795 results about "Chlorophyllin" patented technology

The invention relates to the technical field of water environment risk monitoring, in particular to a method for evaluating the algal bloom risk of a water body. The method comprises the following steps: collecting historical monitoring data of a to-be-evaluated water body, wherein the historical monitoring data comprises blue-green algae abundance data, water quality data and hydrological data; analyzing the correlation between the cyanobacteria abundance or chlorophyll a concentration and the water quality and hydrological data of the to-be-evaluated water body; hydrological and water quality parameters with the highest correlation with the cyanobacteria abundance or chlorophyll a concentration are screened out; hydrology and water quality parameters of a water body to be evaluated are taken as predictive variables, and cyanobacteria abundance or chlorophyll a concentration is taken as a response variable to construct a Bayesian network model; the weight of each parameter in the Bayesian network model is calculated, and the algal bloom risk probability that the cyanobacteria abundance exceeds a specific threshold value under the given parameter condition is calculated according to the weights. According to the invention, the scene-based probability deduction of the stable period and the dynamic period is realized through the double-branch Bayesian network model, so that the accuracy and timeliness of algal bloom risk assessment are improved.

The invention discloses a method and a system for adjusting a tea leaf fixation process and a medium, and aims to realize cooperative regulation and control of enzyme activity and aroma substances in a fixation process. The method comprises the following steps: collecting multi-modal sensing information in a fixation process, and carrying out feature extraction on the multi-modal sensing information to obtain multi-dimensional features; dynamically constructing an LSTM prediction model based on the multi-dimensional features, and outputting an enzyme activity prediction value sequence; the enzyme activity predicted value sequence is input into a fuzzy PID controller for reinforcement learning optimization, a temperature-rotating speed adjusting instruction is generated, and adjusting parameters of the controller are dynamically updated through a reward function of the chlorophyll retention rate and the energy consumption ratio; according to the temperature-rotating speed adjusting instruction, adjusting the hot air temperature of each temperature area of the enzyme deactivation chamber, the rotating speed of the enzyme deactivation cylinder and the axial segmentation wind speed; and collecting fixation end point judgment information, and terminating fixation when a preset condition is met. According to the invention, the problems of unstable quality and overhigh energy consumption caused by parameter solidification in the traditional enzyme deactivation process are solved, and dynamic optimization and accurate control of the enzyme deactivation process are realized.

The invention discloses a water source chlorophyll a concentration prediction model design method based on machine learning. The method comprises the following steps: acquiring chlorophyll a concentration data in a to-be-predicted region for a continuous period of time; carrying out data preprocessing on the chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing a concentration prediction model, carrying out data preprocessing on chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing different concentration prediction models, and inputting the processed chlorophyll a concentration data and physicochemical parameters into the prediction models to obtain a chlorophyll a concentration data prediction result; and comparing prediction results of different prediction models, and determining the prediction model. According to the prediction model design method, the WT-GRU model is adopted to preprocess the data through wavelet transform, the wavelet transform effectively extracts key time scale characteristics through signal decomposition, and the accuracy of chlorophyll a concentration prediction is remarkably improved.

The invention discloses a regional GPP assimilation inversion system fusing foundation-multi-source satellite chlorophyll fluorescence, and belongs to the technical field of terrestrial ecosystem carbon cycle monitoring. The signal unmixing and simulation generation module is used for unmixing the total fluorescence of the mixed pixels into pure chlorophyll fluorescence (SIF) and driving an ecological model to generate SIF and GPP simulation values based on environmental stress data; the state component decoupling module is used for decoupling the total GPP analog value into a light response fast component, a light response slow component and a light response memory component; the state evolution prediction module is used for predicting dynamic evolution of the slow component and the memory component; and the self-adaptive assimilation inversion module is used for performing inversion on the region GPP by taking the slow component and the memory component as state variables, taking the SIF observation value as observation data and combining a prediction result of the state evolution prediction module. According to the method, systematic deviation caused by model simplification or data singleness in a traditional method is overcome.

The invention discloses a corn yield prediction method and system based on chlorophyll fluorescence and deep learning, and the method comprises the steps: obtaining the yield prediction related data of a target region for many years in the past, and carrying out the preprocessing of the yield prediction related data; screening yield prediction related data of the hot and dry years according to a preset threshold value, and constructing a training data set under a stress condition based on the yield prediction related data of the hot and dry years; constructing a deep neural network model, and training, verifying and testing the deep neural network model by adopting the training data set to obtain a corn yield prediction model based on DNN; and inputting to-be-predicted data into the trained corn yield prediction model, outputting a corn yield prediction result, and generating a spatial distribution diagram. According to the method, the spatial distribution goodness of fit between a prediction result and official statistical data is remarkably superior to that of a traditional method, and a quantifiable and low-cost solution is provided for precise agricultural decision making under the extreme climate condition.

The invention relates to the technical field of environmental monitoring, in particular to a forest vegetation coverage growth monitoring system and method based on big data, and the method comprises the steps: extracting vegetation patch chlorophyll dispersion, texture direction and near-infrared uniformity to generate a microstructure feature set, comparing homogeneity intensity, a reference value and an adjacent condition marker to generate a connected state identifier, and carrying out the analysis of the connected state identifier; and according to the soil moisture capacity and the illumination radiation quantity, abnormal items of chlorophyll reflectivity deviation degree and near-infrared band concentration degree are removed. According to the method, by analyzing the chlorophyll ratio of the sub-pixels and the neighborhood texture, the near-infrared uniformity is quantified, and the heterogeneity precision is improved. And the multi-dimensional homogeneity strength is established by standardized parameters. And establishing a communication criterion by fusing edges, terrains and homogeneity. And performing dynamic screen spectrum abnormity early warning. And integrating the water retention, illumination and correction data of the soil to establish a dynamic model, and improving the prediction temporal-spatial resolution.

The invention discloses a vegetable disease incubation period detection method and system based on bimodal time sequence collaborative fusion, and the method comprises the steps: obtaining a leaf image through building an acquisition environment, constructing a training sample data set, and constructing a downy mildew incubation period spectral feature adaptive enhancement (AW-FPF) module; the method comprises the following steps: decomposing a hyperspectral signal into low-frequency and high-frequency components through spectrum time sequence adaptive wavelet decoupling, obtaining an enhanced feature tensor through spectrum multi-scale pathological feature frequency-time dual-path aggregation fusion frequency domain and time domain paths, and obtaining a first feature sequence through weighted screening by using a multi-head attention mechanism; a downy mildew incubation period prediction (HyChl-TFNet) model containing a hyperspectral branch, a chlorophyll fluorescence parameter branch, a feature fusion branch and a classifier is constructed, bimodal features are processed and fused to output a day number prediction result, accurate recognition of the downy mildew incubation period is achieved, the detection precision can be controlled to the day number level, and the detection accuracy is improved. And an accurate time basis is provided for early prevention and control of diseases.

The invention provides application of molybdenum disulfide nanoparticles in preparation of a tomato leaf fertilizer, and particularly relates to the technical field of tomato fertilizers. The molybdenum disulfide nano-particles act on tomato plants in a foliage spraying manner, so that the growth of the tomato plants is promoted and / or the fruit quality is improved. The molybdenum disulfide nanoparticles are used as the foliar fertilizer to significantly increase the plant height and stem diameter of tomatoes, increase the chlorophyll content and trace element content of plants, increase the vitamin C content, soluble sugar content and soluble solid content of tomato fruits, and significantly increase the accumulation of Mo and S of plants through the sulfur-molybdenum synergistic effect, so that the yield of the tomatoes is increased. A scientific basis is provided for reasonable application of the nano molybdenum fertilizer, and a way is opened up for high-yield and high-quality cultivation of tomatoes.

The invention belongs to the technical field of remote sensing information processing, and particularly relates to a water chlorophyll a concentration inversion-oriented remote sensing time-space-spectrum fusion method, which realizes improvement of spatial resolution of a chlorophyll a concentration inversion sensitive wave band by fusing complementary information of different sensors on time-space-spectrum resolution. The method comprises the following steps of: establishing a water body chlorophyll a concentration inversion-oriented MSI and OLCI space-time spectrum fusion deep learning network, embedding a time sequence dynamic adjustment module, and establishing a water body chlorophyll a concentration inversion-oriented MSI and OLCI space-time spectrum fusion deep learning network for water body chlorophyll a concentration inversion. According to the method, the network can intelligently combine time and space features to generate a more accurate prediction image, an Adaboost machine learning joint inversion model is constructed based on time-space-spectrum fusion data and corresponding limited ground station data, and high-precision remote sensing inversion of the concentration of chlorophyll a is realized.

The invention discloses a potato cold resistance identification method and system, and relates to the technical field of cold resistance identification. The method comprises the following steps: cultivating potato tissue culture seedlings in a standardized manner, and randomly dividing the seedlings into a low-temperature stress group and a normal-temperature control group; performing standardized low-temperature treatment including slow descent and constant-temperature stages on the stress group; the method comprises the following steps: acquiring multi-source data such as chlorophyll fluorescence parameters, canopy thermal imaging, digital image phenotypes and biochemical indexes, and forming a unified data set after derivative index calculation and standardized fusion; inputting the fusion data into an improved neural network model, training the model through a particle swarm optimization algorithm, and outputting a quantitative potato cold resistance index; automatically evaluating the cold resistance grade according to the potato cold resistance index and generating an identification report; standardization of the identification process, fusion analysis of multi-dimensional indexes and intelligent result analysis are achieved, and the accuracy, efficiency and practicability of potato cold resistance identification are remarkably improved.

The invention provides a water chlorophyll a concentration inversion method and system based on multi-modal data and a lightweight model, and relates to the technical field of water environment remote sensing evaluation. The method comprises the following steps: firstly, acquiring a Gaofeng No.5 satellite remote sensing image, a sentinel No.3 satellite image and ground actual measurement data, and completing image preprocessing and water body pixel extraction; constructing a hyperspectral index and an aquatic vegetation index, and fusing the hyperspectral index and the aquatic vegetation index with the water body temperature, the pH environmental factors and the spectral reflectivity to form a multi-dimensional feature sample set; a core feature subset is obtained through random forest and XGBoost coupling feature selection, and a lightweight student model is trained based on knowledge distillation; and constructing a to-be-predicted feature sample for the to-be-predicted time phase image and the environment factor, inputting the to-be-predicted feature sample into the lightweight student model to obtain a chlorophyll a concentration predicted value, and generating a spatial distribution map and a quality control map layer. According to the invention, high-precision, low-redundancy and efficient deployment chlorophyll a concentration inversion is realized.

The invention relates to the technical field of agricultural quantitative remote sensing and intelligent agriculture, and discloses a multi-source remote sensing data radiation correction and chlorophyll content cross-platform inversion method, which comprises the following steps: synchronously obtaining ground actual measurement, unmanned aerial vehicle and satellite remote sensing data, calculating vegetation indexes, and screening sensitive characteristic variables by using Pearson correlation analysis; establishing a radiation correction model by adopting a ratio averaging method, performing radiation normalization processing on satellite data by taking unmanned aerial vehicle data as a reference, and eliminating sensor response differences; an SPAD inversion model is constructed and optimized based on Z-score standardization, the SPAD inversion model is migrated and applied to corrected satellite data, a regional scale chlorophyll distribution diagram is generated through pixel-by-pixel calculation, and then a differentiated farmland management strategy is formulated. According to the method, the difficulty of multi-source data collaboration is solved, the regional scale crop chlorophyll monitoring precision is improved, and scientific decision support is provided for large-area crop fertilization.

The invention provides a water chlorophyll a concentration inversion method based on a hyperspectral remote sensing image, which relates to the technical field of remote sensing inversion and comprises the steps of image preprocessing, water pixel extraction, actually measured sample space registration, spectral feature construction, model score optimization, concentration prediction and the like. According to the method, an optimal scheme is screened in a feature-model combination through cross validation and a unified scoring function, the optimal scheme is applied to whole image calculation, and a chlorophyll a concentration spatial distribution map and a matched quality control map layer are output. The method has the characteristics of high precision, self-adaption and high engineering practicability, and is suitable for a water quality remote sensing inversion scene driven by multi-source hyperspectral data.

The invention belongs to the technical field of dressing preparation, and particularly relates to a preparation method of a photocuring methacrylated gelatin hydrogel dressing, which comprises the following steps: step 1, dissolving methacrylated gelatin in a phosphate buffer solution to prepare a methacrylated gelatin solution with the mass concentration of 5-15%; 2, adding a natural photoinitiator into the solution obtained in the step 1, and uniformly stirring and mixing; 3, adding the traditional Chinese medicine active ingredient powder, and stirring until the powder is uniformly dispersed to obtain a mixed solution; and 4, pouring the mixed solution into a mold, and curing and molding under the irradiation of visible light. Chlorophyll or riboflavin is used as a natural photoinitiator and is directly dispersed in a methacrylated gelatin solution in a physical blending mode, a curing light source enables chlorophyll to completely avoid a chemical initiator, meanwhile, the process is simplified into physical stirring to replace nanoparticle synthesis and modification, visible light reduces the risk of tissue burn, the production cost is reduced, and the method is suitable for industrial production. The method is suitable for primary medicine.

The invention belongs to the technical field of agricultural remote sensing, and discloses a method for evaluating cold resistance of pennisetum alopecuroides based on unmanned aerial vehicle multispectral images, which comprises the following steps: acquiring agronomic characters related to cold resistance, such as chlorophyll content, overwintering rate and the like of the pennisetum alopecuroides before and after low-temperature stress through manual measurement, and acquiring the multispectral images by using an unmanned aerial vehicle; the image is preprocessed, and various vegetation indexes such as DVI, NDVI and RVI are extracted; on the basis of agronomic character data, a comprehensive cold resistance index CRI is constructed by adopting principal component analysis and a membership function method; using the vegetation index as an independent variable and CRI as a dependent variable, and using a machine learning algorithm to construct a prediction model; and finally, realizing rapid and nondestructive evaluation on the cold resistance of a large-range pennisetum alopecuroides group by utilizing the model, and outputting a five-level cold resistance grading result according to a CRI value. According to the method, the problems of low efficiency and high difficulty of traditional manual evaluation are solved, high-throughput and precise cold resistance identification is realized, and reliable technical support is provided for cold-resistant breeding and cultivation management of pennisetum alopecuroides.

The invention discloses a virulence factor gene relative abundance prediction model based on water quality physicochemical indexes, and relates to the technical field of ecological environment protection, and the virulence factor gene relative abundance prediction model comprises the following steps: taking a plurality of river water samples, obtaining virulence factor gene relative abundance of the river water samples, and obtaining water quality physicochemical index data of the river water samples; temperature, redox potential, ammonia nitrogen, nitrate nitrogen, nitrite nitrogen, total nitrogen, total dissolved solids, pH, conductivity, chlorophyll-a, chemical oxygen demand, dissolved oxygen, total organic carbon, total phosphorus, organic nitrogen, phosphate, carbon-nitrogen ratio and organic nitrogen / total nitrogen are taken as explanatory variables, and relative abundance of virulence factor genes is taken as a target variable. And establishing a regression model by adopting a GBM algorithm in an h2o packet in R software, and predicting the relative abundance of the virulence factor gene of the river water sample to be detected by utilizing the prediction model. The research provides a brand new method for predicting the relative abundance of the virulence factor gene and analyzing the environmental response of the virulence factor gene, and the method is suitable for identification and interpretation of a high-dimensional nonlinear ecosystem with complex interaction.

The invention provides a method for cooperatively regulating and controlling chlorophyll-a concentration variability through vortex-upwelling, and relates to the technical field of ocean remote sensing and environmental monitoring. A multi-source satellite and reanalysis data set is obtained, and a stationary time sequence is constructed; identifying a dominant variation period in chlorophyll-a concentration, sea surface temperature and sea surface height anomalies, and extracting a monthly abnormal value sequence; performing complex empirical orthogonal function analysis on the sea surface height anomaly, extracting a dominant spatial variation mode of the sea surface height anomaly, and identifying a vortex distribution position and form; classifying and screening target vortexes according to vortex types and seasons, carrying out normalization processing on the chlorophyll-a concentration, aligning to vortex centers, and analyzing a spatial response relationship between the vortexes and the chlorophyll-a concentration; and drawing a density time-depth profile map and a CHL-SLA composite map by combining surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, and analyzing an influence mechanism of vortex-upwelling on chlorophyll-a horizontal distribution.

The invention provides a seawater chlorophyll a concentration three-dimensional remote sensing detection method, medium, equipment and product, and relates to the technical field of ocean remote sensing. The method comprises the following steps: acquiring satellite remote sensing data and photon counting laser radar data of a to-be-detected area; obtaining a seawater chlorophyll a concentration profile based on the diffusion attenuation coefficient; the method comprises the following steps: constructing an improved VGGNet model which comprises a multi-branch VGGNet, replacing standard convolution in the VGGNet with depth separable convolution, and introducing an SE attention mechanism into a backbone network; the time and position variables, the satellite remote sensing data, the photon density, the backscattering coefficient, the diffusion attenuation coefficient and the water depth serve as input variables of the model, the seawater chlorophyll a concentration profile serves as a target variable, and the model is trained; and inputting new satellite remote sensing data into the trained model to generate three-dimensional distribution prediction of the seawater chlorophyll a concentration. The method can better extract the features in the data, improves the prediction precision of the model, and is suitable for a complex water body environment.

The invention discloses a potato seedling grading environment parameter dynamic optimization regulation and control method, and relates to the technical field of agricultural informationization. Based on an Internet of Things sensor deployed in a target area, stem diameter, chlorophyll content and plant height data of potato seedlings are collected, the seedlings are divided into weak seedlings, middle seedlings and strong seedlings by adopting K-means clustering, and the weak seedlings, the middle seedlings and the strong seedlings are classified into a weak seedling classification model, a middle seedling classification model and a strong seedling classification model; setting the duration of the initial light period; and carrying out gradient photoperiod test on each type of potato seedlings by taking the stem elongation rate and the chlorophyll content as constraint conditions. Through grading photoperiod modeling and ant colony algorithm dynamic optimization, the limitation of traditional fixed photoperiod regulation and control is broken through, the initial photoperiod range is set based on the seedling physiological difference, and the photoperiod-growth rate response curved surface model is constructed in combination with the stem elongation rate and chlorophyll content constraint conditions. The influence of different photoperiod combinations on seedling growth is quantified, accurate matching of photoperiods and seedling requirements is ensured, excessive growth or premature senility is avoided, and the growth rhythm stability and the resource utilization efficiency are improved.

The invention relates to the technical field of water environment remote sensing monitoring, solves the technical problem of systematic overestimation or underestimation under the condition of algae bloom outbreak or strong stratification due to the fact that a water body is regarded as an optical uniform monolayer parameter in a traditional method, and particularly relates to a stratified light field correction chlorophyll-a remote sensing inversion method and system for an eutrophic lake. Performing vertical type identification and three-layer layering by using multispectral / hyperspectral remote sensing reflectivity, a synchronous chlorophyll-a vertical profile and a diffusion attenuation coefficient, calculating light field weight and light path weighted concentration of each layer, constructing a layered light field correction coefficient, performing layered light field correction on the remote sensing reflectivity, and establishing an empirical chlorophyll-a inversion relationship; and generating a chlorophyll-a spatial distribution map and an algae bloom risk map. According to the method, the layered light field correction coefficient with clear physical significance is constructed to correct the water surface remote sensing reflectivity, so that the inversion precision and robustness under strong layering and complex optical conditions are improved.

The invention relates to an alfalfa salt tolerance prediction method based on artificial intelligence, and belongs to the technical field of data processing and prediction. The method comprises the following steps: collecting alfalfa bimodal time series data, and constructing a data set; processing the bimodal time sequence data by adopting a dynamic segmentation normalization method to obtain normalized bimodal time sequence data; constructing a medicago sativa salt tolerance prediction model, wherein the medicago sativa salt tolerance prediction model comprises a cross-modal interactive coding layer, a multi-scale feature pyramid module, a gating recombination module, a feature distillation module, a bidirectional gating unit and a classification module; the normalized bimodal time series data are adopted to train a model, and a trained model is obtained; and performing synchronous acquisition of soil conductivity and chlorophyll relative content on newly acquired alfalfa monitoring data, performing normalization processing, and inputting the data into the trained model to obtain a prediction result. The method can effectively enhance the accuracy and discrimination ability of salt tolerance classification.

The invention belongs to the technical field of ocean laser radar remote sensing detection, and particularly relates to an ocean subsurface water body element vertical structure inversion method and system and a computer program. The method comprises the following steps: carrying out preprocessing, distance correction and logarithm conversion on a laser radar echo signal; constructing an iterative hybrid inversion model to invert a water body attenuation coefficient and a particulate matter backscattering coefficient; performing scattering correction for multiple times; matching multi-source data to construct a deep learning model; and finally inverting the chlorophyll concentration and the vertical section of the granular organic carbon. According to the method, the multiple scattering effect of the water body and the influence of system parameters are comprehensively considered, data driving and a physical model are combined, high-precision and automatic marine water body element inversion can be achieved under the complex water quality condition, and the method is suitable for ecological environment monitoring and research of large-range and multi-type sea areas.

The invention provides a cotton yield prediction method and device, equipment and a medium, and relates to the field of cotton yield prediction, and the method comprises the steps: obtaining an RGB image and a multispectral image of to-be-predicted cotton in a flowering and boll-setting period; based on the RGB image and the multispectral image, determining the growth state of the to-be-predicted cotton and agricultural condition parameters of a cotton field where the to-be-predicted cotton is planted; inputting the agricultural condition parameters into a pre-trained chlorophyll prediction model to obtain predicted chlorophyll of the to-be-predicted cotton in a flowering and boll setting period; inputting the growth state and the predicted chlorophyll into a pre-trained cotton yield prediction model to obtain the predicted yield of the to-be-predicted cotton; wherein the cotton yield prediction model is obtained by training chlorophyll detection values and growth states of different varieties of cotton in historical flowering and boll-forming periods and yields of corresponding cotton in historical harvesting periods. The cotton chlorophyll content can be accurately predicted, the cotton yield can be efficiently predicted, and support is provided for cotton germplasm screening and accurate breeding.

The invention relates to the technical field of polymer compositions, and particularly discloses a degradable bio-based food packaging material and a preparation method thereof.The degradable bio-based food packaging material is prepared from polylactic acid, ITA esterified bacterial cellulose, CSL-ITA esterified bacterial cellulose, polycaprolactone glycol, acetyl tributyl citrate, a photoinitiator and tocopherol; the preparation method comprises the following steps: esterifying bacterial cellulose hydrogel with itaconic anhydride, grafting with sodium copper chlorophyllin, dehydrating with gradient ethanol, and drying with supercritical CO2 to obtain modified powder; according to the preparation method, the interfacial compatibility of the bacterial cellulose and the polylactic acid is enhanced through ITA esterification, the illumination antibacterial function is given by utilizing CSL grafting, the polycaprolactone glycol and the acetyl tributyl citrate are synergistically used for toughening and improving the brittleness, the photoinitiator is added, the photoinitiator is added for mixing, and ultraviolet crosslinking curing is performed after film forming. The tocopherol protects the stability of the antibacterial structure of the CSL, the water resistance is improved through photo-crosslinking, and the material is degradable and meets the food packaging requirement.

The invention belongs to the technical field of remote sensing image data processing, and relates to a multi-feature fusion coastal zone culture pond extraction method, which comprises the following steps: obtaining spectral features and polarization features based on an obtained Sentinel-1 image and an obtained Sentinel-2 image; calculating and evaluating an NDWI time sequence based on the NDWI to generate a time sequence synthesis NDWI image; a water body main body is obtained through the hierarchical feature fusion decision tree; obtaining morphological characteristics based on the water body object; obtaining the chlorophyll a concentration and the dynamic characteristic factor of the chlorophyll a concentration based on the Sentinel-2 image; and extracting a culture pond through a random forest classifier, and generating a culture pond spatial distribution diagram. According to the method, the spectral features, the polarization features, the morphological features, the chlorophyll a concentration and the chlorophyll a concentration dynamic feature factors are fused, and the decision tree and the random forest classifier are fused through the hierarchical features, so that the problems of low accuracy and poor stability of existing culture pond extraction are solved.

The invention belongs to the technical field of water ecology risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, and the method comprises the steps: obtaining remote sensing image data, meteorological data and water quality data of a target region, and carrying out the preprocessing; performing frequency domain feature extraction to generate a three-dimensional frequency domain feature vector; fusing the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector by using a multi-task deep learning network to obtain a feature map; obtaining an algae bloom binary segmentation probability graph and a continuous value distribution graph of chlorophyll a concentration based on the characteristic graph; generating an image semantic embedding vector by utilizing a semantic embedding head, and performing semantic alignment on the image semantic embedding vector by adopting a pre-trained knowledge graph to generate a research and judgment information text; and performing cross validation on the study and judgment information text, the algae bloom binary segmentation probability graph and the continuous value distribution graph of the chlorophyll a concentration to obtain a final algae bloom risk judgment result. According to the invention, the algal bloom risk remote sensing intelligent identification is realized.

The invention discloses a method for evaluating and screening salt tolerance of triticale germplasm resources in a germination stage, and belongs to the technical field of evaluation and screening of plant salt tolerance, salt stress concentration screening: triticale seeds are treated by NaCl solutions with different concentrations, indexes such as survival rate, plant height and overground part fresh weight are measured, the NaCl solution with the concentration of 200 mmol. L <-1 > is determined as the salt tolerance evaluation appropriate concentration, and the salt stress concentration is determined as the salt tolerance evaluation appropriate concentration. Salt tolerance evaluation test: carrying out salt stress treatment by adopting a 200mmol. L <-1 > NaCl solution, determining phenotypic indexes such as survival rate, plant height, fresh weight of overground part, root length and chlorophyll content, and physiological and biochemical indexes such as catalase, peroxidase and superoxide dismutase activity and malondialdehyde content, and carrying out principal component analysis, membership function analysis and clustering analysis to obtain a salt tolerance evaluation result. Calculating a salt tolerance comprehensive evaluation value, and screening salt tolerance triticale germplasm resources.

The invention relates to the field of gene engineering and molecular breeding, and discloses application of a ZmTCP5 gene in regulation and control of heat resistance of corn. According to the invention, three types of knockout mutants of the gene are obtained by using a CRISPR / Cas9 technology, and three overexpression materials are created. At normal temperature (25-28 DEG C), the material has no obvious phenotypic difference with a wild type; however, under high temperature stress (45 DEG C), the ZmTCP5 knockout mutant has enhanced heat resistance, reduced reactive oxygen species (ROS) accumulation, and significantly increased ROS scavenging enzyme activity, chlorophyll content and photochemical efficiency; on the contrary, the ZmTCP5 overexpression strain is more sensitive to heat stress, which indicates that the ZmTCP5 negatively regulates the heat resistance of the corn. The research provides a theoretical basis for analyzing a heat-resistant molecular mechanism of the corn, and also provides an important gene resource for creating a new germplasm of the heat-resistant corn.

The invention provides a fresh tobacco leaf chlorophyll content integrated model prediction method, medium and system, and belongs to the technical field of tobacco leaf detection.The method comprises the steps that transmission spectrums of fresh tobacco leaves with different maturity degrees are collected and subjected to black and white correction; dividing the leaf into six areas, collecting spectrums, and calculating an average value; carrying out multiple pretreatments on the original spectrum and dividing a data set; analyzing and establishing a relationship between the chlorophyll content and the spectrum based on a photosynthetic pigment absorption mechanism, and screening key wavelengths; the FMR-NSGA multi-objective optimization algorithm is fused with the F test and mutual information method to screen characteristic wavelengths; dynamically eliminating redundant features by using a recursive feature elimination method; establishing a parameter mapping function, and automatically determining a model hyper-parameter according to the wavelength importance index; a Stacking framework is constructed, hyper-parameters are dynamically optimized by adopting a grey wolf optimization algorithm, the prediction precision is improved, and the technical problem of rapid and accurate prediction of the chlorophyll content of the fresh tobacco leaves is solved.

The invention belongs to the technical field of satellite remote sensing application, and discloses an active and passive satellite remote sensing fused ocean three-dimensional chlorophyll field reconstruction method and system. According to the method, satellite-borne laser radar data is subjected to correction, signal processing and biological optical model inversion, and a chlorophyll a concentration vertical section along an orbit is obtained; forming a training set by the passive satellite observation optical variable and the marine environment variable of which the profile is matched with the space-time, so as to train a long short-term memory (LSTM) neural network model; and utilizing the trained model to reconstruct a three-dimensional chlorophyll a concentration field of a target area according to passive observation and environment variables of the target area. By fusing the advantages of a satellite-borne laser radar ICESat-2 satellite and a passive optical remote sensing satellite, three-dimensional chlorophyll a concentration field detection based on active and passive fusion remote sensing is developed, the structure and function of a marine ecosystem can be deeply known, and three-dimensional dynamic observation of the ocean is realized.