207 results about "Chlorophyll" 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.

The invention provides an ocean red tide early warning method based on remote sensing inversion and a time-space map convolutional network. The ocean red tide early warning method comprises the following steps: collecting hyperspectral data and multispectral data; respectively carrying out data quality inspection on the collected hyperspectral data and multispectral data; preprocessing the hyperspectral data and the multispectral data after the quality inspection is qualified to respectively obtain a hyperspectral result image and a digital orthoimage; analyzing the hyperspectral result image and the digital orthoimage to obtain remote sensing inversion data of water temperature, salinity, total nitrogen, total phosphorus and chlorophyll a, and generating a gradient distribution thematic map of each index; according to water temperature, salinity, total nitrogen, total phosphorus, chlorophyll a and historical monitoring data, in combination with a change trend of key environmental parameters, introducing a space-time diagram convolutional network model, simulating a red tide diffusion situation, setting a risk threshold, and performing risk prediction on red tide occurrence through a red tide occurrence risk prediction model; and updating the red tide occurrence risk prediction model based on buoy monitoring and field monitoring data.

The invention belongs to the technical field of river and lake water quality monitoring, and discloses a river and lake water quality intelligent traceability monitoring system and method.Dissolved organic matter fluorescent fingerprints are analyzed in real time through a three-dimensional fluorescent monitoring unit, pollution markers are detected, and parameters such as COD, ammonia nitrogen and total phosphorus are synchronously detected through a full-spectrum monitoring unit; the video monitoring unit is used for collecting section images / videos of rivers and lakes, other sensors are used for collecting real-time values of chlorophyll, pH, conductivity, turbidity and dissolved oxygen in a water body, then the data processing module is used for carrying out normalization processing on multi-source data, and the analysis and early warning module is used for analyzing water quality early warning scores, spectrum early warning scores and video early warning scores. All scores are synthesized to obtain a comprehensive early warning score, traceability is started when comprehensive early warning is larger than or equal to the grade II, pollution sources are recognized based on three-dimensional fluorescence spectrum comparison and upstream patrol, and a river and lake water body monitoring-early warning-traceability-verification closed loop is achieved.

The invention discloses a river organic carbon flux inversion method based on multi-source data and related equipment. The method comprises the following steps: acquiring multi-source data of a river basin range in a target research area; obtaining a normalized difference index based on remote sensing image conversion; river parameters of the target research area are obtained based on the normalized difference water body indexes and multi-source data processing, and river runoff is obtained through conversion of a hydraulic geometrical relationship; on the basis of the remote sensing image, the normalized difference chlorophyll index and the normalized difference turbidity index, the particle organic carbon concentration is obtained through inversion by means of a first inversion model; based on the population number and the rainfall capacity, a second inversion model is used for inversion to obtain the concentration of dissolved organic carbon; and converting the river runoff, the particle organic carbon concentration and the dissolved organic carbon concentration to obtain the river organic carbon flux. The method can significantly improve the accuracy and reliability of monitoring on a high-dynamic river, accurately realizes the inversion of the organic carbon flux of the river, and can be widely applied to the technical field of data processing.

According to the unmanned aerial vehicle multi-source quantitative remote sensing rape growth state evaluation method, rape is divided into a seedling stage, a flowering stage and a legume stage according to growth nodes, ground hyperspectral and multi-spectral data are synchronously obtained, resolution is increased and decreased, the data are converted into all-dimensional, multi-scale and multi-temporal data, a growth state factor inversion model of the rape is established, and the rape growth state is evaluated. Evaluating the contribution of different growth state factors to the growth vigor of the rape, and evaluating the growth state of the rape based on comprehensive evaluation indexes of leaf area index, overground biomass and chlorophyll content. The method comprises the following steps: establishing a model based on multi-layer linear regression and partial least square optimization models of three types of growth factors LAI, AGB and CC, multi-mode prediction based on entropy aggregation and hierarchical analysis of entropy loading expert knowledge in combination with expert knowledge and agronomic knowledge, and setting a model with an optimal verification index in different growth periods as a growth state evaluation model in the period. The rape growth state evaluation period is short, the pertinence is strong, and the accuracy is good.

The invention provides a fruit tree canopy three-dimensional model reconstruction method and a fruit tree canopy three-dimensional model reconstruction system based on multi-modal data, and relates to the technical field of fruit tree canopy three-dimensional model reconstruction based on multi-modal data. Sub-pixel-level space registration is achieved by means of ground control points, voxelization division is carried out based on the range of a point cloud bounding box, and the number of point clouds is screened to construct an initial seed set; and adopting a structure-spectrum collaborative growth strategy, taking the adjacent voxels which are not accessed as candidate voxels, calculating a spectrum similarity mean value of the candidate voxels and directly adjacent voxels in the seed set, stopping growth when no new voxels are added in continuous three times of iteration, and outputting a final seed set. Meanwhile, the set is converted into a three-dimensional canopy model fusing space coordinates and full-wave-band spectral reflectivity, voxel-level pathological labeling is achieved based on the chlorophyll sensitivity index and the blue-red slope, and the whole process of reconstruction and analysis is completed.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention discloses an apple anthracnose leaf blight recognition five-dimensional data fusion method based on a patrol robot, relates to the technical field of anthracnose leaf blight recognition, and solves the problems of limited sensing dimension, large illumination interference and inaccurate spatial positioning in the prior art. Comprising the following steps: S1, acquiring surface curvature characteristics of apple leaves, collecting RGB-D images of the leaves, constructing a three-dimensional topological structure of the leaves, and positioning scab space distribution; s2, acquiring spectral reflection information of apple leaves, quantifying biochemical components of chlorophyll a and carotenoid, and constructing a vegetation index feature set sensitive to diseases; s3, apple tree canopy temperature information is collected, a transpiration anomaly detection model is constructed, and multi-modal fusion of thermal infrared and point cloud data is realized; and S4, integrating spatial distribution, spectral reflection and thermal radiation information, constructing a five-dimensional phenotypic characteristic matrix model, and comprehensively analyzing apple phenotypic characteristics. According to the method, the recognition accuracy under the conditions of blade shielding, uneven illumination, blade posture change and environment interference can be remarkably improved.

The embodiment of the invention provides a coal field fire area gas and particulate matter monitoring method, device and equipment and a storage medium, and the method comprises the steps: carrying out the temperature inversion of thermal infrared remote sensing image data based on a preset temperature inversion algorithm, and obtaining a temperature abnormal region; based on a pre-constructed hyperspectral detection model, performing inversion on the soil moisture data, the chlorophyll content data and the vegetation coverage data to obtain a surface vegetation growth condition; based on the temperature abnormal area and the earth surface vegetation generation condition, a preset judgment model is adopted for judgment, and the fire combustion stage is determined; based on the acquired atmospheric monitoring data, determining a two-dimensional distribution condition of gas and particulate matters in each fire combustion stage; and based on the two-dimensional distribution condition, performing overlay analysis on the atmospheric monitoring data at different heights in the temperature abnormal region to obtain the three-dimensional distribution condition of the gas and the particulate matters. Therefore, the accurate judgment of the combustion condition of the fire area is improved, and the accuracy and reliability of gas and particulate matter monitoring are improved.

The invention relates to the technical field of plant drought tolerance, in particular to a plant drought tolerance related protein FYVE4 and application of a coding gene thereof. By inhibiting expression or protein activity of the FYVE4 gene in the plant, the survival rate of the plant under drought stress is increased; according to the method, in genetic improvement of arabidopsis thaliana, after genetic transformation is conducted through an agrobacterium-mediated method or a gene gun method, the stomata closing response speed and chlorophyll accumulation amount of crops can be effectively increased, and therefore plants with stable genetic drought resistance characters are established; the technical scheme is particularly suitable for constructing a water-saving agricultural system, has very important significance for improving crops and cultivating drought-resistant crops, and is suitable for popularization and application.

The invention relates to the technical field of image recognition, in particular to a red tide algae recognition method based on a hyperspectral image, and the method comprises the following steps: calculating a gradient value of each pixel in a spatial dimension and a spectral dimension according to an original hyperspectral data cube, setting a diffusion coefficient for diffusion, and obtaining a three-dimensional fidelity hyperspectral data cube. According to the method, the gradient change trends of the spatial dimension and the spectral dimension are extracted at the same time pixel by pixel, and the regional differentiation diffusion operation is matched, so that the expression ability of the alga plaque boundary and the consistency of the internal spectral characteristics are enhanced, and the fidelity of the hyperspectral data in fine-grained region identification is improved; upper convex hull calculation of a spectral reflectivity curve and construction of a continuous spectrum background baseline are introduced, and characterization of pigment absorption morphological characteristics is enhanced; and in combination with the characteristic absorption wave bands of chlorophyll and phycobiliary, the synergistic weighted response to the distribution of the algae pigment is realized, and the sensitivity and selectivity of pigment recognition are improved.

The invention relates to a precision agriculture monitoring system and method based on multispectral imaging. The system and method are applied to real-time monitoring of crop physiological parameters and variable fertilization decision making. The system comprises an unmanned aerial vehicle imaging module, an edge computing unit and a cloud analysis server. The unmanned aerial vehicle module is provided with a multispectral filter wheel, a three-axis holder and an RTK positioning device and is used for acquiring a high-resolution crop image; the edge calculation unit integrates a radiation correction module, an image splicing module and a canopy segmentation module to realize on-site preprocessing; the cloud server runs a deep learning model and a feature fusion mechanism, outputs estimation of parameters such as nitrogen, chlorophyll and moisture, and generates a high-resolution fertilization prescription map. In the aspect of the method, dynamic monitoring of the nitrogen content of crops is realized through route planning, data synchronization, radiation normalization, multi-source feature fusion and time sequence prediction. The system supports online updating and ground verification of the model, has high precision, low delay and large-area operation capability, and is suitable for intelligent agriculture and precise fertilization scenes.

The invention discloses an intelligent greenhouse environment control method and system, particularly relates to the technical field of greenhouse control, and is used for solving the problem that the layering phenomenon is difficult to identify in the existing greenhouse carbon dioxide concentration monitoring. Modeling and identifying carbon dioxide concentration and gas flow distribution data in the greenhouse based on a clustering analysis algorithm to judge whether a carbon dioxide layering phenomenon exists or not; when a layering phenomenon exists, airflow velocity field characteristics are extracted through an optical flow method, a velocity field image is analyzed in combination with a convolutional neural network, and airflow vortex morphological characteristics of a local area of the greenhouse are identified; calculating the information entropy of the concentration change in combination with Shannon entropy, analyzing the diffusion path in combination with a dynamic programming algorithm, and evaluating the complexity of the diffusion path of the layered region; further identifying an eddy current risk area; the chlorophyll distribution characteristics of crops are analyzed through a spectral analysis algorithm, the growth abnormality is evaluated by combining carbon dioxide concentration data, and the abnormality is used as an auxiliary condition to precisely regulate and control ventilation and carbon dioxide supplementation.

A method for early warning of algal bloom levels based on an Ordinal Forests model includes the following steps: S1, preprocessing water quality data from a system for online monitoring of water quality and water ecology; S2, determining an algal bloom level according to a chlorophyll a value of the pre-processed water quality data; S3, using a resampling method to solve the problem of imbalanced algal bloom level data, and synthesizing a dataset of balanced algal bloom levels; and S4, taking the newly synthesized dataset in the S3 as an input variable, constructing a model for early warning of algal bloom levels based on the Ordinal Forests model, and performing early warning of algal bloom levels through the trained model for early warning of algal bloom levels.

The invention discloses a plant growth monitoring system, and relates to the technical field of plant growth monitoring, and multi-parameter monitoring equipment in the system comprises a main body, a supporting rod, a data processing module, a fixed sensor group and a sensor expansion module interface, atmospheric temperature, atmospheric humidity, atmospheric pressure, carbon dioxide content, illumination intensity, rainfall, wind speed, wind direction, soil temperature, soil humidity, soil conductivity, short-wave radiation flux, long-wave radiation flux, photosynthetically active radiation flux, multiband solar spectrum, plant reflection spectrum, dynamic crop coefficient, canopy temperature, latent heat flux and net radiation flux can be obtained; according to the method, various plant growth monitoring parameters such as the vegetation index, the chlorophyll index and the effective accumulated temperature are normalized, so that more plant growth parameters can be obtained, and more accurate plant growth information is provided.

The invention discloses engineering chlamydomonas reinhardtii for synthesizing high-value pigment and a construction method and application thereof.An engineering chlamydomonas reinhardtii strain Blup-BKT-CHYB-CHLC is preserved in the China General Microbiological Culture Collection Center on September 1, 2025, and the preservation number is CGMCC NO.46634. The content of astaxanthin in the constructed engineering chlamydomonas reinhardtii strain reaches 25.5 mg / L, and the content of astaxanthin in the engineering chlamydomonas reinhardtii strain reaches 9.5 mg / L; in addition, the light energy utilization efficiency is remarkably enhanced, the stress resistance is improved, very strong oxidation resistance is achieved, the accumulation of astaxanthin endows the engineering strain with highlight tolerance, the yield of high-value pigments is increased, and the biomass of the engineering strain is greatly superior to that of a wild strain under the highlight condition. In addition, the chlorophyll c synthase is applied to the reconstruction of a pigment system of the chlamydomonas reinhardtii for the first time, a frontier tool is quickly converted, and a molecular mechanism of'chlorophyll c-astaxanthin-blue pigment 'for synergistically regulating the photosynthetic efficiency of the chlamydomonas reinhardtii and product synthesis is clarified for the first time. The research blank of pigment system coupling regulation and control of photosynthesis and metabolism in the chlamydomonas reinhardtii is filled.

The invention discloses an intelligent algae control method and device based on target recognition, and relates to the technical field of wastewater biological treatment. According to the method, algal bloom severity identification is carried out based on the average concentration of chlorophyll a, algae identification is carried out according to images at different positions, algae identification results are obtained, the algae identification results of the images at different positions are counted, the number of different types of algae in a target algae control water area is obtained, and the algal bloom severity is determined. According to the method, the hydration severity, the number of the different types of algae and the growth rate can be automatically monitored, different algae control schemes are set according to the different hydration severity, the number of the different types of algae and the growth rate, and the algae control efficiency is improved. The functions of all-weather fixed-point water quality monitoring, water quality data analysis and early warning, algae outbreak early growth inhibition and the like of an unmanned ship algae control system are achieved, and efficient targeted algae control is completed.

The invention discloses a method for maintaining continuous photosynthetic hydrogen production of microalgae under ultrahigh light intensity, and belongs to the technical field of biological energy sources. The method comprises the following steps: culturing microalgae: transferring microalgae species into a TAP culture medium, and carrying out illumination culture until the number of microalgae cells reaches a logarithmic phase; the preparation method comprises the following steps: synthesizing a poly (N-isopropylacrylamide-butyl acrylate) copolymer, namely dissolving N-isopropylacrylamide, butyl acrylate and an azodiisobutyronitrile solution in anhydrous tetrahydrofuran, removing oxygen for 30 minutes under the action of magnetic stirring, and reacting for 24 hours at the temperature of 50 DEG C after the oxygen removal is finished, so as to obtain PNIPAM-BA (poly (N-isopropylacrylamide-butyl acrylate)-BA (poly (N-isopropylacrylamide-butyl acrylate))-BA (poly (N-isopropylacrylamide)-BA); the method comprises the following steps: taking a microalgae solution, centrifugally collecting microalgae, dispersing the microalgae in an HM culture medium, adding PNIPAM-BA and graphene oxide, and continuously illuminating at 25 DEG C under the light intensity of 2000 [mu] mol photons.m <-2 >. S <-1 >; the sunlight-driven intelligent microalgae photoreactor is constructed through graphene oxide and PNIPAM-BA, the survival rate and chlorophyll content of microalgae under the ultrahigh light intensity can be maintained, and the microalgae can maintain high-rate photosynthetic hydrogen production under the ultrahigh light intensity of 2000 micromoles photons * m <-2 > * s <-1 >.

The invention discloses a preparation method and application of an iron-based metal organic framework material / carbon nitride nitrogen fixation photocatalyst in the field of photocatalytic materials, the iron-based metal organic framework material / carbon nitride nitrogen fixation photocatalyst is prepared by taking ferric chloride, terephthalic acid and graphite phase carbon nitride as raw materials through a hydrothermal reaction, a heterojunction is constructed through a solvothermal method, uniform loading of MIL-53 (Fe) on the surface of b-C3N4 is achieved, and the photocatalytic performance of the photocatalyst is improved. And the formed heterogeneous interface significantly improves the photon-generated carrier separation efficiency. The obtained MIL-53 (Fe) / b-C3N4 photocatalytic composite material has good visible light response characteristic and excellent photocatalytic nitrogen fixation performance, and is safe, non-toxic, green and environment-friendly. The produced ammonia water can be directly absorbed by wheat, so that the chlorophyll content of leaves is increased by 11-13%, the plant height is increased by 19-21%, and a novel photocatalysis scheme is provided for green agriculture.

This invention belongs to the field of biomass subcritical fertilizer production, specifically relating to a mobile photovoltaic-powered subcritical fertilizer production system and its product application aimed at reducing nitrogen and phosphorus emissions, increasing efficiency, and resource-based disposal of high-moisture waste. It includes a solar preheating system, a photovoltaic power supply system, an auxiliary heating system, a hydrothermal reaction system, and a post-treatment system. The photovoltaic power supply system and auxiliary heating system are connected to the electrical load of the hydrothermal reaction system; the hydrothermal reaction system is connected to both the solar preheating system and the post-treatment system. This invention utilizes electrodialysis technology to desalinate the hydrothermal charcoal before returning it to the field. Through electrodialysis, the desalination rate of the charcoal reaches 45%-90%, and the conductivity of the desalinated charcoal is 1.40-6.75 mS / cm. The desalinated charcoal significantly increases crop yield; chlorophyll content increases by 25.5-67.5%, fresh weight increases by 18.5%-175%, and lettuce root length increases by 37.0%-98.2%. The high degree of desalination avoids the negative effects of salt stress on lettuce roots.

The present invention relates to the technical field of the preparation of Cr3+-doped fluorescent materials. Disclosed are the preparation of a Cr3+-doped fluorescent material and the use thereof in a plant light supplement lamp. The preparation comprises the following steps: weighing a matrix raw material and a Cr3+-containing raw material according to the stoichiometric ratio, and respectively grinding and then storing same in a dryer; placing the raw materials into a ball milling tank, and subjecting same to planetary ball milling for 3-6 hours; transferring same to an alumina crucible, and pre-sintering same for 2-5 hours; after secondary ball milling, adding a fluxing agent thereto; placing same in a high-temperature furnace and sintering same for 6-10 hours, and furnace cooling same or cooling same under the protection of an inert gas; and firstly coarsely crushing same, then grinding same to 10-30 μm, alternately cleaning same with deionized water and ethanol, drying same in an air dry oven, detecting and screening the product by using a fluorescence spectrophotometer, an X-ray diffractometer and a scanning electron microscope, and remanufacturing unqualified products after adjusting process parameters according to detection results. The present invention has a high light-emitting efficiency, and the spectrum thereof is suitable for plant photosynthetic absorption. When being applied to a light-supplementing lamp, plant growth can be promoted, the plant height and the chlorophyll content are significantly increased, and the natural spectrum is simulated, such that the material is suitable for multiple cultivation scenarios.

The invention discloses a plant leaf physicochemical parameter inversion method based on spectrum two-dimensional characterization and multi-task deep learning, and belongs to the technical field of plant physiological detection. The method comprises the following steps: constructing a hyperspectral plant leaf data set; preprocessing a hyperspectral plant leaf data set; reconstructing the preprocessed one-dimensional spectral vector into a two-dimensional image; taking the generated two-dimensional image as input, constructing a multi-task learning model, and performing training and evaluation; and applying the trained model to a test set to obtain an inversion result of the physical and chemical parameters of the leaf. The method solves the problem of parameter inversion interference caused by the same-spectrum foreign matter phenomenon, achieves the precise modeling of local fine feature extraction and global context relation, solves the inversion precision bottleneck caused by spectral response overlapping among parameters, improves the distinguishing capability of the model for easily confused parameters such as chlorophyll and carotenoid, and improves the accuracy of the model. And finally, high-precision and robust synchronous inversion of the physical and chemical parameters of the plant leaves is realized.

The invention relates to the technical field of scenic spot plant growth monitoring, and particularly discloses a scenic spot plant growth intelligent monitoring method and system, and the method comprises the steps: carrying out the multi-band image data collection of ornamental vegetation, and obtaining leaf spectrum purification data; extracting wave band response curves of anthocyanin, carotenoid and chlorophyll according to the spectral purification data to obtain pigment wave band characteristic data; performing spectrum aliasing sensitivity analysis on the pigment wave band characteristic data to obtain spectrum aliasing nonlinear characteristic data; carrying out differentiation attribution calculation on a wave band aliasing region between the healthy purple leaves and the iron-deficient yellowing to obtain aliasing difference attribution data; and finally obtaining vegetation pigment unmixing mapping data. Performing physiological status difference identification on the pigment unmixing mapping data to obtain a vegetation health monitoring result; the precision, high efficiency and foresight of scenic spot vegetation monitoring are technically guaranteed, the technical requirements of modern scenic spot vegetation management are met, and losses caused by vegetation health problems are effectively reduced.

The invention discloses an application method for realizing rice nitrogen fertilizer substitution by utilizing a photocatalytic nitrogen fixation material in the technical field of agricultural production. The application method comprises the following steps: S1, pretreating and proportioning a catalyst; s2, reaction container deployment and illumination optimization; s3, carrying out photocatalytic reaction and ammonium nitrogen generation; s4, synergistically applying base fertilizer and additional fertilizer; s5, monitoring and feeding back nitrogen utilization efficiency; the performance of the photocatalyst is improved by adopting a surface doping and composite modification technology, atmospheric nitrogen can be stably and efficiently converted into ammonium nitrogen which can be directly absorbed by rice under a complex environmental condition, and the nitrogen conversion rate and the nitrogen supply efficiency are improved; the AI algorithm is combined with the chlorophyll meter, the soil nitrogen sensor and the water ammonia nitrogen concentration data to predict and dynamically adjust the nitrogen demand of the rice in real time; a closed loop of nitrogen fixation-conversion-absorption-recovery is formed through a segmented application strategy of base fertilizer and topdressing in combination with physical and chemical synergism and a biochar improvement technology.

The invention belongs to the technical field of microorganisms, and particularly discloses bacillus impetus DXGZ2-6 as well as a microbial agent and application thereof. The preservation number of the bacillus haynesii DXGZ2-6 disclosed by the invention is CGMCC (China General Microbiological Culture Collection Center) No.33710. The bacillus haynesii DXGZ2-6 disclosed by the invention has the advantages that the preservation number is CGMCC No.33710 The bacillus marinensis can generate various enzymes, can effectively promote plant growth, slows down decomposition or transfer of chlorophyll of plant leaves, and can effectively inhibit various phytopathogens.

The invention discloses a crop nutrient information inversion method based on an ELM inversion model, and the method comprises the following steps: obtaining leaf physicochemical parameters, canopy structure parameters, ground parameters and angle information of related crops, and constructing a rice canopy reflectivity simulation data set through an RPIOSL-UBM model; constructing an initial lookup table; constraining the initial lookup table; constructing a comprehensive data set by combining actual measurement spectrum data of related crops; carrying out ELM inversion model training in combination with a whale optimization algorithm; and carrying out nutrient information inversion on crops to be detected through the optimized ELM inversion model to obtain corresponding leaf area index LAI, normalized vegetation index NDVI, chlorophyll, nitrogen and moisture nutrient indexes, and generating a nutrient map. According to the method, the RPIOSL-UBM model is coupled, and ELM parameters are optimized in combination with actual measurement data constraints and the whale algorithm, so that high-precision and strong-generalization inversion of multi-nutrient indexes of crops is realized, and the method can be suitable for multi-growth-stage monitoring requirements in a complex field environment.

The invention provides an automatic sugarcane identification method cooperating with soil-green-light time sequence linkage, and the method comprises the steps: carrying out the detection of a cultivation disturbance event: quantifying the dynamic change of a soil structure in a spring cultivation period based on the radar texture heterogeneity induced by sugarcane ridging cultivation; canopy steady-state event detection: based on the scattering mechanism stability of the sugarcane closed canopy, quantifying the biomass steady state in the elongation mature period through normalized variance; group light competition event detection: based on chlorophyll absorption competitive change caused by sugarcane close planting, time sequence extreme value positioning and cumulative intensity analysis are carried out; multi-source collaborative decision making: coupling radar structure stability and optical physiological response characteristics, and fusing event detection results through linkage discriminant indexes; outputting the spatial distribution of the sugarcanes; wherein the detection of the farming disturbance event, the canopy steady state event and the group light competition event is based on the time sequence data linkage analysis of the whole growth period of the sugarcane.

The application belongs to the technical field of microorganisms, and specifically discloses a Bacillus haynesii DXGZ2-6, a bacterial agent thereof and application thereof. The Bacillus haynesii DXGZ2-6 has a preservation number of CGMCC No.33710. The Bacillus haynesii can produce various enzymes, effectively promote plant growth, slow down the decomposition or transfer of chlorophyll in plant leaves, and effectively inhibit various plant pathogenic bacteria.

The invention provides a mulberry disease and insect pest early warning model construction and precise pesticide application method, belongs to the technical field of agricultural disease and insect pest prevention and control, and aims to solve the problems of pesticide waste and excessive pesticide residues caused by traditional manual monitoring early warning lag and extensive pesticide application. Precise prevention and control are realized through multi-source data fusion and an intelligent decision technology. According to the technical scheme, the method comprises the steps that a high-precision sensor (the temperature is + / -0.5 DEG C, and chlorophyll is + / -5SPAD) is deployed to collect environment, physiology and pest data, and after cleaning and noise reduction, a machine learning model (50-100 random forest decision trees are arranged, and the optimization target accuracy is larger than or equal to 85%) is constructed; and delimiting a key pesticide application area (the pest and disease damage density is greater than or equal to 10 / hundred plants or the infection rate is greater than or equal to 15%) according to a model result.

The invention discloses a saline-alkaline tolerant bacterium strain and application thereof. The saline-alkaline tolerant bacteria strain is selected from any one or more of the following bacteria: Pseudoclavibacter sp., the preservation number of which is CGMCC No.32646, the preservation number of which is CGMCC No.32646, the preservation number of which is CGMCC No.32646, and the preservation number of which is CGMCC No.32646. The preservation number of the providencia sp. Is CGMCC (China General Microbiological Culture Collection Center) No.32647, and the preservation number of the providencia sp. Is CGMCC No.32647. The preservation number of the planococcus sp. Is CGMCC No.32648, the planococcus sp. Is preserved in the China General Microbiological Culture Collection Center (CGMCC) on November 15, 2024, and the preservation address is No.3, Yard 1, Beichen West Road, Chaoyang District, Beijing, China. The bacillus amyloliquefaciens and the bacillus amyloliquefaciens have the capabilities of fixing nitrogen The saline-alkaline tolerant growth of plants can be obviously promoted, the content of chlorophyll and proline in rice is increased, the activity of antioxidant enzyme is improved, and the content of malondialdehyde is reduced; in addition, the providencia has nitrogen fixation capacity, the pseudoochrobactrum pseudococcum has iron production capacity, and the motility coccus has iron production and phosphorus solubilizing capacity.