191 results about "Winter wheat" patented technology

A rapid monitoring and identification method for winter wheat drought levels includes: 1) obtaining multispectral images and ground-measured LAI through UAV-based multi-payload low-altitude remote sensing technology, and calculating vegetation indices such as Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI), Enhanced Vegetation Index (EVI), Optimized soil adjusted vegetation index (OSAVI) and Transformed Chlorophyll Absorption Reflectance Index; 2) establishing regression equations between the calculated vegetation indices and measured LAI for different growth stages, and selecting the optimal model equation for each growth stage; 3) using the optimal model equation to invert the LAI of winter wheat at various growth stages, and calibrating the LAI thresholds for different drought stress levels; 4) acquiring the multispectral images of target plot through real-time monitoring, calculating the required vegetation indices, inverting to obtain the LAI value, and comparing it with the threshold to determine the current drought stress level.

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 discloses a cultivation method for rapid vernalization of winter wheat to accelerate annual iteration, and belongs to the technical field of plant growth regulation and control. The cultivation method comprises the following steps of disinfection and germination, low-temperature vernalization, transplanting and cultivation, and drying and harvesting. The method has the beneficial effects that compared with other current wheat growth acceleration methods, the technical process and the required environment of each stage are simple, and the (semi) winter wheat breeding efficiency is greatly improved. The temperature or photoperiod does not need to be repeatedly adjusted, and vernalization can be performed by adopting short photoperiod / low illumination intensity, so that energy is saved, and the method is environment-friendly and suitable for large-scale popularization. The cultivation method is green, efficient, low in power consumption, low in requirement for environment equipment and simple and convenient. The method is suitable for being used in large-scale plant factories, and meanwhile, a method for rapid vernalization of winter wheat to accelerate annual accelerated iteration of wheat is provided for scientific research institutions, enterprises and public institutions with limited conditions.

The invention relates to the technical field of winter wheat growth and development recognition, in particular to a winter wheat growth and development recognition method based on a satellite remote sensing image, and the method comprises the following steps: S1, carrying out the preprocessing of the satellite remote sensing image; according to the method, satellite remote sensing images are preprocessed, including radiation correction, atmospheric correction, geometric correction and the like, so that noise and distortion in the images are eliminated, image quality is improved, planting and phenological characteristics of winter wheat are investigated and analyzed, specific planting phenological characteristics of winter wheat are obtained, extracted characteristics are classified and identified by using a support vector machine (SVM), and the winter wheat planting phenological characteristics are identified. Winter wheat areas in the images are judged, characteristics related to the wheat development stage can be extracted by analyzing the spectral reflectivity of wheat in different growth stages, the growth condition of winter wheat is evaluated, and the health condition of winter wheat and whether the problems of diseases and insect pests, water stress, nutrient deficiency and the like exist or not are analyzed according to the evaluation result. And corresponding management suggestions are provided accordingly.

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 lodging winter wheat spatial distribution extraction method based on bimodal feature fusion, and relates to the technical field of agricultural remote sensing monitoring, and the method comprises the steps: building a double-flow semantic segmentation network DMFCFNet, and training the network through employing a constructed sample data set, inputting the preprocessed remote sensing image and the DSM elevation image into a network in parallel to obtain semantic texture feature information of the remote sensing images with different scales and spatial structure feature information of the DSM elevation image; and performing feature correction and fusion on the obtained semantic texture feature information of the remote sensing image and the spatial structure feature information of the DSM elevation image, and performing segmentation prediction on the fused feature information to realize extraction of the spatial distribution information of the winter wheat lodging region. By correcting and fusing remote sensing image semantic texture features and DSM elevation features and combining multi-scale information aggregation processing, the winter wheat lodging region extraction precision is effectively improved, and the problems of small target missing detection and boundary blur are solved.

The invention discloses a winter wheat field soil fertility evaluation data processing method based on comprehensive weight calculation, and relates to the technical field of data processing, and the method comprises the steps: S1, collecting nutrient content and moisture retention data from field soil through a sensor network, performing grouping processing on the acquired nutrient content and moisture retention data by adopting decision node splitting and entropy calculation to obtain a soil fertility layering result; s2, according to a soil fertility layering result, acquiring root depth and leaf moisture content indexes of the winter wheat in different layers of soil from crop monitoring equipment, analyzing association strength of the root depth and leaf moisture content indexes and fertility layering by adopting leaf node classification and feature selection standards, and determining a fertility contribution coefficient; according to the winter wheat field soil fertility evaluation data processing method based on comprehensive weight calculation, efficient resource allocation and stable yield are realized, and the drought resistance and resource utilization efficiency of winter wheat in a drought environment are remarkably improved.

The invention discloses a winter wheat LAI inversion method based on scenarized parameter calibration and ensemble learning. The method comprises the following steps: fusing a remote sensing image with auxiliary geographic information such as terrain and soil, and performing typical planting scene division on a research area by adopting a spectral clustering method; then scene optimization is carried out on highly sensitive parameters (such as LAI, Cab, Cm and ALA) in the radiation transfer model PROSAIL based on a Markov chain Monte Carlo (MCMC) algorithm, and parameter posterior distribution more consistent with actual observation is obtained; further utilizing optimization parameters to generate a high-quality and homogenized simulation sample set, and mapping the simulation sample set to a Sentinel-2 waveband to construct a training data set; and finally, performing training by adopting various machine learning models (such as LightGBM, XGBoost, RF and KNN), and constructing a fusion model through a stacking integration strategy to realize high-precision inversion of LAI. Experimental results show that the method has higher fitting precision and stronger generalization ability in a plurality of typical planting scenes.

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 belongs to the technical field of crop planting, and discloses a Yellow River Delta winter wheat salt-tolerant variety screening and cultivated land quality improving method, which comprises the following steps: step 1, indoor culture test, namely influence of different salinity concentrations on winter wheat budding, step 2, field positioning test, and step 3, field positioning test. The invention relates to screening of salt-resistant suitable-growth high-yield varieties of winter wheat in mild saline-alkali cultivated land, and influences of organic materials with different carbon sources on soil quality and crop yield. According to the invention, the physiological status and agronomic traits of the current Yellow River Delta slight saline-alkali soil winter wheat variety in different growth periods are explored, and a theoretical basis is provided for screening the salt-resistant, suitable-to-grow and high-yield winter wheat variety; the influence of different carbon source materials on the physicochemical properties of the saline-alkali soil is analyzed, the microbial enhancement action mechanism in the saline-alkali soil is explored, 4-5 salt-tolerant winter wheat varieties are screened for the theoretical basis for improving the quality and productivity of the saline-alkali soil of the Yellow River delta, the physicochemical properties of the saline-alkali soil are improved, and the soil microbial community structure and diversity are enhanced.

The invention discloses an edge-guided winter wheat fine spatial distribution remote sensing extraction method, which relates to the technical field of agricultural remote sensing monitoring, introduces edge information into a feature extraction and classification process, and establishes an edge-guided extraction network EGENet. Through EGENet, extraction and subsequent fusion prediction of spatial feature information and multi-scale semantic feature information of a remote sensing image and edge information of a winter wheat field parcel are realized. In the feature classification process, in order to effectively utilize edge information, the EGENet constructs a dynamic weighting classification method based on edge density. According to the method, the output weight of Softmax is dynamically adjusted in combination with the edge weight map, so that the classification decision of the edge region is more dependent on accurate edge information, the classification precision is improved at the edge, and meanwhile, the consistency of the classification result and the actual surface feature boundary is ensured.

The invention 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, the method comprises the following steps: preprocessing meteorological data, soil data, crop parameter data and irrigation management data to obtain standardized input data; carrying out coupling processing on the AquaCrop model and the PyFAO56 model based on the standardized input data to obtain final output of a coupling model; based on the final output of the coupling model, optimizing decision variables by adopting an NSGA-2 multi-objective optimization algorithm to obtain a Pareto optimal solution set; and sorting the candidate schemes in the Pareto optimal solution set by combining a fusion strategy of an entropy weight method and a random forest algorithm to obtain a final irrigation strategy. According to the invention, through multi-objective optimization, objective weighting and machine learning intelligent evaluation, a novel precise irrigation scheme considering both yield and water efficiency is provided.

The invention discloses a cultivation method for accelerating wheat cycle iteration, and belongs to the technical field of wheat cultivation. The method comprises the following steps: (1) seed disinfection: performing disinfection treatment on wheat seeds, and culturing in a dark place until the seeds are white, (2) vernalization: performing illumination treatment on winter wheat at 4-13 DEG C; and (3) planting operation: transplanting the treated wheat seeds into a seedling tray, culturing under the combination of illumination intensity of 100-400 [mu] mol m <-2 > s <-1 >, white spectrum and photoperiod of 12-24 h light / 12-0 h dark until the wheat seeds are mature, and harvesting. The method has the beneficial effects that the TOPSIS method is utilized to comprehensively score and sort different light intensity and light period combination conditions so as to determine the optimal illumination condition combination. By the adoption of the optimization method, the growth cycle of wheat can be remarkably shortened, screening and cultivation of germplasm resources are accelerated, the iteration efficiency and germination rate of generations are effectively improved, technical support is provided for rapid breeding of good varieties, and the optimization method has good application prospects.

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 provides a winter wheat growth stage classification method based on improved YOLOv8, and the method comprises the steps: carrying out the multi-angle and multi-scale omnibearing shooting of a crop, and obtaining an original image covering a plurality of growth stages of the crop; performing data preprocessing on the original image to obtain a data set; the YOLOv8n-cls model is improved to obtain an improved model, and the improved model comprises the steps that a down-sampling convolution kernel in the YOLOv8n-cls model is replaced with a double-branch down-sampling module combining convolution and pooling; standard convolution in a Bottleneck structure of a C2f feature extraction module in the YOLOv8n-cls model is replaced with full-dimensional dynamic convolution, and a C2f-ODConv module is obtained; a triple attention module is added to the output end of the C2f-ODConv module; and performing classification operation on the data set through the improved model to obtain a classification result. According to the method, the YOLOv8n-cls model is structurally improved, and a triple attention mechanism is introduced, so that the winter wheat growth stage identification and classification accuracy, the agricultural production efficiency and the resource utilization rate are improved.

The invention belongs to the technical field of desert agricultural engineering, and discloses a method for planting winter wheat in desert land, which comprises the following steps: S1, land pretreatment; s2, an intelligent drip irrigation system; s3, constructing a composite water retention layer; s4, finely leveling the ground; s5, accurate sowing; s6, secondary wheat cultivation; s7, performing no-tillage continuous planting; and S8, crop rotation period management. By the adoption of the laser leveling instrument and the intelligent drip irrigation system, efficient leveling and precise irrigation of the land are achieved, the labor cost and the operation difficulty are reduced, the water retention capacity of the soil is further improved through construction of the composite water retention layer, and the irrigation frequency and the water consumption are reduced. The accurate sowing and secondary wheat cultivation technology ensures uniform distribution and healthy growth of wheat seeds, and improves the emergence rate and the survival rate. By means of the no-tillage continuous planting technology, the soil turning frequency is reduced, the soil structure is protected, development of wheat root systems and nutrient absorption are promoted, and then the yield of wheat is increased.

The invention provides a winter wheat soil moisture inversion method and system based on an improved water cloud model, storage equipment and electronic equipment, and the method comprises the following steps: obtaining total backscattering characteristics VV and VH, an incident angle and a plurality of polarization characteristic parameters through employing a single-view complex image and a geocoding radiation correction image; calculating a vegetation index from the preprocessed optical image by applying a wave band calculation mode; an improved water cloud model method is applied to correct the radar backscattering coefficient affected by the winter wheat plants, the backscattering coefficient of the earth surface is obtained, and data redundancy is reduced; performing hyper-parameter optimization of the CNN network by applying a CPO algorithm; and constructing a CNN-CPO-RF model by using CNN and RF models to carry out soil moisture estimation. According to the invention, a more accurate soil moisture result can be obtained. According to the method, the robustness of farmland surface soil moisture inversion is remarkably improved, and a new technical means is provided for performing soil moisture inversion by using a satellite-borne SAR image.

The invention provides a method for planting winter wheat in saline-alkali soil, which belongs to the technical field of wheat planting, and comprises the following steps: S1, rotary tillage and soil preparation: carrying out rotary tillage at the depth of 25-30cm 30-40 days before sowing, doping foreign soil, and applying straw and gypsum; s2, seed treatment: selecting saline-alkaline tolerant wheat seeds, soaking the wheat seeds in a liquid medicine, and soaking the wheat seeds in the liquid medicine; s3, ridging operation: high ridges with the ridge height of 15-20 cm, the ridge surface width of 40-50 cm and the ridge spacing of 25-30 cm are constructed; s4, sowing in line on ridges: sowing the wheat seeds treated in the step S2 on the ridges in line, wherein the sowing depth is 2-3cm; s5, film mulching operation, wherein the ridges are covered with mulching films of a double-film structure, and the film edges of the mulching films are sealed with humic acid slurry; s6, field management: uncovering the film and supplementing seedlings in the seedling stage, and drip irrigation and topdressing under the film in the growth stage. According to the saline-alkali soil winter wheat planting method provided by the invention, the wheat emergence rate can be remarkably increased by constructing a low-salt micro-domain environment.

The invention discloses a method for preparing a special fertilizer for winter wheat-middle-season rice crop rotation system crops under straw returning, and belongs to the technical field of wheat-rice crop rotation. According to the method, soil nutrient testing and the yield increasing effect of fertilization on crops are combined, and the nutrient supply level of soil is truly reflected; the habitual fertilization of farmers is optimized under the condition of comprehensively considering the nutrients brought by the straws of previous crops, the nutrient release rate and the nutrient effectiveness of the straws, the relatively high carbon-nitrogen ratio under straw returning and the apparent balance of potassium in the soil of a producing area. Compared with traditional farmer habitual fertilization, the optimized recommended fertilization formula provided by the invention not only contains previous crop straw nutrients, but also provides a special fertilizer formula for a main growth stage. The formula of the special fertilizer for winter wheat-middle-season rice crop rotation system crops under straw returning not only can improve the yield of crops, but also can improve the utilization efficiency of nutrients.

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 crop disaster early warning, and discloses a winter wheat drought early warning method and system based on multi-source data, electronic equipment and a storage medium, and the method comprises the steps: obtaining a satellite remote sensing vegetation index, meteorological parameters and soil moisture data, and carrying out the time sequence synchronization processing of the obtained data through a preset time window, obtaining a multi-source data set with unified time resolution; performing dimension reduction processing on the multi-source data set by using a principal component analysis method to obtain a feature vector set after dimension reduction; performing growth stage division on the feature vector set through a preset winter wheat growth stage division rule to obtain a staged feature sequence; modeling the water demand characteristic change trend of the characteristic sequence of each growth stage by using a long-short term memory network to obtain a prediction parameter set reflecting a dynamic water demand relationship; and extracting a water demand change trend from the prediction parameter set, extracting drought characteristics from the water demand change trend, carrying out drought grading, and sending out a drought early warning according to a drought grading result.

The invention discloses a method for direct seeding and multiple cropping of cotton after wheat in southern Xinjiang, and belongs to the technical field of cotton planting in southern Xinjiang areas. According to the method, cotton varieties with compact plant types are selected, the plant spacing in a wide-narrow planting mode is shortened, the number of plants is increased, meanwhile, the number of cotton bolls of a single plant is reduced, water and fertilizer integrated management measures are taken in cooperation, and the method adapts to accumulated temperature changes between the winter wheat harvest period and the frost season in June by means of the population effect advantage of cotton crops; high yield of cotton is guaranteed, and double cropping of winter wheat and cotton in one year is realized.

The invention provides a large-scale winter wheat yield remote sensing estimation method based on unmanned aerial vehicle and satellite image cross-scale information fusion, and the method comprises the following steps: obtaining an unmanned aerial vehicle image in a wheat key growth period and a satellite image in the same period through satellite-aircraft-ground synchronous scatter sampling, and constructing an unmanned aerial vehicle platform winter wheat yield estimation model; obtaining a yield sample augmented data set; the method comprises the following steps: constructing a satellite pixel spatial diversity index by using an unmanned aerial vehicle image, realizing cross-scale information fusion, and determining the uncertainty influence of growth difference in a field and mixed pixels on satellite winter wheat yield estimation; and making a sample augmentation optimization strategy based on the optimal spatial resolution index, determining an unmanned aerial vehicle and satellite fused winter wheat yield estimation model, and realizing large-scale field-level winter wheat yield estimation. According to the method, the winter wheat yield can be estimated timely and accurately, and the method has great application potential in the aspects of grain policy making, safety evaluation and the like.

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 remote sensing winter wheat identification method based on a multi-task learning model, and aims to solve the problems of inaccurate winter wheat edge extraction in a high-resolution remote sensing image and omission, misclassification and the like caused by over-dense distribution and intra-class diversity in an existing classical semantic segmentation model. According to the invention, a Vision Transform (MCFormer) based on multi-task learning is designed, and a semantic segmentation task and a semantic boundary task are associated together, so that the accuracy of boundary detection is remarkably improved. NDVI and LST are introduced as sample wavebands, so that the spectral characteristics of the winter wheat are enhanced.

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 relates to the technical field of agricultural planting, and discloses a winter wheat rain-suitable planting configuration optimization method and system based on drought year identification, and the method comprises the steps: constructing a drought year identification index system, collecting the data of each drought year identification index in a target planting region in real time, carrying out the preprocessing of the collected data, and carrying out the calculation of the data; pre-processed data is obtained; inputting the preprocessed data into a drought year type identification model, and identifying the current drought year type of the target planting area through the drought year type identification model; determining planting configuration parameters of winter wheat suitable for rain according to the identified drought type in combination with historical rainfall data and soil characteristics of the target planting area; monitoring parameter information in real time through a sensor to obtain monitoring data, and adjusting planting configuration parameters according to the monitoring data so as to enable winter wheat to obtain optimal growth conditions in different drought years; according to the method, different drought years can be accurately identified, and a reliable basis is provided for winter wheat rain-suitable planting configuration optimization.

The invention discloses a synergistic planting method for spring green manure-winter wheat crop rotation synergistic soil improvement, and relates to the technical field of crop planting, and the method comprises the following steps: S1, carrying out soil preparation treatment on three test fields in the first ten days of June in the current year, and then dividing areas in the three test fields to respectively sow six green manure crops; s2, in the first ten days of September, the upper portion of the green manure is cut up and evenly spread on the spot, and then green manure stubbles are turned and pressed through a small machine; s3, applying a ternary compound fertilizer as a base fertilizer on October 20th in the same year, and sowing wheat after 3 days; and S4, harvesting the wheat by adopting a harvester in the next year, and performing threshing and warehousing operation. The method has the advantages that the soil fertility is improved by turning and pressing green manure, the saline-alkali soil structure is effectively improved, the yield and quality of winter wheat are effectively improved, and environment friendliness is achieved.

The invention provides a winter wheat drought remote sensing monitoring method fused with downscaling sunlight-induced chlorophyll fluorescence (SIF) data. A winter wheat drought remote sensing monitoring method fused with downscaling SIF data comprises the steps that S1, input data of an SIF data reconstruction machine learning model are selected, and the input data comprise MODIS reflectivity, fPAR, LAI and ERA5 reanalysis data of temperature, saturation vapor pressure difference, photosynthetically active radiation and soil humidity data; s2, training and constructing a machine learning model; s3, construction and effect inspection of a long-time-sequence high-resolution downscaling SIF product data set are carried out; and S4, constructing a novel drought fluorescence monitoring index. The winter wheat drought remote sensing monitoring method provided by the invention has the advantages that the problem of how to fuse SIF information can be solved, the timeliness and accuracy of agricultural drought monitoring can be improved in mechanism, and the agricultural meteorological disaster prevention and reduction capability can be effectively improved.

The invention discloses a breeding method for promoting flowering phase synchronization of winter wheat and spring wheat, and belongs to the technical field of wheat breeding and cultivation. The breeding method comprises the following steps: disinfection and germination, low-temperature vernalization, transplanting and cultivation, and flowering phase synchronization. The method has the beneficial effects that aiming at the vernalization / low-temperature treatment stages of wheat with different winter and spring properties after germination, environmental conditions such as the photoperiod, the light intensity, the environment temperature and the vernalization / low-temperature treatment duration and environmental conditions such as the photoperiod, the light intensity and the environment temperature in the growth and development stage after vernalization are regulated and controlled; flowering phase encounter of winter wheat and spring wheat is promoted, and the growth cycle is remarkably shortened. According to the method, the flowering phases of the spring wheat and the winter wheat can meet, the bottleneck that the spring wheat and the winter wheat are difficult to hybridize due to ecological distant flowering asynchronism is broken through, and the growth cycle is remarkably shortened compared with a field.