24 results about "Chlorophyll fluorescence" patented technology

The present application belongs to the technical field of pattern recognition and intelligent agriculture, and particularly relates to a crop disease and pest intelligent identification and crop condition perception method. The method first collects crop environment and image data through a multi-source perception network, extracts features using a physical information neural network, and introduces a physiological constraint operator containing a transpiration model and a chlorophyll fluorescence decay parameter therein. At the same time, micro-meteorological parameters are injected into the network as weight correction factors to establish the coupling relationship between environmental factors and physiological characteristics, so as to realize decoupling identification of physiological adversity and pathological infection. Finally, state evaluation is carried out in combination with a physiological adversity formula, and the identification result and crop condition report are output. The present application uses physical laws to decouple the identification logic, accurately distinguishes adversity and infection with similar visual forms, reduces the dependence on sample size, and improves the accuracy and robustness of crop condition monitoring in complex environments.

The application provides a kind of plant photosynthetic physiology three-dimensional imaging method and system, wherein the method includes respectively obtaining the stripe structure light image of target plant left and right view angle, according to the local texture change degree of pixel, region division is carried out, the sampling urgency index of each pixel is determined, the phase shift number of each pixel is dynamically adjusted, and then phase calculation is carried out, and the dual-view phase result is obtained;Then calculate the three-dimensional depth information, obtain the dual-view three-dimensional point cloud with confidence factor;Dual-view point cloud is assisted registration, segmentation and completion using camera matrix calibration, to generate the first three-dimensional model;Finally, the whole chlorophyll fluorescence image is fused with the first three-dimensional model, to obtain the second three-dimensional model representing photosynthetic physiology.The application realizes pixel-level adaptive sampling, improves the accuracy and efficiency of three-dimensional reconstruction, improves the integrity and alignment accuracy of point cloud through calibration assisted registration, and fuses physiological information with geometric model, realizes the integrated perception of plant morphology and photosynthetic function.

The application discloses a water body chlorophyll concentration profile inversion method and a laser radar detection method, wherein the inversion method comprises the following steps: obtaining laser radar detection echo signals at a specific depth in a target area, which comprises a fluorescence channel and a Raman channel; expressing a backscattering signal profile of the fluorescence channel as a function comprising a fluorescence backscattering coefficient; expressing a backscattering signal profile of the Raman channel as a function comprising a Raman backscattering coefficient; normalizing the backscattering signal profile function of the fluorescence channel with the backscattering signal profile function of the Raman channel to obtain a normalized signal; inverting the fluorescence backscattering coefficient based on a perturbation method; inverting a chlorophyll fluorescence absorption coefficient at a specific laser wavelength based on the inverted fluorescence backscattering coefficient; and inverting a chlorophyll concentration profile in the water body based on the inverted chlorophyll fluorescence absorption coefficient. The inversion method can invert the water body chlorophyll concentration profile from one measurement.

This invention discloses an early detection and evaluation method and system for the abiotic stress tolerance of new forest tree germplasm. The method includes: collecting chlorophyll fluorescence indicators, phenotypic indicators, and physiological and biochemical indicators of forest trees under abiotic stress conditions, and performing data cleaning and standardization; performing principal component analysis on the multi-source standardized dataset to screen candidate indicators; performing grey relational analysis using the comprehensive score of forest tree stress tolerance as a reference sequence and the candidate indicators as a comparison sequence; determining the core indicators and their weights based on the variance contribution rate of the principal component analysis and the correlation degree of the grey relational analysis; obtaining the abiotic stress assessment index from the core indicators using a membership function; and classifying the tolerance level of forest tree germplasm using a clustering algorithm based on this index. This invention achieves early, rapid, and accurate evaluation of the abiotic stress tolerance of forest tree germplasm, and can effectively guide forest tree stress resistance breeding work.

This invention provides a method for monitoring marine red tides based on chlorophyll fluorescence remote sensing, belonging to the field of remote sensing monitoring technology for large floating algae in nearshore areas. The steps of this invention include: acquiring sunlight-induced chlorophyll fluorescence data, chlorophyll concentration data, and fluorescence index data of the target area; performing gridding and data quality control on the sunlight-induced chlorophyll fluorescence data to obtain effective sunlight-induced chlorophyll fluorescence data; screening the effective sunlight-induced chlorophyll fluorescence data according to chlorophyll concentration, correcting the screened sunlight-induced chlorophyll fluorescence data for solar zenith angle to obtain corrected sunlight-induced chlorophyll fluorescence data; constructing a relationship model based on the corrected sunlight-induced chlorophyll fluorescence data and fluorescence index data, performing linear threshold conversion to determine the sunlight-induced chlorophyll fluorescence threshold for red tide identification, identifying red tides in the target area, and obtaining red tide monitoring results. This method improves the accuracy and reliability of red tide monitoring.

The application provides a method for improving drought resistance of tea trees, and belongs to the technical field of tea tree cultivation, and the method is that 0.1-2 mL of 0.01-0.2 mM ethylamine aqueous solution is externally applied to 1-5 leaves of the tea tree, and after the treatment, the chlorophyll fluorescence value, water content and MDA value of the leaves of the tea tree after drought treatment are higher than those of the drought treatment group, so that it can be proved that the external application of ethylamine can significantly enhance the drought resistance of the tea tree. The method is simple, convenient to operate and low in cost, and has great economic value for tea tree planting.

This invention discloses a rapid detection method for the stress effects of typical organophosphorus flame retardants, belonging to the field of aquatic environmental ecological risk assessment technology. The method includes steps such as cultivating *Microcystis aeruginosa*, preparing solutions of typical organophosphorus flame retardants with concentration gradients, preparing the algal solution, stress cultivation, regular monitoring, and establishing a stress effect model. This invention establishes a standardized culture system for *Microcystis aeruginosa*, stabilizing the physiological state of the algae and effectively reducing detection errors. The invention employs acute and chronic dual-mode stress cultivation and sampling, simultaneously meeting the needs of emergency monitoring of sudden pollution and long-term cumulative toxicity assessment. Furthermore, this invention introduces chlorophyll fluorescence as a core parameter, overcoming the limitations of traditional single-indicator detection. The stress effect model of this invention has undergone rigorous verification and recalibration design, exhibiting strong resistance to matrix interference and is suitable for complex real-world aquatic scenarios.

The application discloses a rapeseed suitable fertilization amount test screening method, and comprises the following steps: collecting chlorophyll fluorescence parameters of rapeseed canopy in each test area, screening test areas with abnormal chlorophyll fluorescence parameters according to the chlorophyll fluorescence parameters, and generating a rapeseed canopy physiological state early warning list; positioning acoustic sensor nodes based on the rapeseed canopy physiological state early warning list, generating an acoustic sensor node group, and scanning soil in a rapeseed root area according to the acoustic sensor node group to obtain a soil medium acoustic characteristic data set; converting the soil medium acoustic characteristic data set into an acoustic wave velocity distribution diagram and a signal absorption coefficient diagram by means of an acoustic imaging algorithm, and performing spatial registration and data fusion to generate a rapeseed root area soil acoustic characteristic profile diagram. The application realizes nondestructive visual characterization of the root area soil structure and root system morphology, thereby providing accurate root system spatial distribution and physical characteristic insight.

The application discloses a SIF spectrum inversion method based on singular value decomposition and machine learning regression. The method comprises the following steps: step 1, acquiring sample radiance spectrum data, solar-induced chlorophyll fluorescence spectrum data corresponding to the radiance spectrum data and the like; step 2, performing singular value decomposition on a feature matrix, selecting a plurality of main singular vectors to construct a reconstruction base matrix, and calculating low-dimensional expansion coefficients of the fluorescence spectrum; step 3, performing singular value decomposition on the feature matrix; step 4, taking the low-dimensional feature coefficients of the radiance spectrum, preset key band radiance features, band ratio features and observation geometry features as input variables of a machine learning regression model; and step 5, inputting a sample to be inverted into the trained machine learning regression model. The application converts the high-dimensional solar-induced chlorophyll fluorescence spectrum inversion problem into a low-dimensional expansion coefficient prediction problem, and has the advantages of high inversion efficiency, strong model stability and suitability for popularization and application.

This invention discloses a method for identifying the high-temperature state of tea seedlings based on chlorophyll fluorescence imaging, comprising the following steps: Step S1, acquiring chlorophyll fluorescence parameters and images of tea seedlings under different high-temperature stress conditions, performing Spearman rank correlation analysis on each parameter, and determining that the parameter significantly correlated with the stress level is the maximum photosynthetic efficiency Fv / Fm; Step S2, preprocessing the chlorophyll fluorescence images corresponding to Fv / Fm to construct a high-temperature stress state dataset; Step S3, constructing an improved YOLOv11-MEIP model, wherein the improved YOLOv11-MEIP model replaces the backbone network of the original network with a MobileNetV4 network and introduces EUCB, iRMB, and PConv modules; Step S4, training, validating, and testing the improved YOLOv11-MEIP model; Step S5, deploying the tested improved YOLOv11-MEIP model for identifying the high-temperature stress state of tea seedlings. This study aims to provide a rapid and accurate monitoring method for the high-temperature stress state of tea seedlings, meeting the real-time monitoring needs of large-scale tea gardens.

This invention relates to the field of plant stress diagnosis technology, and more particularly to a modulated fluorescence imager that uses blue and green light to calculate the photochemical efficiency Φ of leaf photosystem I. PSI The method described in this invention utilizes a Mini-PAM chlorophyll fluorometer to measure blue and green light excitation in tomato leaves of both the test and control groups to generate fluorescence. Based on the measurement results, the photochemical efficiency Φ of photosystem I is calculated. PSI This allows for the diagnosis of the condition of the crop under test. The photochemical efficiency Φ of the photosystem I mentioned in this invention... PSI It is more sensitive to stress and is more effective in reflecting adverse stress. It is expected to be applied to the early diagnosis of stress in plants, so as to intervene in time and reduce losses.

This invention relates to the field of remote sensing imaging systems, and more particularly to a chlorophyll fluorescence imaging system, imaging method, and application. The imaging system includes a housing, an imaging lens, a moving assembly, and a processor. The imaging lens is mounted on the moving assembly and extends outside the housing. The moving assembly is used to move the imaging lens. Inside the housing, from the imaging lens backwards, are arranged a slit, a first lens group, a grating, a second lens group, and an area array detector. A light intensity acquisition module is located on one side of the slit. The slit and the first lens group are both parallel to the imaging lens. The first and second lens groups are not parallel to the grating, and are not parallel to each other. The processor is located inside the housing and is electrically connected to the moving assembly and the area array detector. An electronic interface for outputting data is installed on the outer wall of the housing and is electrically connected to the processor. This invention can achieve fluorescence imaging with high spectral resolution, high spatial resolution, and a high signal-to-noise ratio.

This invention discloses a method for estimating marine carbon fixation based on phytoplankton live chlorophyll fluorescence technology. The method includes the following steps: 1) culturing a single phytoplankton species to obtain an algal culture medium; 2) determining the PSIIactive content of the algal culture medium using FRR chlorophyll fluorescence induction technology; and 3) using isotope... 14 C) The photosynthetic carbon fixation of algal solutions was determined using tracer technology; 3) The PSIIactive content was combined with the photosynthetic carbon fixation amount to establish a model relating the photosynthetic carbon fixation rate based on PSIIactive and changes in light intensity. Using PSIIactive as a bridge, a method for estimating marine carbon fixation based on phytoplankton chlorophyll fluorescence technology was established. This invention has the advantages of being simple to operate, economical, and rapid, solving the problems of high cost, environmental pollution, and harm to human health in marine phytoplankton carbon fixation measurement.

This invention discloses an AHP-EWM-RSR evaluation method suitable for optimizing micro-spraying and misting control. Micro-spraying and misting parameters are set as core control variables, and key growth and physiological indicators such as crop height, photosynthetic parameters, chlorophyll fluorescence parameters, and yield are used as feedback signals. The analytic hierarchy process (AHP) and entropy weighting method are used to obtain subjective and objective weights respectively. The combined weights are calculated using the scalar multiplication synthesis normalization method, and the AHP-EWM-RSR model is constructed using the rank-sum ratio method. The frequency distribution is obtained by ranking, and the data are sorted according to RSR values. Linear regression analysis is performed with probit as the independent variable and RSR as the dependent variable. The evaluated objects are ranked and categorized according to the RSR estimates obtained from the regression equation. This method has significant advantages, as it can compensate for the objectivity bias inherent in single weighting and accurately determine the optimal combination of micro-spraying height and duration in field production management, improving water use efficiency and high-temperature stress resistance, and achieving efficient and reliable field high-temperature stress resistance management.

This invention discloses a rapid assessment method for rice flood damage based on multi-source remote sensing constrained interpolation and stress process modeling, belonging to the field of agricultural remote sensing and disaster assessment technology. The method includes: extracting NDVI time-series curves for each field using optical images; dividing the rice growth stage according to characteristic inflection points and assigning stress-sensitive weight coefficients to each stage, constructing a growth stage-stress-sensitive weight coefficient matrix; acquiring solar-induced chlorophyll fluorescence products, calculating the photosynthetic efficiency index and comparing it with the NDVI ratio of the same period to identify early stress areas and correct stress exposure levels; inputting the growth stage weight coefficients and corrected stress exposure levels into a growth stage-weighted stress cumulative loss model, combining it with the counterfactual baseline yield assimilated from the pre-disaster crop growth model, obtaining the yield loss rate and the amount of loss, and outputting a spatial distribution map of the loss. This invention achieves rapid and accurate assessment of rice flood damage.

The present application relates to the technical field of plant factory light environment regulation, and discloses a light period regulation method and system based on dynamic adaptation of plant growth stages, which comprises the following steps: executing an interlayer spectral crosstalk calibration sequence to generate an interlayer spectral crosstalk transmission coefficient tensor; identifying the growth stages of plants in each layer, retrieving growth stage adaptation target light period parameter groups; collecting chlorophyll fluorescence kinetic parameters and decomposing direct and crosstalk spectral components to generate a photosynthetic response dataset grouped by varieties; independently training a spectral sensing Gaussian process regression model for each variety; executing multi-objective constraint Bayesian optimization to generate a set of Pareto optimal light period candidate schemes; and outputting the scheduling scheme to each layer of light controllers for synchronous execution; the present application solves the technical problem of light period parameter misalignment caused by interlayer light crosstalk in multi-layer vertical planting racks.

This invention discloses an integrated space-air-ground solar-induced vegetation chlorophyll fluorescence monitoring system, belonging to the field of remote sensing monitoring technology. The system outlines the functions, parameter constraints, and coordination relationships of modules for space-based, air-based, and ground-based chlorophyll fluorescence information acquisition, data analysis, processing, and application. By matching the acquired multi-element data at different scales (space, spectral, observation angle, and atmospheric elements), the system can comprehensively acquire the reflection characteristics, heat dissipation, photochemical index, emission fluorescence, and atmospheric transport characteristics of vegetation during photosynthesis. The system also presents parameter relationships and methods for extracting full-spectrum fluorescence radiation information curves based on oxygen absorption and the Fraunhofer dark line. Based on space-air-ground calibration and model verification, the system can perform chlorophyll fluorescence detection at varying levels of detail across a large global area, key regions, and ground canopy scales, enabling real-time monitoring of various vegetation stress effects and accurate estimation of primary total productivity.

This invention discloses a system and method for standardizing plant chlorophyll fluorescence data, relating to the fields of plant phenotypic analysis and precision agriculture. The system includes: a chlorophyll fluorescence measurement unit configured to acquire raw fluorescence data from measurement points on the plant canopy; a three-dimensional structural imaging unit configured to acquire a three-dimensional structural model containing the measurement points; and a processing unit configured to: spatially register the coordinates of the measurement points in the measurement system coordinate system with the coordinates in the three-dimensional structural model; determine the incident vector and observation vector based on a pre-calibrated offline measurement system; calculate the normal vector of the leaf surface at the measurement point from the three-dimensional structural model; and construct a standardized model based on the incident vector, observation vector, normal vector, and fill factor; and calculate the raw fluorescence data to generate standardized fluorescence data. This system eliminates interference caused by geometric relationships, obtaining comparable data.

The application discloses a kind of green plant flowering synchronous regulation method and system based on dynamic perception, the method includes: using visible light-near infrared dual-channel imaging equipment scanning obtains green plant canopy multispectral image sequence and environmental light spectrum distribution data, through extracting chlorophyll fluorescence intensity value, anthocyanin reflectivity and so on Feature generation plant physiological state vector, provide accurate data for subsequent regulation;With the aid of flowering convolution prediction model optimized by transfer learning, the flowering time window of each green plant is accurately predicted by fusing environmental light spectrum and historical phenology data;Then, through spatiotemporal superposition analysis, abnormal green plants in flowering period are screened out, and spectral control instructions are generated by calculating compensation spectrum parameters, and finally directional irradiation is carried out through rotatable array type spectral emitter.The application can improve the accuracy of synchronous regulation of green plant flowering.

The application discloses a standard light path system for simulating chlorophyll absorption peaks and a spectral calibration method thereof, and the system comprises a light source module, a light path collimation and coupling module, an integrating sphere homogenization module, a monitoring and feedback module, a spectral calibration module and a control and communication module. The application realizes controllable output in the range of 470-680 nm through the standard light path system, and combines the integrating sphere homogenization technology and the spectral instrument traceability calibration, so that a stable, uniform and traceable standard light source is provided for a chlorophyll fluorometer and other plant physiological measurement devices, the comparability and scientific reliability of measurement data are improved, and thus basic support is provided for ecological system carbon sink monitoring and related instrument metrological calibration.

The application provides a kind of semiconductor laser-based agricultural precision irradiation system and irradiation method, it is related to facility agriculture and intelligent light environment control technical field.The system first obtains crop canopy structure, organ position, spectral reflectance, photosynthetic activity and surface temperature data through three-dimensional depth camera, multispectral camera, chlorophyll fluorescence collector, infrared thermal imager and environmental spectrum sensor, constructs crop organ unit;Then based on insufficient light degree, production target contribution degree, physiological deviation degree, target wavelength response sensitivity, thermal risk degree and cumulative irradiation load to generate bottleneck organ queue;Finally, through semiconductor laser source station, optical fiber distribution module and laser delivery terminal, low thermal load laser corresponding to wavelength, spot shape and scanning path is delivered to target organ, and subsequent irradiation strategy is corrected according to temperature rise state and irradiation response.The application improves the precision, safety and light energy utilization rate of agricultural light supplement.

This invention relates to the field of intelligent equipment and environmental control technology for facility agriculture, and discloses a method for sensing the growth status and adaptive supplemental lighting control of leafy vegetables in plant factories. The method includes the following steps: Step S1: Multimodal data acquisition; Step S2: Multimodal data fusion and growth status assessment; Step S3: Intelligent identification of growth stages and prediction of growth trends; Step S4: Adaptive optimization of multi-dimensional supplemental lighting parameters, etc. This method for sensing the growth status and adaptive supplemental lighting control of leafy vegetables in plant factories, through the fusion of hyperspectral, RGB-D, and chlorophyll fluorescence multimodal data plus attention mechanism feature weighting, can achieve comprehensive perception of leafy vegetable morphology, physiology, and spectral information, significantly improving the accuracy of growth status assessment. Through growth stage identification, LSTM trend prediction, and improved particle swarm optimization, it can achieve synchronous adaptive control of spectrum, light intensity, and photoperiod, while simultaneously improving leafy vegetable yield and quality, and significantly reducing supplemental lighting energy consumption, making the control more intelligent and energy-efficient.

This invention provides a method for evaluating the high-temperature resistance of Lycium barbarum germplasm resources, relating to the field of Lycium barbarum germplasm resource evaluation. The evaluation method includes sample collection, indicator measurement and standardization, calculation of the high-temperature resistance comprehensive index (HR), and high-temperature resistance evaluation. The method provided by this invention integrates three types of indicators: photosynthetic system stability (chlorophyll fluorescence), reproductive capacity (pollen viability), and population continuity capacity (seed germination rate), covering all stages of Lycium barbarum growth and comprehensively encompassing the dimensions of high-temperature impact on Lycium barbarum, resulting in more accurate evaluation results. A weighted standardized model is used to quantify high-temperature resistance, allowing for a direct comparison of the high-temperature resistance of different Lycium barbarum germplasm resources. Furthermore, by combining rating criteria, the differences in resistance among different germplasms can be accurately distinguished. The method clarifies the measurement conditions and calculation methods for each indicator, forming a standardized operating procedure. The measurement methods for the three types of indicators are simple to operate and suitable for rapid screening of large-scale Lycium barbarum germplasm resources.

The application provides a winter wheat yield estimation method and a yield estimation model training method and device, wherein the estimation method comprises: obtaining characteristic data of winter wheat, the characteristic data comprising the following types of data: remote sensing vegetation index, meteorological information, soil moisture information, and solar-induced chlorophyll fluorescence GOSIF data; inputting the characteristic data into a pre-trained yield estimation model to obtain a yield estimation result of winter wheat output by the yield estimation model, wherein the yield estimation model is a parallel TCN-LSTM model (hereinafter referred to as TCN-LSTMs) generated by combining a time convolution network (TCN) and a long short-term memory network (LSTM). In the embodiment of the application, the TCN-LSTMs model is used for winter wheat yield estimation with multiple types of characteristic data, thereby improving the accuracy of yield estimation.