51 results about "Arbitrary Fluorescence Unit" patented technology

The invention provides an automatic illumination adjusting method and system based on seed and seedling cultivation, and relates to the technical field of illumination control and intelligent agriculture, the method comprises the following steps: firstly, collecting seedling chlorophyll fluorescence parameters to construct a light stress index, and accurately identifying a light inhibition risk; then calculating a physiologically permissible light intensity threshold adaptive to the dark response capability of the plant by combining the environment temperature and the CO2 concentration; positioning a shielded weak light area by using three-dimensional point cloud voxelization and a ray tracing technology, and calculating a shadow compensation gain coefficient; and finally, generating a control matrix in combination with a dynamic left-rotation light strategy, and driving an independent addressing LED array and a variable-angle lens to execute local precise light supplement and light vector adjustment. Through physiological feedback and spatial light field reconstruction, the problem that traditional illumination control neglects individual tolerance difference and canopy shielding is solved, photooxidation damage is effectively prevented, and the growth uniformity and the light energy utilization rate of seedling groups are remarkably improved.

The invention provides a crop functional chlorophyll fluorescence inversion method, system and device and a readable medium, and relates to the technical field of ecological environment monitoring, and the method comprises the steps: obtaining a multi-source data cube; acquiring a crop type distribution diagram based on the multi-source data cube; constructing a sun-induced chlorophyll fluorescence SIF inversion basic model according to the crop type distribution diagram; and outputting a crop solar-induced chlorophyll fluorescence time sequence product according to the solar-induced chlorophyll fluorescence SIF inversion basic model. According to the crop functional chlorophyll fluorescence inversion method provided by the invention, the multi-source data cube is used as input, and two special processing links which are completely independent in physical mechanism and dynamic correction are constructed; high-precision and high-physical-reliability SIF time sequence product production for crops can be realized, and a reliable data basis is provided for precise monitoring of photosynthesis of an agricultural ecosystem.

The application relates to the technical field of intelligent agriculture and plant protection, and particularly discloses a broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion, which comprises a portable collection platform, a mobile experiment terminal and a server. The portable collection platform is integrated with a hyperspectral imaging camera, a chlorophyll fluorescence imager, a dark-adapted leaf clamp and a camera for sample barcode recognition, and is provided with an adjustable height support and a controllable light source to ensure the consistency of data collection conditions. The mobile experiment terminal is internally provided with a physicochemical index rapid detection module, including a portable PCR instrument for on-site nucleic acid rapid extraction and amplification and a miniature ultraviolet spectrophotometer for rapid determination of pigment content. The broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion are highly integrated, portable and automated, are suitable for a field environment and provide an efficient disease diagnosis tool for non-expert users.

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.

The invention discloses a method, system and medium for machine learning inversion of sunlight-induced chlorophyll fluorescence, and relates to the technical field of vegetation sunlight-induced chlorophyll fluorescence remote sensing inversion, and the method comprises the steps: obtaining an actual measurement data set, and generating a synthetic data set; introducing principal component analysis to carry out dimension reduction preprocessing on features in the synthetic data set to obtain a preprocessed synthetic data set, and taking the preprocessed synthetic data set as a training set; introducing noise of different levels into the training set to train each machine learning model, and obtaining each trained machine learning model; pre-processing the incident irradiance and the reflected radiance in the measured data set, taking the pre-processed incident irradiance and reflected radiance as input of each trained machine learning model, and comparing performance indexes of output results of each model to obtain an optimal machine learning model; executing an SIF inversion task based on the optimal machine learning model; the method is used for high-resolution and rapid inversion of the SIF.

The application relates to the technical field of remote sensing monitoring, and discloses a lake cyanobacterial bloom detection method and system fusing remote sensing images. The method comprises the following steps: acquiring multispectral remote sensing images and synthetic aperture radar images of a target lake; calculating phycocyanobilin characteristic indexes and chlorophyll fluorescence peak indexes according to characteristic band reflectivity, screening pixels meeting a discrimination condition to obtain an optical water bloom distribution mask; calculating a radar-retrieved phycocyanobilin index according to a dual-polarization backscattering coefficient through a nonlinear regression model to obtain a radar water bloom distribution mask; and fusing the two masks to obtain a cyanobacterial bloom monitoring result. The application solves the problems that the existing cyanobacterial bloom detection method cannot realize all-weather monitoring, lacks cyanobacterial specificity recognition capability, and has insufficient reliability of a single data source, and improves the timeliness, accuracy and reliability of cyanobacterial bloom detection.

This invention discloses a rapid detection method and system for antibiotic residues in vegetables based on chlorophyll fluorescence imaging, belonging to the field of non-destructive testing technology for agricultural product quality and safety. The invention applies saturated pulsed light to dark-adapted vegetable leaves and acquires multi-temporal resolution fluorescence imaging sequences at a logarithmic scale within the transient time window from phase J to phase I of the rapid chlorophyll fluorescence induction kinetics. From these sequences, a temporal feature tensor composed of transient inflection point time shifts and a spatial feature tensor composed of leaf vein-mesophyll boundary heterogeneity textures are extracted. These two tensors are then fused using a Tucker core tensor cross-external product to obtain an antibiotic type fingerprint tensor. The antibiotic type and confidence level are output through CNN classification. Finally, a category-conditional gradient boosting regression is used to output concentration prediction values, and a reverse closed-loop adjustment is applied to adjust the sampling time window and region of interest. This invention achieves integrated rapid detection of antibiotic residues at the level of tens of micrograms per kilogram in vegetables, combining type identification and concentration quantification.

The invention relates to an imaging method for measuring sunlight-induced chlorophyll fluorescence (CF) of an object to be measured (30), in which the object to be measured is irradiated with sunlight (40), and light (41) reflected off the object to be measured, after being filtered to form filtered light (42) by means of an interference filter (15) that is tiltable vis-à-vis an optical axis, is recorded to form an image (20) by means of an image sensor (11) having several light-sensitive sensor elements (12) and is made available to a calculation and control device (16) by said image sensor, wherein an image series (21) of several recorded images of the object to be measured is created by means of the calculation and control device, said images being recorded at different tilt angles (δ, ε) of the interference filter in order to change, for a portion of the sensor elements within the image series and by means of the different tilt angles per image, an effective transmission wavelength (λ') of the interference filter at which said interference filter has maximum transmission, said effective transmission wavelength being related to the individual sensor elements, wherein the sunlight-induced chlorophyll fluorescence detected per sensor element is determined for this portion of the sensor elements by means of the calculation and control device on the basis of intensity values (IP, Px,y) of the filtered light (42) recorded by the respective sensor element per image of the image series; the invention furthermore relates to a device (10) for measuring sunlight-induced chlorophyll fluorescence of an object to be measured.

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 invention provides a crop yield remote sensing inversion method based on a chlorophyll fluorescence mechanism, and belongs to the technical field of agricultural crop growth monitoring, and the method comprises the steps: obtaining sunlight-induced chlorophyll fluorescence remote sensing data, photosynthetically active radiation data and crop type distribution data in the whole growth period of crops; and distinguishing the photosynthetic path according to the crop type distribution data, calculating by adopting a corresponding photoreaction mechanism model based on the obtained data to obtain the total primary productivity of the crop, obtaining the net primary productivity by combining the historical net primary productivity and the total primary productivity ratio, and obtaining the crop yield based on carbon content inversion. By utilizing coupling association between sunlight-induced chlorophyll fluorescence and a photosynthesis process, the problem that photosynthetic physiology in vegetation is difficult to effectively capture due to dependence on a static vegetation index in an existing crop technology is solved; the problem of how to accurately associate instantaneous sunlight-induced chlorophyll fluorescence remote sensing data with photosynthesis of crops in the whole growth period is solved.

1. Name of the product in this design: Sunlight-induced chlorophyll fluorescence measuring device. 2. Purpose of this design: It is used in a sunlight-induced chlorophyll fluorescence measurement device to achieve non-destructive real-time monitoring by accurately capturing fluorescence signals from plant photosynthesis. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention discloses a high-light-resistant algal strain high-speed screening method and system based on micro-fluidic single cell sorting, and relates to the field of bioengineering.The high-light-resistant algal strain high-speed screening method comprises the steps that haematococcus free cells in the logarithmic phase are taken and added into a double-fluorescence probe mixed solution, and incubation is conducted in a dark place to obtain a haematococcus solution; bBM sheath fluid and haematococcus fluid are introduced into a sample introduction area of the micro-fluidic chip, the BBM sheath fluid and the haematococcus fluid are focused by the sample introduction area and then enter a highlight stress area, highlight treatment is carried out to induce motile cells to be transformed into spores, and the transformed cells enter a detection area. Through in-situ high light stress and single cell continuous sorting of the micro-fluidic chip, the sorting efficiency is high, and the screening period is greatly shortened; three parameters of chlorophyll fluorescence, astaxanthin fluorescence and forward scattering light are adopted for cooperative detection, and a threshold value which is quantitatively set based on a control group is combined, so that the proportion of a false positive strain is reduced to be less than 5%, and the screening accuracy of a double-superior strain is greatly improved; the microfluidic chip is extremely low in single reagent consumption, greatly reduces the screening cost of single strains, and is easy for large-scale popularization and application.

The invention provides a laser-induced observation system and method, and the system comprises a laser-induced light source which is used for outputting laser, exciting chlorophyll fluorescence of a detected sample, and forming a detectable fluorescence signal; wherein the detected sample is a living plant; the optical receiving system is used for collecting fluorescence signals excited by the detected sample; the spectral information acquisition system is connected with the optical receiving system and is used for carrying out light splitting and detection on the fluorescence signal to obtain spectral data; the spectral information processing system is connected with the spectral information acquisition system and is used for preprocessing the spectral data acquired by the spectral information acquisition system to obtain preprocessed spectral data; and the quantitative analysis algorithm system is connected with the spectral information processing system and is used for carrying out quantitative analysis on the preprocessed spectral data to calculate chlorophyll fluorescence parameters. The problem that laser-induced chlorophyll fluorescence of living plants cannot be remotely observed or cannot be detected in an existing laser-induced chlorophyll fluorescence technology is solved.

The invention relates to the field of agricultural detection, discloses a lossless seed maturity detection system, and solves the problems that the existing seed maturity detection technology depends on a single optical mode, so that the detection precision and robustness are insufficient, the misjudgment rate is high, and accurate planting decision support cannot be quickly provided on site. The system comprises a fusion sensing module which comprises a resonance acoustic microscopic unit and a chlorophyll dynamic fluorescence unit and is used for collecting an internal structure acoustic signal and a chlorophyll fluorescence dynamic signal of a seed and constructing multi-dimensional seed characteristic data; the intelligent edge calculation module is integrated with a processor of a preset lightweight artificial intelligence model, and is used for performing feature extraction and data fusion on the acoustic signal and the fluorescence dynamics signal, and reasoning and outputting a seed maturity score; and the interaction module is used for receiving agronomic parameters input by a user, so that the processor generates planting decision suggestions based on the maturity score and the agronomic parameters, and displays the maturity score and the planting decision suggestions.

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.

The fusion foundation multi-source satellite chlorophyll fluorescence regional GPP assimilation inversion system belongs to the technical field of terrestrial ecosystem carbon cycle monitoring, and comprises an observation data acquisition module; a signal unmixing and simulation generation module, which is used for unmixing total fluorescence into pure chlorophyll fluorescence SIF, and driving an ecological model to generate SIF and GPP simulation values based on environmental stress data; a state component decoupling module, which is used for decoupling total GPP simulation values into a light response fast component, a slow component and a memory component; a state evolution prediction module, which is used for predicting dynamic evolution of the slow component and the memory component; and an adaptive assimilation inversion module, which is used for taking the slow component and the memory component as state variables, taking SIF observation values as observation data, and combining a prediction result of the state evolution prediction module to perform inversion on regional GPP. The system overcomes systematic deviation caused by model simplification or data singularity in traditional methods.

The application discloses a corn nitrogen reduction and biochar effect time sequence dynamic characteristic evaluation method, first, different combinations are set, chlorophyll fluorescence parameters, crown layer spectral reflectance and yield data of corn at key growth stages such as jointing stage and big bell mouth stage are collected; second, the spectral data is preprocessed, single time phase red edge characteristics are calculated, and time sequence dynamic characteristics are constructed, biochar sensitive wave bands are screened through reflectance difference percentage during growth, red edge index is designed, and fluorescence weight spectral index is constructed in combination with wide band vegetation index; then, the data is integrated to construct a multi-time phase spectral characteristic data set, a time sequence dynamic chlorophyll inversion model containing dynamic inversion, yield prediction and attribution branches is built, the former outputs chlorophyll and photosynthetic code vector at the grain filling stage, and the latter predicts yield and related parameter contribution; finally, the model is trained, the optimal matching is obtained based on the trained model, and support is provided for corn planting and fertilization decision-making.

The invention discloses a time sequence dynamic characteristic evaluation method for a corn nitrogen-reducing combined application biochar effect, which comprises the following steps of: firstly, setting different combined application combinations, and collecting indexes such as chlorophyll fluorescence parameters, canopy spectral reflectivity and the like and yield data in key growth periods such as a jointing period and a large bell mouth period of corn; secondly, preprocessing spectral data, calculating a single-temporal red edge feature and constructing a time sequence dynamic feature, screening a biochar sensitive wave band through a reflectivity difference value percentage in a growth period, designing and adjusting a red edge index, and constructing a fluorescence weight spectral index in combination with a broadband vegetation index; then integrating data to construct a multi-temporal spectral feature data set, and constructing a time sequence dynamic chlorophyll inversion model containing dynamic inversion, yield prediction and attribution branches, the former outputting chlorophyll and photosynthetic coding vectors in a filling period, and the latter predicting yield and contribution degrees of related parameters; and finally, training the model, constructing a combined application decision map based on the trained model, obtaining an optimal ratio from the map, and providing support for corn planting fertilization decision.

The invention discloses a cooperative sunlight-induced chlorophyll fluorescence measurement method, and relates to the technical field of crop growth monitoring, the method is realized based on a cooperative sunlight-induced chlorophyll fluorescence measurement system, and the cooperative sunlight-induced chlorophyll fluorescence measurement system adopts a combination mode of combining a single spectrometer with a single optical fiber. The optical fiber is driven by the rotating mechanism to rotate to two different detection positions to cooperatively measure the solar incident radiation illuminance irrad and the canopy reflection radiation illuminance rad, so that the fixed deviation between instruments can be effectively eliminated, and meanwhile, the integrity of an optical path is ensured to a certain extent; the cosine corrector and the optical fiber adopt a split structure, the optical fiber faces upwards and is matched with the cosine corrector to measure the solar incident radiation illuminance irrad so as to obtain a larger view field range, and the optical fiber faces downwards to directly measure the canopy reflection radiation illuminance rad so as to focus a target plant canopy in a concentrated manner, so that the influence of too many ground interferents due to the too large view field range is avoided; and the measurement accuracy can be improved.

The invention discloses a GIS (Geographic Information System)-based rice low-temperature cold injury risk assessment method and system. The method comprises the following steps of 1, constructing rice physiological behavior time sequence data; 2, constructing a day and night behavior phase model; 3, mapping the rice physiological behavior time sequence data to a day and night behavior phase model, setting a biological clock drift judgment condition, and generating a biological clock drift signal when the condition is met; 4, constructing a fusion feature vector sequence; 5, inputting the fusion feature vector sequence into an improved TimesNet model, embedding a double-time-domain gating residual filtering mechanism, and outputting a rhythm anomaly feature vector; step 6, constructing a cold damage feature vector; and 7, analyzing the recovery conditions of the photosynthetic efficiency and the chlorophyll fluorescence, and outputting a cold damage risk grade layer. According to the method, the day and night behavior phase model and the improved TimesNet model are fused, and intelligent risk grading of rice cold damage is achieved.

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.

The invention discloses an all-weather sun-induced total chlorophyll fluorescence reconstruction method and device. The method comprises the following steps: S1, estimating fluorescence efficiency and a vegetation absorption photosynthetically active radiation ratio fPAR; s2, performing space-time reconstruction based on tensor decomposition to obtain a space-time continuous fPAR time sequence; s3, calculating photosynthetically active radiation of the canopy top under the all-weather condition; and step S4, based on the fPAR, obtaining the all-weather sunlight induced chlorophyll fluorescence total amount AST-SIF. By adopting the technical scheme of the invention, accurate estimation of high time resolution in a global range under an all-weather condition is realized, and remote sensing inversion precision of vegetation photosynthetic activity and general primary productivity GPP is improved.

The invention discloses a chlorophyll fluorescence inversion method, device and system and a storage medium. The chlorophyll fluorescence inversion method comprises the steps that hyperspectral observation data and irradiance are acquired, and apparent reflectivity is calculated; ensemble empirical mode decomposition (EEMD) is carried out on the apparent reflectance so as to adaptively separate a real non-fluorescence reflectance spectrum component; a genetic algorithm (GA) is introduced, an average absolute percentage error (MAPE) of reflectivity reconstruction is used as a fitness function, key parameter auxiliary white noise amplitude in EEMD decomposition is dynamically optimized, and an optimal decomposition parameter is obtained; and reconstructing a non-fluorescence true reflectivity curve by using the optimal parameter, and calculating and inverting an SIF signal by deducting a reflected radiation part. By adopting the technical scheme of the invention, the problems of low precision and poor stability in the traditional chlorophyll fluorescence SIF inversion method are solved.

The invention relates to the technical field of intelligent agriculture and plant protection, and particularly discloses a broccoli black spot early diagnosis system and method based on multi-mode machine learning fusion, and the system comprises a portable collection platform which is integrated with a hyperspectral imaging camera, a chlorophyll fluorescence imager and a dark adaptation leaf clamp, the platform is provided with a height-adjustable bracket and a controllable light source, so that the consistency of data acquisition conditions is ensured; a physical and chemical index rapid detection module is arranged in the mobile experiment terminal, and the mobile experiment terminal comprises a portable PCR instrument for rapid extraction and amplification of nucleic acid on site and a miniature ultraviolet spectrophotometer for rapid determination of pigment content. According to the broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion, the system is highly integrated, portable and automatic, is suitable for a field environment, and provides an efficient disease diagnosis tool for non-expert users.

This invention discloses a three-dimensional chlorophyll fluorescence imaging system for plants based on structured light vision, comprising: Step S1: constructing a three-dimensional chlorophyll fluorescence imaging system; Step S2: switching between measurement light source, photochemical light source, saturated pulse light source, and far-red light source in red filter mode to acquire chlorophyll fluorescence images of plants under different conditions; Step S3: turning on ordinary white light source, acquiring single-channel images of the plant sequentially in red, green, and blue filter modes, and fusing them to obtain a three-channel color image of the plant; Step S4: turning off all light sources, acquiring a dual-frequency twelve-step phase-shifting fringe image modulated by the plant in neutral filter mode, and reconstructing a three-dimensional point cloud model of the plant; Step S5: using the chlorophyll fluorescence image and the three-channel color image to render the three-dimensional point cloud model of the plant pixel by pixel to achieve three-dimensional chlorophyll fluorescence imaging of the plant.

A method of controlling light conditions for the growth of one or more plants grown in a setting in which said plants are exposed to sunlight is described, which comprises receiving a target in respect of photosynthetic activity of one or more plants over a predetermined interval. One or more chlorophyll fluorescence measurements of at least one of said plants are taken. A relationship between photosynthetic activity of the plant and photosynthetically active light intensity incident upon the plant is estimated based on the one or more chlorophyll fluorescence measurements. A solar forecast is obtained in respect of at least part of the predetermined interval. A light conditions plan in respect of the predetermined interval is generated for the one or more plants based on the target in respect of photosynthetic activity, the estimated relationship between photosynthetic activity of the plant and photosynthetically active light intensity incident upon the plant, and the solar forecast. One or more supplemental photosynthetically active light sources arranged to irradiate the one or more plants and / or one or more light shades arranged to selectively block a proportion of sunlight incident upon the one or more plants are controlled in accordance with the light conditions plan.

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.

The invention relates to the field of chlorophyll fluorescence imaging, and discloses a movable plate facilitating sample determination of a chlorophyll fluorescence imaging system.The movable plate comprises a chlorophyll fluorescence imager body, one side of the chlorophyll fluorescence imager body is fixedly connected with an adjusting structure, and one side of the adjusting structure is provided with a movable plate body; the device comprises a movable plate, adsorption holes are uniformly formed in the top of the movable plate, a gas collection cavity is formed in the movable plate, connecting plates are symmetrically and fixedly connected to the interior of the movable plate, mounting plates are fixedly connected to the opposite sides of the connecting plates, and a vacuum pump is fixedly mounted at the top of each mounting plate. The moving block is driven to move by the electric push rod, and the electric push rod is matched with the exhaust pipe II and the gas conveying pipe, so that the height adjustment of the movable plate and the fixation of the blade are realized, and the problems that most sample plates of a traditional chlorophyll fluorescence imaging system are fixed, the height cannot be adjusted according to detection requirements and the practicability is influenced are solved.

The invention relates to the technical field of agricultural biology, and discloses a lettuce rapid carbon assimilation light formula based on 450 / 460 nm laser and 660 nm LED pulse time sequence coupling and a regulation and control method, and the regulation and control method comprises the following steps: S1, collecting the net photosynthetic rate (Pn), chlorophyll fluorescence parameters (Fv / Fm, phi PSII), intercellular CO2 concentration (Ci) and leaf surface temperature (Tleaf) of lettuce functional leaves in real time; the environment parameters comprise environment temperature (Tenv), environment relative humidity (RH) and environment CO2 concentration (Cenv); and S2, inputting the real-time data into a preset photosynthetic stage recognition model, and outputting the current photosynthetic stage of the lettuce, the photosynthetic stage at least comprising an activation stage, an efficient carbon sequestration stage, a saturation stage and a photosuppression risk stage. Through a multi-parameter real-time monitoring and stage recognition model, the light pulse time sequence can be accurately matched with the instantaneous state of a lettuce photosynthetic mechanism, and energy waste or light suppression caused by a fixed formula in different time periods is avoided.