157 results about "Chlorophyll fluorescence" patented technology

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

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 sky-ground integrated sunlight-induced vegetation chlorophyll fluorescence monitoring system, and belongs to the technical field of remote sensing monitoring. The system provides functions, parameter constraints and cooperation relations of space-based, empty-based and foundation chlorophyll fluorescence information acquisition and data analysis processing and application modules. Spatial scale matching, spectral scale matching, observation angle matching and atmospheric element matching are performed on the acquired sky-ground multi-element data with different scales, so that reflection characteristics, heat dissipation, photochemical indexes, fluorescence emission and atmospheric transmission characteristics during vegetation photosynthesis can be completely acquired; the invention provides a parameter relation and a method for extracting a fluorescence full-spectrum radiation information curve based on oxygen absorption and Fraunhofer dark line. On the basis of sky and landmark calibration and model verification, chlorophyll fluorescence detection of different fineness degrees of global large-range, key areas and ground crown planting scales can be carried out, various stress effects of vegetation can be monitored in real time, and the primary total productivity of the vegetation can be accurately estimated.

The invention belongs to the technical field of intelligent irrigation, and discloses a garden environment monitoring and management system based on the Internet of Things technology, and the system comprises a soil moisture monitoring module which obtains the moisture content of a root layer of garden vegetation through an implantable TDR soil moisture sensor array; the carbon flux sensing module is used for respectively monitoring carbon dioxide flux and vegetation photosynthetic efficiency through a vortex covariance tower and a chlorophyll luminoscope and acquiring carbon flux data and photosynthetic efficiency parameters; obtaining net carbon exchange capacity based on the carbon flux data and the photosynthetic efficiency parameters; the data coupling module is used for performing coupling analysis on the moisture content of the root system layer and the net carbon exchange capacity, establishing a moisture-carbon fixed correlation model, calculating a carbon sink efficiency index and judging whether an initial irrigation demand signal is generated or not; the utilization rate of water resources is increased; and meanwhile, ecological benefits are considered.

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 remote sensing monitoring, and discloses a lake cyanobacterial bloom detection method and system fused with a remote sensing image. The method comprises the following steps: acquiring a multispectral remote sensing image and a synthetic aperture radar image of a target lake; calculating a phycocyanobilin characteristic index and a chlorophyll fluorescence peak index according to the characteristic wave band reflectivity, and screening pixels meeting discrimination conditions to obtain an optical water bloom distribution mask; calculating a radar inversion phycocyanobilin index through a nonlinear regression model according to the dual-polarization backscattering coefficient to obtain a radar water bloom distribution mask; and fusing the double masks to obtain a cyanobacterial bloom monitoring result. The method solves the problems that an existing cyanobacterial bloom detection method cannot realize all-weather monitoring, lacks cyanobacterial specific recognition capability and is insufficient in reliability of a single data source, and improves the timeliness, accuracy and reliability of cyanobacterial bloom detection.

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 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 discloses a differential chlorophyll fluorescence inversion method, system and device and a storage medium, and is applied to the field of vegetation remote sensing and physiological ecological monitoring, and the method comprises the steps: obtaining vegetation state monitoring data of a target region; on the basis of vegetation state monitoring data, asynchronous leaf-changing type evergreen arbors and synchronous deciduous type arbors are identified; carrying out SIF simulation on the asynchronous leaf-changing type evergreen arbors and the synchronous deciduous type arbors by adopting corresponding models respectively; according to the phenological phase function and the stress response function, generating correction factors corresponding to the asynchronous leaf-changing evergreen arbors and the synchronous deciduous arbors; the obtained initial SIF value is optimized through a corresponding correction factor; fusing the optimized SIF values to generate an SIF time sequence product; according to the method, differential model configuration and physical mechanism driven online correction are coupled, so that the problems of model mismatching and physiological mechanism deficiency in heterogeneous vegetation SIF inversion are solved, and an SIF time sequence product which is high in precision and clear in physiological and ecological significance is generated.

The invention relates to the technical field of vegetation remote sensing observation, in particular to a chlorophyll fluorescence observation system based on laser radar, which comprises a target evaluation module used for responding to an observation evaluation condition to determine an observation analysis strategy of each observation target; the observation analysis module is used for determining the dominant direction of the mirror surface of the observation target and determining an inversion execution strategy of the observation target in response to the inversion execution condition; the dominant inversion module is used for determining a pre-estimation execution wave band set and determining observation pre-estimation data based on the polarization reflection parameters of the pre-estimation execution wave bands; the multi-parameter inversion module is used for determining an inversion weight coefficient of each observation wave band and determining whether to adjust the inversion weight index of each observation wave band in response to a weight analysis condition; the auxiliary analysis module is used for responding to the auxiliary analysis condition to determine the auxiliary analysis mode of the observation target, and the reliability of the fluorescence observation result of the observation target is improved.

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 discloses a satellite-borne sunlight-induced chlorophyll fluorescence spectrum detection imaging system, and belongs to the technical field of spectral imaging. The system includes a telescope optical system, a field-of-view separator assembly, a hyperspectral spectrometer system, and a hyperspectral spectrometer system. A vegetation chlorophyll signal from the earth surface is condensed by the telescope optical system and then is separated into a first view field object signal and a second view field object signal by the view field separator assembly, and chlorophyll fluorescence information and vegetation photochemical index information are synchronously acquired by the hyper-spectral spectrograph system and the hyperspectral spectrograph system respectively. According to the invention, synchronous high-precision detection of chlorophyll fluorescence and vegetation physiological parameters is realized, dynamic spectrum sampling is supported to match fluorescence peak valley characteristics, and an advanced remote sensing technical means is provided for global vegetation photosynthesis and carbon cycle research.

The invention relates to the technical field of submerged plant planting, and discloses an intelligent submerged plant planting method and system based on bionic light compensation and a storage medium. The system comprises the steps that a bionic sea anemone tentacle flexible base disc is constructed, and a shape memory polyurethane fixing arm and a silica gel suction cup are arranged; preparing a three-layer gradient nutrient sustained-release microcapsule and loading the microcapsule into a nutrient cavity of the basal disc; a water depth spectrum attenuation coefficient is calculated through a chlorophyll fluorescence attenuation inversion algorithm, and a depth adaptive light compensation parameter is generated; degradable spiral barb ground anchors are adopted for fixing, and intelligent planting of submerged plants is achieved. The technical problems that anchoring is unstable, nutrition supply is not matched and light compensation is not accurate in the deep water area submerged plant planting process are solved, and the success rate and the ecological restoration effect of plant planting in the deep water area 3 m or above are improved.

The invention relates to the technical field of agricultural drought monitoring and precise irrigation, in particular to a crop water stress diagnosis method and system based on sunlight-induced chlorophyll fluorescence, crop canopy oxygen absorption band spectral data is collected through a hyperspectral sensor, and a sunlight-induced chlorophyll fluorescence intensity SIF value is solved by adopting a fluorescence extraction algorithm. The method comprises the following steps: taking an SIF value of a crop canopy without water stress as a reference canopy SIF value, determining a difference between the SIF value of a to-be-detected crop canopy and the reference canopy SIF value, then generating a relative SIF water deficit index SIFWDI by combining a preset formula, establishing a mapping relation between the SIFWDI and a soil water deficit index by utilizing a regression analysis model, and outputting a diagnosis report. Variable irrigation decision making and early warning are triggered according to the stress level, and model parameter self-adaptive optimization is achieved through a feedback mechanism. The system realizes lossless, real-time and high-precision water stress monitoring and irrigation management.

The embodiment of the invention discloses a time-space prediction method and device for sun-induced chlorophyll fluorescence. The method comprises the following steps: acquiring a time-space data set; based on the spatio-temporal data set, taking a county administrative unit as a reference unit to perform area aggregation, dividing a large-scale area into a plurality of homogenized sub-areas, and selecting a representative spatial point from each sub-area; extracting the SIF of the corresponding representative spatial point and variable feature information of the key covariable, and constructing a four-dimensional space-time cube based on the variable feature information; sTL decomposition is carried out on the four-dimensional space-time cube to obtain a trend term, a seasonal term and a residual term, and flattening processing is carried out on the trend term, the seasonal term and the residual term to obtain a time sequence feature vector; the time sequence feature vector is input into a trained LSTM-Transform model for prediction, and a trend-season point value prediction result is obtained; and jointly interpolating the point value prediction result and a preset covariable to a preset target prediction area by using an ANUSPLIN thin plate smooth spline interpolation method, and generating an SIF large-scale space-time prediction map.

The invention discloses a wetland carbon sink monitoring system and a carbon sink evaluation method, and the wetland carbon sink monitoring system is designed based on open circuit / closed circuit vorticity. Detection equipment of the wetland carbon sink monitoring system comprises a carbon dioxide analyzer, a three-dimensional ultrasonic anemorumbometer, a methane analyzer, sunlight-induced chlorophyll fluorescence observation equipment and micrometeorological monitoring equipment, and a carbon sink model is constructed based on the wetland carbon sink monitoring system. Main greenhouse gases are monitored in real time, high-frequency continuous atmosphere-vegetation interface and atmosphere-water body interface carbon income and expenditure data are obtained, wetland and water body ecological system carbon sink time change characteristics are revealed, a carbon sink model is constructed in combination with micrometeorological monitoring and environmental data, the Hongze lake wetland carbon sink pattern and influence factors are clarified, and the wetland carbon sink time change characteristics are analyzed. And estimating the wetland carbon sink potential of the Hongze lake.

The invention discloses a method for evaluating the environmental risk of perfluorooctane sulfonic acid by using euglena gracilis, and belongs to the field of environmental pollutant biological monitoring and ecotoxicology evaluation. The method comprises the following steps: detecting the photosynthetic activity and the movement ability of Euglena gracilis exposed to perfluorooctane sulfonic acid with environmental concentration and the organelle distribution condition of the perfluorooctane sulfonic acid in euglena gracilis cells in situ; the photosynthetic activity comprises in-situ monitoring of chlorophyll fluorescence parameters and maximum non-photochemical quenching coefficient of euglena gracilis, and quantitative analysis of chloroplast pigment content; the movement ability comprises horizontal light-driving movement speed and vertical light-driving movement speed measurement of the euglena gracilis and flagellum shedding rate calculation. The method breaks through the limitation of a traditional single autotrophic mode, reveals a regulation and control mechanism of nutritional conditions on toxic effects, provides a scientific basis for water environment safety monitoring and optimization, and has the advantages of being convenient to operate and low in cost.

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

The invention provides an open environment crop chlorophyll fluorescence parameter imaging detection method and device, and the method comprises the steps: carrying out the crop canopy segmentation of a crop canopy hyperspectral image based on a crop canopy mask image, and obtaining a crop canopy image; performing hierarchical segmentation based on the crop canopy image according to a clustering algorithm to obtain an upper-layer leaf image and a lower-layer leaf image; determining upper-layer chlorophyll fluorescence intensity of the upper-layer leaf image and lower-layer chlorophyll fluorescence intensity of the lower-layer leaf image; respectively compensating the upper chlorophyll fluorescence intensity and the lower chlorophyll fluorescence intensity based on the balanced brightness and crop absorption photosynthetically active radiation to obtain an upper chlorophyll fluorescence signal correction value and a lower chlorophyll fluorescence signal correction value; and constructing a visual chlorophyll fluorescence parameter spatial distribution image based on the upper-layer chlorophyll fluorescence signal correction value and the lower-layer chlorophyll fluorescence signal correction value. The method can realize rapid and accurate imaging detection of the chlorophyll fluorescence parameters of the crop canopies in the open environment.

The invention relates to a device and a method for producing a microalgae liquid fertilizer by utilizing monosodium glutamate wastewater, and belongs to the technical field of microalgae fertilizer production devices and methods. Comprising a dilution device, a buffer pool and a pipeline type photobioreactor which are sequentially communicated through a pipeline, a culture medium inlet and a monosodium glutamate wastewater inlet are formed in the dilution device, and a chlorophyll luminoscope, an ultraviolet visible light detector and a water quality tester are installed on an output pipeline of the pipeline type photobioreactor. And the operation of the buffer pool, the tube (channel) type photobioreactor, the chlorophyll luminoscope, the ultraviolet visible light detector and the water quality tester is controlled by the console. According to the method, a culture medium suitable for microalgae growth is prepared by diluting monosodium glutamate wastewater in a certain proportion and adding necessary nutrient elements, chlorella capable of rapidly proliferating in the monosodium glutamate wastewater is cultured by using a photobioreactor, and the chlorella whole-cell sap can promote wheat seedling growth and rooting through tests.

The application provides a tobacco virus classification model construction method based on tobacco leaf hyperspectrum, and belongs to the technical field of tobacco virus classification models. Fractional differential spectrum enhancement processing is adopted, persistent homology eigenvalues and Betti number sequence values are calculated through spectrum topological manifold embedding of enhanced spectrum data, dimension reduction is performed through kernel principal component analysis to obtain topological embedding feature vectors, fusion feature vectors are output through a multimodal attention fusion module in combination with thermal infrared temperature field data and chlorophyll fluorescence kinetic curve data, data enhancement is performed on minority class samples by using a spectral generative adversarial network, the fusion feature vectors are input into a virus classification and recognition model adopting a focal loss function and a dynamic network adjustment mechanism for training, virus type classification and disease severity grade recognition of tobacco leaves to be tested are realized, and the problem that different types of tobacco viruses cannot be accurately recognized and distinguished in the early stage of disease is solved.

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 invention provides a rotating speed dynamic adjustment multispectral seedling growing system, which comprises a rotating platform, a water supply device, a visual acquisition device and a light supplementing lamp group, the rotating platform comprises a tripod and a rotating controller, a motor is fixed outside the tripod, a circulation driving assembly is arranged inside the tripod, and a rotating shaft of the motor is in driving connection with the circulation driving assembly. A plurality of seedling trays are hinged to the circulation driving assembly; an encoder is arranged on a rotating shaft of the motor, the motor and the encoder are connected with the rotation controller through circuits, and an information processing controller, a wireless communication module, a luminous flux sensor and a chlorophyll fluorescence sensor are installed on the seedling raising tray; the information processing controller transmits information to the rotation controller through wireless communication. And the visual acquisition device and the light supplementing lamp group are fixed above the rotating platform and are electrically connected to the rotating controller. According to the greenhouse seedling raising device, maximum space utilization of greenhouse seedling raising and uniformity of seedling emergence are achieved through optimal design of the device.

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 present application relates to the technical field of agricultural drought monitoring and precision irrigation, and particularly relates to a crop water stress diagnosis method and system based on sunlight-induced chlorophyll fluorescence, which collects crop canopy oxygen absorption band spectral data through a hyperspectral sensor, and calculates sunlight-induced chlorophyll fluorescence intensity SIF values by using a fluorescence extraction algorithm. The SIF value of a non-water-stressed crop canopy is taken as a reference canopy SIF value, the difference between the SIF value of a to-be-measured crop canopy and the reference canopy SIF value is determined, then a relative SIF water deficit index SIFWDI is generated by combining a preset formula, a regression analysis model is used to establish a mapping relationship between the SIFWDI and a soil water deficit index to output a diagnosis report, a variable irrigation decision and early warning are triggered according to a stress level, and model parameter adaptive optimization is realized through a feedback mechanism. The system realizes non-destructive, real-time and high-precision water stress monitoring and irrigation management.

The invention discloses a method for extracting spring phenology of an evergreen broad-leaved forest, and relates to the technical field of ecological observation, and the method comprises the following steps: carrying out negative pixel correction processing and time interpolation processing on collected sunlight-induced chlorophyll fluorescence data; fusing the processed sunlight-induced chlorophyll fluorescence data with the temperature variable data, constructing a time sequence, and performing fitting processing to obtain a target time sequence; extracting spring phenology of the evergreen broad-leaved forest by adopting a plurality of preset vegetation phenology extraction methods based on the target time sequence; performing precision evaluation on the extracted spring phenology of the evergreen broad-leaved forest by using ground observation data; and based on a precision evaluation result, determining the optimal spring phenology of the evergreen broad-leaved forest from the extracted spring phenology of the evergreen broad-leaved forest. According to the method, through negative value pixel correction and time interpolation processing, sunlight-induced chlorophyll fluorescence data and temperature variable data are fused, and the combination of a multi-fitting method and a multi-extraction method is adopted, so that the accuracy of a result is improved.

The application discloses a water-carbon flux optimization method based on carbonyl sulfide (COS) and sun / solar-induced chlorophyll fluorescence (SIF), and belongs to the technical field of land ecosystem model optimization and application. The application combines COS and SIF observation information, adopts a four-dimensional variation algorithm to perform data assimilation on observation data and simulation data, simultaneously restrains plant photosynthesis processes and gas transmission processes, and then realizes joint optimization of carbon flux and water flux. Meanwhile, the application compares and verifies simulation data before and after optimization and observation data, and proves effectiveness and reliability of the method.

The utility model discloses a chlorophyll luminoscope capable of dark adaptation, which comprises a light shield which is of a lightproof hollow structure, the top of the light shield is provided with a measuring hole, and the bottom of the light shield is provided with a hollow part; the leaf pressing assembly is arranged in the light shield and is used for pressing the to-be-detected plant penetrating through the hollow part to a plane; the fluorescence measurement part is arranged above the measurement hole and is used for carrying out dark adaptation and measurement on the plant to be measured through the measurement hole; the baffle plate is movably arranged on the light shield and is positioned between the measuring hole and the hollow part; when the baffle plate is located at the first position, the baffle plate can shield the measuring hole; and when the baffle plate is located at the second position, no shielding exists between the measuring hole and the hollow part. According to the utility model, large samples such as whole plants or lawns can be subjected to dark adaptation and measurement, and the application range of the luminoscope is expanded.

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 kind of evergreen broad-leaved forest spring phenology extraction method, it is related to ecological observation technical field, the method comprises: to the collected sunlight-induced chlorophyll fluorescence data is handled with negative value pixel correction and time interpolation processing;The sunlight-induced chlorophyll fluorescence data after processing is fused with temperature variable data, constructs time series and is handled to obtain target time series;Based on target time series, a variety of preset vegetation phenology extraction methods are used to extract evergreen broad-leaved forest spring phenology respectively;The accuracy of the extracted evergreen broad-leaved forest spring phenology is evaluated using ground observation data;Based on the accuracy evaluation result, the optimal evergreen broad-leaved forest spring phenology is determined from the extracted evergreen broad-leaved forest spring phenology.The application improves the accuracy of the results by negative value pixel correction and time interpolation processing, the fusion of sunlight-induced chlorophyll fluorescence data and temperature variable data, the combination of multiple fitting methods and multiple extraction methods.