat yield reduction rate variation coefficient, constructing a winter wheat spring low-temperature disaster climate risk index; and carrying out risk grade division according to the winter wheat spring low-temperature disaster climate risk index. The accuracy of winter wheat spring low-temperature disaster climate risk quantitative evaluation is improved.

The invention provides a winter wheat leaf area index estimation method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: S1, data acquisition: obtaining unmanned aerial vehicle multispectral remote sensing image data of a research region, and collecting corresponding field actual measurement data in different growth periods of winter wheat; according to the winter wheat leaf area index estimation method and system based on unmanned aerial vehicle remote sensing provided by the invention, through the step design of S1 to S7, especially the collaborative cooperation of data preprocessing, data fusion, model construction and optimization, during use, on one hand, the leaf area index of the winter wheat can be estimated; in S2, splicing and geographic coordinate system embedding processing are carried out on unmanned aerial vehicle multispectral remote sensing image data, spatial positions of different wavebands can be automatically aligned, the problem that the number of rows and columns of multiband data is inconsistent is solved fundamentally, and a spatially unified data source is provided for subsequent vegetation index calculation and feature extraction.

The invention discloses a primer combination for water saving and quality screening of winter wheat and application thereof, and relates to the technical field of molecular biology, the primer combination for water saving and quality screening of winter wheat comprises a primer pair P1 and a primer pair P2; according to the primer combination, the water-saving and drought-resistant characters are screened through PCR amplification, and SDS-PAGE electrophoresis is combined to detect grain protein subunit composition so as to screen quality characters. The efficient breeding method suitable for winter wheat is formed by designing a specific primer combination, constructing a molecular marker screening system and innovatively combining a cultivation management mode of water before drought and drought after water before winter wheat. According to the method, dependence on a single known gene is avoided, oriented improvement of water-saving drought resistance and quality is realized through collaborative screening and environment adaptability cultivation of a primer combination, and a new technical path is provided for wheat breeding in the arid region of North China.

The disclosure relates to commercially elite hard red winter wheat varieties having increased dough strength. The wheat variety designated OK15MASBx7 ARS 8-20, the plants and seeds and plant parts of wheat variety OK15MASBx7 ARS 8-20, methods for producing a wheat plant produced by crossing the variety OK15MASBx7 ARS 8-20 with another wheat plant, and hybrid wheat seeds and plants produced by crossing the variety OK15MASBx7 ARS 8-20 with another wheat line or plant, and the creation of variants by mutagenesis or transformation of variety OK15MASBx7 ARS 8-20 are also provided. Methods for producing other wheat varieties or breeding lines derived from wheat variety OK15MASBx7 ARS 8-20 and wheat varieties or breeding lines produced by those methods are also provided. Further provided are commodity plant products from wheat varieties having increased dough strength.

The invention discloses a Sentinel-1 SAR (Synthetic Aperture Radar) data-based winter wheat late frost damage assessment method, which comprises the following steps of: firstly, carrying out noise reduction processing on vertical emission-vertical reception (VV) and vertical emission-horizontal reception (VH) wavebands by adopting a 51 * 51 pixel window mean value aggregation method, and while satisfying the stability of a variable coefficient (CV) and a signal-to-noise ratio (SNR), carrying out noise reduction processing on the wavebands; and noise suppression and space detail reservation are effectively balanced. The method comprises the following steps of: extracting a time sequence signal sudden drop characteristic caused by frost on the basis of a VH / VV polarization ratio (VH / VV) by combining a Savitzky-Golay (SG) filtering and envelope line fitting method, and constructing an FDSI index according to the time sequence signal sudden drop characteristic. The verification result of a field survey sample point shows that the correlation coefficient r between the FDSI based on VH / VV and the freeze injury grade reaches 0.82 (plt, 0.01) and is obviously superior to RVI (r = 0.73) and DPSVI (r = 0.70). According to the method, an efficient and reliable evaluation means can be provided for regional-scale late frost damage evaluation.

This invention belongs to the field of agricultural planting technology, specifically relating to a method for wide-row precision sowing and shallow-buried drip irrigation for winter wheat. It combines wide-row precision sowing technology with a shallow-buried drip irrigation system, along with precise water and fertilizer management, to achieve high-yield and efficient winter wheat cultivation. By integrating wide-row precision sowing, shallow-buried drip irrigation, and precise water and fertilizer management technologies, and through a system coupling design, it achieves a synergistic improvement in sowing quality, irrigation efficiency, and fertilizer utilization. The spatially coordinated layout of wide-row precision sowing and shallow-buried drip irrigation enables precise matching of the drip irrigation tape and seed rows, significantly improving water and nutrient utilization efficiency, while simultaneously promoting winter wheat root development and yield stability.

The invention relates to a water and fertilizer integrated drip irrigation experiment monitoring method suitable for winter wheat, and relates to the technical field of crop irrigation, and the method comprises the following steps: 1, selecting an experiment address: selecting 15 mu of land with sufficient light and heat and the same period of rain and heat in a plain area of a warm temperature zone; 2, sowing is conducted, mechanical sowing is conducted, the sowing quantity is 30 jin / mu, the line spacing is 15 cm, and a tested wheat variety is Zhongmai 29; 3, quantitatively applying water and fertilizer, setting irrigation quota of 20, 30 and 40 m < 3 > / mu according to test design, setting fertilization amount as a secondary factor, and respectively setting 210 and 240 kgN / hm < 2 >, 4, walking in an experimental field through a plurality of monitoring devices, picking or measuring and monitoring the wheat in each period of wheat growth, and calculating the water utilization efficiency according to the wheat yield; and step 5, recording meteorological data, and recording the data once every 1h. The method has the beneficial effects that the growth conditions and various parameters of the winter wheat under different variable conditions are obtained, and then the drip irrigation water quantity and the water-fertilizer proportion which are most suitable for the winter wheat are obtained.

This invention proposes a winter wheat cultivation method to enhance lodging resistance. Step one involves selecting high-quality varieties with high single-plant productivity, strong resistance, high economic efficiency, and no premature aging; choosing flat, sunny plots that are not prone to waterlogging, have fertile soil, good aeration, and convenient irrigation and drainage; deep plowing and fine cultivation, applying base fertilizer per acre, spreading it evenly on the ground, and immediately tilling to a depth of 20-25 cm. This planting method, through scientific and reasonable seed selection and field management, results in high wheat emergence rate, low growth, robust straw, good lodging resistance, and high ear formation rate, effectively improving wheat yield and quality. By setting up anti-lodging anchors and applying fertilizer during the jointing and grain-filling stages, and introducing prepared corn straw fermentation products, the bending strength of wheat stems is enhanced. By significantly increasing the bending strength of wheat stems, the wheat straw's ability to withstand wind and ear pressure is improved.

The utility model relates to the technical field of agricultural machinery, in particular to a topdressing device for winter wheat cultivation, which comprises an adjusting component, a fertilizing component and a discharging component, the adjusting component comprises a mounting frame, third sliding chutes are arranged on the upper side and the lower side of the mounting frame, a first sliding chute is arranged on the front side of the mounting frame, and a movable plate is arranged on the third sliding chutes. A rack structure is arranged on the first sliding groove, a fixed frame is arranged below the movable plate and arranged on an adjusting plate, a second sliding groove is formed in the adjusting plate, a strip-shaped notch is formed in one end of the fixed plate and obliquely arranged, a connecting column is arranged at the tail end of an adjusting rod after the adjusting rod penetrates through one side of the fixed frame, and the lower portion of the fixed column is arranged in the strip-shaped notch. The tail end of the fixing plate is of a sawtooth structure and is matched and fixed with the rack structure, the adjusting rod is matched with the strip-shaped notch through the connecting column, the fixing plate and the rack structure are matched and fixed, different fertilization intervals can be met, and self-locking can be achieved in cooperation with the weight.

The invention relates to a winter wheat drought-resistant composition system and an application method thereof, and belongs to the technical field of agricultural chemistry. The technical problems that an existing single formula cannot meet the differentiation requirements of root promotion in the tillering stage and leaf protection in the heading stage of winter wheat, so that root system development is poor, the photosynthetic efficiency is reduced and the yield loss is serious under the drought condition are solved. The system comprises a formula A, a formula B and a formula C, wherein the formula A contains 35-45 mmol / L of glycine betaine, 5-8 mmol / L of trehalose and 0.1-0.5% (w / v) of polyglycerol fatty acid ester; and the formula B contains 55 to 65 mmol / L of glycine betaine, 0.2 to 0.4 mmol / L of salicylic acid and 0.1 to 0.5 percent (w / v) of polyglycerol fatty acid ester. When the water content of a soil layer of 0-20 cm is smaller than or equal to 12% and wheat is in the tillering last stage, the formula A is sprayed, and root growth is promoted; when the wheat is in the heading stage, the formula B is sprayed to protect the physiological functions of canopies. The system is mainly used for improving the drought resistance of the winter wheat under continuous drought stress, stabilizing the yield and reducing the biomass loss.

This invention relates to the fields of remote sensing image processing and agricultural information technology, and in particular to a method for refined extraction of winter wheat planting areas based on Gaofen-2 imagery and edge-enhanced DeepLabV3+. The method includes: acquiring Gaofen-2 imagery and performing preprocessing such as radiometric calibration, atmospheric correction, geometric fine correction, and image fusion to construct a labeled training dataset; constructing an edge-enhanced DeepLabV3+ network, which adds an edge enhancement module to the standard DeepLabV3+ network to explicitly learn the edge features of winter wheat fields and inject edge information into the decoder; designing a joint loss function, including segmentation loss, edge loss, and boundary-aware loss, and training the network end-to-end; and using the trained network to perform sliding window prediction and post-processing on the image to be extracted to obtain the refined extraction results of winter wheat planting areas. This invention significantly improves the segmentation accuracy of winter wheat field boundaries through the edge enhancement module and boundary-aware loss.

The present application belongs to the technical field of agricultural remote sensing and crop yield estimation, and specifically discloses a winter wheat yield estimation method based on a combination of spectral characteristic indexes in key growth periods. The method is based on winter wheat canopy hyperspectral reflectance data, and first constructs spectral characteristic indexes FVI and a spectral characteristic index set. Then, the core indexes related to yield estimation are obtained through progressive screening, and the index combination optimization is carried out in each growth period respectively, and further growth period combination optimization is carried out, so as to determine the target key growth period combination for yield estimation. On this basis, a cross-period dynamic feature is further constructed, and a two-stage XGBoost winter wheat yield estimation model oriented to key growth period information utilization is established, so as to realize accurate estimation of winter wheat yield.

The application discloses a winter wheat multi-target irrigation dynamic optimization method based on a crop model and artificial intelligence, and belongs to the field of agricultural intelligent irrigation optimization and decision-making. The method comprises the following steps: preprocessing meteorological data, soil data, crop parameter data and irrigation management data to obtain standardized input data; performing coupling processing on an AquaCrop model and a PyFAO56 model based on the standardized input data to obtain final output of the coupling model; based on the final output of the coupling model, adopting an NSGA-2 multi-target optimization algorithm to optimize decision variables to obtain a Pareto optimal solution set; and combining a fusion strategy of an entropy weight method and a random forest algorithm to sort candidate schemes in the Pareto optimal solution set to obtain a final irrigation strategy. The application provides a new type of precise irrigation scheme by giving consideration to yield and water efficiency through multi-target optimization, objective weighting and machine learning intelligent evaluation.

The application discloses a winter wheat water and fertilizer coupling water-saving and fertilizer-saving method based on potassium demand intensity regulation, relates to the technical field of water and fertilizer integrated irrigation and fertilization, and comprises the following steps: determining soil moisture in the growth period of winter wheat, recording precipitation in the growth period of winter wheat, dividing the growth period of winter wheat into three potassium demand intensity stages by using the growth time, controlling irrigation water and potassium fertilizer application amount in combination with the soil water holding capacity of the soil layer and the potassium content in the soil for the three different potassium demand intensity stages, and carrying out irrigation and fertilization in an alternating irrigation mode by using a water and fertilizer integrated irrigation and fertilization system; and the application reduces the waste of water and fertilizer resources by precise water and fertilizer management and optimizes and improves the uniformity, precision and efficiency of water and fertilizer application by the water and fertilizer integrated irrigation and fertilization system.

The application discloses a kind of synthetic functional microbial inoculants and its application in salt-tolerant growth promotion of wheat, adopt hydrophobic gordon's bacteria ( Gordonia hydrophobica ), odyssey lysine bacillus ( Lysinibacillus odysseyi ), sea bed motility microorganism ( Planomicrobium okeanokoites ), north China broussonetia yellow color bacterium ( Luteimonas huabeiensis ), epidermis brevibacterium ( Brevibacterium epidermidis ) five strains of bacteria, microbial inoculant has no antagonism between strains, salt-tolerant, and other significant characteristics such as viable count, can significantly promote spring wheat germination rate, seedling length and root length, and has significant promoting effect on seedling length and root length of winter wheat.Under salt stress conditions, the microbial inoculant can significantly improve the activities of catalase, peroxidase and superoxide dismutase of wheat, and the total chlorophyll content;after treatment with the microbial inoculant, the contents of malondialdehyde and proline of wheat are significantly reduced.The microbial inoculant plays a promoting and protecting role in the process of resisting salt stress of spring wheat and winter wheat, and has an impact on the development and sustainable development of arid region agriculture.

The application relates to the technical field of agricultural intelligent irrigation, in particular to a saline-alkali soil high-concentration saline water resource irrigation utilization method, device, equipment and medium. The saline-alkali soil high-concentration saline water resource irrigation utilization method comprises the following steps: according to the variety attribute of a target sowing variety, determining the planting requirement and irrigation target of the target sowing variety; analyzing the planting requirement to determine the preliminary irrigation condition; detecting the soil data and environmental data of a target saline-alkali soil, and sowing the target sowing variety on the target saline-alkali soil; according to the irrigation target, analyzing the soil data, environmental data and preliminary irrigation condition to determine the irrigation time of the target saline-alkali soil. Through the analysis of the planting requirement, the irrigation condition can be preliminarily determined, which helps to reduce the search range of the subsequent irrigation time determination and improve the determination efficiency. In turn, the irrigation efficiency is improved, the utilization efficiency of the saline-alkali soil and the saline water is improved, and the yield of winter wheat in the saline-alkali soil is improved.

The utility model relates to the technical field of agricultural engineering, in particular to a disease and pest treatment device for high-yield winter wheat seed culture, which comprises a liquid medicine box with an air chamber, and a filter device is fixedly connected to the inner side of the liquid medicine box; the filtering device comprises a liquid inlet, the upper end of the liquid inlet is spirally connected with a sealing cover, the inner side of the liquid inlet is fixedly connected with a limiting plate, the inner side of the limiting plate is provided with a filtering plate, the inner wall of the liquid inlet is rotationally connected with a rotating shaft, and the outer side of the rotating shaft is fixedly connected with a water retaining folding plate. A guide rod is fixedly connected to one side, away from the filter plate, of the impact block, penetrates through one side of the liquid inlet and is slidably connected with the inner side of the liquid medicine box; the filter plate comprises a filter screen, and the outer side of the filter screen is fixedly connected with a clamping ring. According to the spray head, solid impurities such as undissolved medicine powder particles and plant residues can be effectively filtered through the filter screen, the spray head is effectively and evenly sprayed, the spray head is prevented from being blocked, and the service life is prolonged.

The utility model relates to the field of crop breeding germination technology discloses a winter wheat breeding is with germination device, including the breeding box, the inner top wall middle part of breeding box is provided with the wheat seed level mechanism, the wheat seed level mechanism is used for the even level of wheat seed in rectangular tray, the inside of breeding box is provided with the automatic cleaning mechanism for cleaning V -shaped tray bottom jamming hydrophobic hole, the wheat seed level mechanism includes fixed end plate no.

The present application relates to winter wheat drought early warning technical field, especially relates to a kind of winter wheat drought early warning method, device, electronic equipment and storage medium, the method of the present application first obtains first factor vector;Then the first factor vector is converted by first coefficient matrix, and the second factor vector obtained from conversion is found from multiple soil moisture content conditions classes as target moisture content condition class;Next, meteorological forecast data queue is input into target moisture content change model, and soil moisture content data queue is determined according to the first soil moisture content change data queue obtained and current soil moisture content;Finally, according to the wheat growth period and soil moisture content data queue, drought early warning is carried out.The present application fits the law that meteorology and soil moisture content change by multiple moisture content change models, reduces the complexity of model, significantly improves the accuracy of drought early warning analysis by meteorological data, and since different soil moisture content characteristics are matched, the applicability is greatly improved.

This invention discloses a primer combination for water-saving and quality screening of winter wheat and its application, relating to the field of molecular biology. The primer combination includes primer pair P1 and primer pair P2. This primer combination is used for molecular marker screening of water-saving and drought-resistant traits in winter wheat and can be combined with SDS-PAGE electrophoresis to detect grain protein subunit composition for screening quality traits in winter wheat. This invention designs specific primer combinations, constructs a molecular marker screening system, and innovatively combines it with the "pre-irrigation followed by drought" cultivation management model, forming a highly efficient breeding method suitable for winter wheat. This method avoids dependence on a single known gene, instead achieving targeted improvement of water-saving, drought-resistant, and quality through synergistic screening of primer combinations and environmental adaptability breeding, providing a new technical path for wheat breeding in the arid North China region.

This invention provides a method for estimating winter wheat yield based on multi-source remote sensing data fusion. The method includes selecting MODIS and GF-1WFV data and preprocessing the data; fusing MODIS and GF-1WFV data using the enhanced spatiotemporal adaptive reflectance fusion model ESTARFM, performing consistency checks on the fusion results, and generating time-series data; using a decision tree method to extract winter wheat planting areas based on the temporal variation pattern of winter wheat NDVI; extracting growth monitoring indicators from the multi-source remote sensing fusion data, obtaining meteorological data for the study area using spatial interpolation methods, dividing the study area into stages according to the key growth stages of winter wheat, and statistically calculating the average values ​​of indicators for the key growth stages; constructing a multi-parameter, multi-time-window winter wheat yield estimation model based on the growth indicators and meteorological parameters of the key growth stages of winter wheat; and verifying the yield estimation accuracy. This invention can obtain winter wheat yield estimation results with continuous time and higher spatial resolution.

The utility model discloses a drip irrigation device for quantitatively applying nitrogen to winter wheat in saline-alkali soil, which comprises a support frame, one end of the support frame is fixedly connected with a material mixing barrel, the material mixing barrel is provided with a material discharging assembly, and the material mixing barrel is provided with a nitrogen adding assembly; a water storage plate abuts against a push plate when rotating, a sealing plate is pushed to move in a sleeve, and opening and closing of a feeding opening are accurately controlled, so that quantitative nitrogen fertilizer adding is achieved, and the accurate requirements of winter wheat for nitrogen in different growth stages are met; a plurality of groups of second springs are uniformly distributed at one end of the sealing plate, so that the stability of the sealing plate in the moving process is ensured, and the consistency of the nitrogen fertilizer addition amount each time is ensured.

The invention provides a multi-dimensional feature fusion winter wheat weed detection method and system, and relates to the field of agricultural information technology and precision agriculture technology, and the method comprises the steps: constructing a deep learning model of WMGNet, the deep learning model of the WMGNet comprises a wavelet feature processing module, a bispectrum Mamba branch module, a graph volume integral branch module, a feature fusion module and a classifier which are connected in sequence; inputting the standardized winter wheat image into a deep learning model of WMGNet; extracting a multi-scale feature map of the standardized winter wheat image through a wavelet feature processing module; through a bispectrum Mama branch module, extracting long-range dependency features of the multi-scale feature map; non-local features of the enhanced features are extracted through a graph volume integral branch module; fusing the long-range dependency feature and the non-local feature to obtain a fused feature; and inputting the fusion features into a classifier, and outputting a detection result of the winter wheat high-resolution hyperspectral image.

The application discloses a brown sugar culture medium suitable for a salt-tolerant composite microbial inoculant and application thereof, and remarkably improves the propagation efficiency of the salt-tolerant composite microbial inoculant and the growth promoting effect on wheat by optimizing the brown sugar culture medium. The culture medium provided by the application can effectively support the growth of strains, and in the range of 0.2-2 g / L, the biomass of the strains increases with the increase of the concentration, and is better than that of an LB control group. After the wheat is treated by the salt-tolerant composite microbial inoculant cultivated by the culture medium provided by the application, the salt-tolerant composite microbial inoculant shows excellent growth promoting capacity, the winter wheat seedling length and root length are longer than those of the control group, and the spring wheat seedling length and root length are also better than those of other treatment groups, thereby significantly promoting the root development and the growth of the above-ground part of crops under a salt stress environment, and showing a broad application prospect of the technology in the improvement of saline-alkali land or the development of biological fertilizer.

The invention discloses a method for estimating a photosynthetically active radiation component of winter wheat, which comprises the following steps of: simulating common 21 vegetation indexes of Sentinel-2 on the basis of reflectivity data measured on the ground, comparing the inversion capability of the indexes on fPAR of the winter wheat, testing the stability of the vegetation index with the highest correlation in fPAR inversion under the stress of different varieties, illumination and water, and calculating the photosynthetically active radiation component of the winter wheat. And then the estimation model is verified at a satellite scale, and the constructed estimation model is verified to still have relatively high stability and precision for fPAR estimation under different scales and different environments. The constructed estimation model can be used in large-scale and near-real-time winter wheat fPAR estimation, provides reference for agricultural condition monitoring and winter wheat yield estimation, and is suitable for performing vegetation index-based winter wheat fPAR estimation in a field scale and a satellite scale.