80 results about "Hyperspectral reflectance" patented technology

The invention discloses an optical performance detection system and method for a camouflage material, and relates to the technical field of camouflage material detection, and the system comprises a structure identification and illumination control module which is used for collecting a surface image of a camouflage material sample, identifying the surface structure of the sample, starting a full-spectrum light source, and simulating step-by-step illumination from weak light to strong light; carrying out RGB imaging based on the standard illumination; and the region division module is used for applying a superpixel segmentation algorithm to the image, automatically dividing sub-regions, performing hyperspectral reflectivity scanning on each region, synchronously acquiring a light intensity image in a polarization direction, calculating a Stokes vector and constructing a polarization enhanced image. According to the method, frequency domain quantitative analysis of microcosmic interference fringes and accurate mask extraction of polarization abnormal areas can be realized, optical exposure risks of camouflage materials under complex illumination and visual angle conditions are effectively quantified through multi-angle reflection sampling and scoring function modeling, and the detection precision, the area pertinence and the automation level are remarkably improved.

The invention relates to the technical field of shoe body detection, in particular to a shoe body quality detection method based on image visual analysis, which comprises the following steps: synchronously acquiring three-dimensional contour and surface spectral information of a to-be-detected shoe body through three-dimensional scanning and spectral imaging equipment to obtain original point cloud spectral data; performing registration and fusion processing on the original point cloud spectral data to construct a registered hyperspectral three-dimensional grid model containing space coordinates and hyperspectral reflectivity values; and calculating a normal vector field and a curvature distribution diagram of the surface based on the hyperspectral three-dimensional grid model so as to quantify geometric features of the surface of the shoe body. According to the invention, through registration and fusion processing, surface spectral information collected by a spectral imaging device is reversely projected and interpolated to each space coordinate vertex of an initial three-dimensional point cloud constructed by a three-dimensional scanning device, so that a registered hyperspectral three-dimensional grid model is constructed, and a surface chemical characteristic spectrum is endowed with three-dimensional space geometric coordinates. And the detection capability is improved.

The embodiment of the invention provides a hyperspectral inversion method, system and equipment for the iron content of soil and a medium, and belongs to the field of soil analysis. The method comprises the following steps: selecting a plurality of measuring points in a to-be-measured soil sample area to collect surface soil samples; performing spectral measurement on the surface soil sample to obtain spectral reflectivity, and performing data transformation on the spectral reflectivity; constructing a correlation curve of the spectral reflectance at different wavelength intervals after data conversion and the iron content of the soil, selecting the wavelength with the highest correlation coefficient as an initial inversion index, and constructing a hyperspectral inversion model of the iron content of the soil; and inverting the iron content of the to-be-detected soil sample area based on the soil iron content hyperspectral inversion model. The soil iron content hyperspectral inversion model suitable for different space-time ranges is established by performing simulation transformation on the soil hyperspectral reflectivity and analyzing a wave band sensitive to the soil iron content, so that space and time barriers of soil spectral data in soil iron content research are broken, and high data sharing is realized.

The invention belongs to the technical field of image processing, and particularly relates to an intelligent agricultural product pesticide residue detection method and system based on a spectrum technology, and the method comprises the steps: obtaining the hyperspectral reflectivity according to the original hyperspectrum, dark current and whiteboard image data; calculating an illumination confidence factor by using the average spectral intensity; performing linear scaling on the obtained standard biological matrix background spectrum according to the illumination confidence factor to obtain a local background estimated value and obtain a net residual spectrum; generating a pesticide residue distribution thermodynamic diagram by combining the cosine similarity of the net residual spectrum and the target pesticide standard fingerprint spectrum and the noise suppression weight based on the illumination confidence factor; and pesticide residue detection is carried out according to the statistical characteristics of the connected region of the suspected defect mask. Through adaptive background deduction and noise suppression, the problems of uneven curved surface illumination and strong background interference are solved, and the detection accuracy is improved.

The invention discloses a visible light hyperspectral measurement method based on multispectral sensing, and belongs to the technical field of spectrum detection. According to the method, visible light eight-channel data is collected through a multispectral sensor, after optical crosstalk correction and filtering preprocessing, key principal components are reserved through PCA dimension reduction, a multispectral and hyperspectral mapping model is constructed through an SVR algorithm, and finally the hyperspectral reflectivity is reconstructed through PCA inverse transformation. The problems that traditional hyperspectral equipment is high in cost and poor in portability are solved, low-cost and high-precision visible light hyperspectral measurement is achieved, and the device and the method can be widely applied to food freshness detection, agricultural product quality evaluation and other scenes.

The invention provides a Mars mineral abundance inversion method and system. The Mars mineral abundance inversion method comprises the steps of firstly obtaining target Mars hyperspectral reflectivity data; and then generating simulated Mars hyperspectral image data based on the target Mars hyperspectral reflectivity data. Secondly, training the constructed mineral abundance inversion deep learning model based on the simulated Mars hyperspectral image data, and determining the trained mineral abundance inversion deep learning model; thirdly, acquiring to-be-processed Mars hyperspectral image data, and performing preprocessing; and finally, performing mineral abundance inversion on the preprocessed to-be-processed Mars hyperspectral image data through the trained mineral abundance inversion deep learning model to obtain a mineral abundance inversion result. Therefore, the weight distribution of each wave band of the hyperspectral image can be enhanced through the mineral abundance inversion deep learning model, and the non-linear modeling capability and the wave band characteristic response capability of abundance inversion are improved, so that the accuracy and the efficiency of Mars mineral abundance inversion can be effectively improved.

The invention provides a method for calibrating hyperspectral reflectivity data of field tea ridges, which comprises the following steps of: acquiring outdoor hyperspectral data and indoor hyperspectral data of the field tea ridges, and performing cubic B spline fitting on the indoor hyperspectral data; according to the wave band position and number of the outdoor hyperspectral data, the reflectivity of the corresponding wave band position is obtained from the indoor spectral reflectivity curve, and an indoor standard reflectivity curve is obtained. Searching the modulation coefficient of the k-th iteration under the condition that the constraint function obtains the minimum value; calculating a correction factor function according to the modulation coefficient of the kth iteration; substituting the training set and the correction factor function into a calibration formula, and substituting the verification set, the kth environmental interference error and the correction factor function into the calibration formula; and if the kth residual matrix meets the preset condition, iteration is stopped. According to the method, the optimal modulation coefficient can be searched, so that the outdoor spectral reflectivity can be calibrated adaptively, and the outdoor hyperspectral data of the field tea ridge can be measured more accurately.

The invention relates to the technical field of structural safety detection of steel structural members, in particular to a steel structural damage identification and repair method, system and device and a medium, and the method comprises the steps: obtaining a multi-source image data set and a standard corrosion test block database of a steel structural member through an unmanned aerial vehicle, the multi-source image data set comprises a polarized visible light image, a long-wave infrared temperature matrix and hyperspectral reflectivity data, and the standard corrosion test block database comprises spectral feature vectors of different corrosion grades and corresponding physical parameters; and carrying out sub-pixel level registration and feature extraction on the multi-source image data set. According to the method, the unmanned aerial vehicle synchronously carries the three cameras to carry out real-time multi-dimensional data acquisition, the problem of data splitting in traditional single-mode detection is solved, the multi-spectral data acquisition efficiency and the sub-millimeter level hidden damage identification precision are remarkably improved, and dynamic environment interference is inhibited in real time through an intelligent parameter inversion mechanism.

The invention discloses an automobile wire harness detection system based on artificial intelligence and machine vision, and relates to the technical field of intelligent detection, and the system comprises a feature extraction module which carries out the noise reduction and feature enhancement of hyperspectral reflection data through a low-rank sparse decomposition method, extracts the principal component features and local sparse features of an automobile wire harness sheath, and carries out the recognition of the local sparse features of the automobile wire harness sheath; spectral features are formed; the material identification module is used for identifying the category of the automobile wire harness sheath material according to the similarity distribution of the spectral characteristics in the multi-dimensional spectral space; the state analysis module is used for combining the spectral characteristics with the types of the automobile wire harness sheath materials, identifying the surface state of the automobile wire harness sheath and carrying out quantitative analysis to obtain an aging grade evaluation result; and the quality evaluation module is used for carrying out comprehensive quality evaluation on the automobile wire harness sheath according to the automobile wire harness sheath material category and the aging grade evaluation result, and generating a detection report. According to the invention, the capability of distinguishing aging modes of different materials is remarkably improved.

The invention discloses a fuzzy theory fused tongue diagnosis characteristic quantitative analysis method and system, and relates to the technical field of traditional Chinese medicine tongue diagnos.The method comprises the steps that a micro piezoelectric sensor array and a hyperspectral imaging module are integrated, tongue surface Young modulus distribution data and hyperspectral reflectivity data are synchronously collected, and a multi-mode tongue picture fusion data set is generated; further performing crack region segmentation and super-resolution reconstruction through a neural network model, outputting a crack network diagram and a tongue papilla form entropy, generating topological fingerprints in combination with continuous homology analysis, identifying a mechanical gradient mutation region and extracting a metabolic index, constructing a mechanical-metabolic associated feature set, and obtaining a mechanical-metabolic associated feature set; topological fingerprints and mechanical-metabolic characteristics are fused in a three-dimensional space-time coordinate system, a dynamic intuitionistic fuzzy decision field is constructed, finally, tongue picture pathology evolution risk indexes and a visual early warning report are output through a pathology prediction model, and the system provides quantifiable decision support for'prevention before illness' in traditional Chinese medicine.

The present invention discloses a method for improving the prediction accuracy of soil iron oxide based on an amplified hyperspectral dataset. The method comprises the following steps: amplifying the hyperspectral dataset and performing multiple fractional-order differential transformations on the original hyperspectral spectrum using a fractional-order differential algorithm; then combining the fractional-order differential spectra with the original spectrum to form different amplified hyperspectral databases to amplify the amount of original spectral data; establishing a 1D-CNN neural network model for each of the different amplified hyperspectral databases, where the independent variable is the hyperspectral reflectance value and the dependent variable is the soil iron oxide content; and selecting the optimal 1D-CNN model based on the models established for the different amplified databases. The present invention effectively solves the problem of low accuracy of deep learning models under small sample conditions, improves the applicability and effectiveness of deep learning models to a certain extent, effectively improves the existing prediction accuracy of soil iron oxide based on hyperspectral data, and provides new methods and ideas for hyperspectral prediction research of other soil properties.

The invention relates to the technical field of paper detection, in particular to a paper finished product detection system based on a framework paper production process. By extracting defect information, modeling based on a graph structure and carrying out graph convolution operation to smooth an image, a defect area is accurately identified. Large-scale trend and small-scale local anomaly are separated through a wavelet decomposition technology, errors caused by environmental factors are eliminated through a local adaptive regression method, and the accuracy of thickness deviation is ensured. The density uniformity is calculated through ultrasonic feature extraction, the hyperspectral reflectivity is corrected in combination with a physical compensation method of chemical residues, and the chemical residue index is accurately obtained. The comprehensive quality index of the paper is obtained through comprehensive index calculation, so that whether the finished paper is qualified or not is judged, and a basis is provided for paper production adjustment of a production line.

The invention relates to a tea tree chlorophyll content inversion method based on hyperspectral remote sensing. The method comprises the following steps: S1, acquiring remote sensing reflectivity data of tea tree leaves in a hyperspectral band; s2, performing spectral derivative processing on the hyperspectral reflectivity data obtained in the S1, and extracting enhanced spectral feature information; s3, selecting at least one characteristic wave band or wave band combination related to the chlorophyll content of the tea tree from the spectral derivative data; s4, establishing an inversion model between the chlorophyll content and the characteristic wave band reflectivity value; and S5, calculating the chlorophyll content of the target tea tree based on the inversion model and the hyperspectral remote sensing data. According to the method, rapid, lossless and high-precision monitoring of the chlorophyll content of the tea tree is realized, and the chlorophyll inversion precision and reliability are improved. The method has the advantages of high monitoring precision, abundant information amount, high model adaptability and the like, and can be widely applied to the fields of tea garden growth monitoring, nutrient diagnosis, precise fertilization, tea tree physiological research and the like.

The invention belongs to the technical field of nondestructive testing of plant diseases, and discloses a nondestructive testing method and system for Candidatus Liberobacter asiaticum based on hyperspectral reflection and transmission fusion, and the method comprises the following steps: respectively obtaining hyperspectral reflection spectrum data and hyperspectral transmission spectrum data of to-be-detected citrus leaves, single reflection characteristic wave band data and single transmission characteristic wave band data are obtained after preprocessing and wave band selection; based on the single reflection characteristic wave band data, the single transmission characteristic wave band data and the spectrum data after the characteristic level fusion of the single reflection characteristic wave band data and the single transmission characteristic wave band data, obtaining a first prediction calculation result, a second prediction calculation result and a third prediction calculation result through respective optimal classification models; and determining a corresponding weight according to the classification accuracy of each optimal classification model, and performing decision-level fusion calculation on the first, second and third prediction calculation results to obtain the citrus Huanglongbing grade. According to the method, feature level and decision level fusion is introduced, so that the method has the capability of distinguishing the surface physical form and the internal structure components of the sample, and can be used for rapidly and nondestructively detecting the citrus huanglongbing.

The invention provides a wheat leaf area index whole growth period estimation method based on canopy pure hyperspectral reflectivity, which comprises the following steps of: firstly, extracting wheat canopy spike, leaf and soil pure pixels based on an RGB (Red, Green and Blue) image combined with a deep learning algorithm; secondly, separating the canopy pure leaf hyperspectral reflectivity by using the extracted pure pixels and canopy hyperspectral reflectivity data in combination with a non-negative matrix factorization algorithm; and finally, calculating a vegetation index based on the canopy pure leaf hyperspectral reflectivity, and establishing a whole-growth-period LAI estimation model in combination with a simple linear regression algorithm. According to the method, the problem that the LAI inversion precision is reduced after heading is solved by combining near-end remote sensing and a machine learning algorithm, and the wheat LAI estimation method based on the canopy pure hyperspectral reflectivity has the potential of accurately predicting the LAI of the wheat in the whole growth period and can be expanded to accurate management of field crops and breeding of high-yield wheat varieties.

The invention discloses a coastal salt marsh wetland non-photosynthetic vegetation coverage inversion method, which comprises the following steps: S1, obtaining hyperspectral reflection curves of soil, non-photosynthetic vegetation and photosynthetic vegetation of the coastal salt marsh wetland of the Yellow River Delta, simulating and measuring spectral characteristics of samples under different moisture contents (dry, low humidity, medium humidity and saturation) in a laboratory, and calculating the coverage of the non-photosynthetic vegetation in the coastal salt marsh wetland; key wave bands of non-photosynthetic vegetation and other ground features are screened and distinguished; s2, constructing a moisture-insensitive hyperspectral NPV index (MINI), and determining an optimal wave band combination through wave band traversal; and S3, constructing mixed data of soil, non-photosynthetic vegetation and photosynthetic vegetation under different moisture conditions in a laboratory, and evaluating inversion precision and stability of MINI under different moisture conditions. S4, acquiring a coastal salt marsh wetland hyperspectral image, and calculating an NDVI value and an MINI index value based on the processed image; and S5, performing spectral unmixing on the mixed pixels based on a triangular space method, and inverting the non-photosynthetic vegetation, photosynthetic vegetation and soil coverage of the research area. The method provided by the invention solves the technical problems of low precision and poor stability of the traditional NPV index inversion coverage under the condition that the coastal salt marsh wetland is influenced by tides and the moisture change is obvious.

The invention discloses a copper alloy surface wiredrawing quality detection method and system, and the method comprises the steps: carrying out the multi-source defect feature collaborative analysis of hyperspectral reflectivity data, linear array image data and surface three-dimensional point cloud data, and obtaining an oxidation defect marking graph, a scratch distribution graph and a texture anomaly graph; inputting the oxidation defect mark graph, the scratch distribution graph and the texture anomaly graph into a pre-trained convolutional neural network, wherein the convolutional neural network outputs and obtains defect space coordinates; and calculating the unit area defect density, the maximum defect area and the deepest scratch depth according to the defect space coordinates, and determining the surface wire drawing quality of the copper alloy according to the unit area defect density, the maximum defect area and the deepest scratch depth. The industrial problems of multi-modal data splitting, missed judgment of tiny defects and misalignment of grading standards can be well solved.

A method for performing point-to-point whiteboard parameter ratio correction on a hyperspectral image includes: capturing hyperspectral data of a standard reference whiteboard in advance as white(x,y,w), and storing the records, then captures hyperspectral data of a sample as sample(x,y,w); then selecting an unobstructed and unshaded whiteboard area within a certain range of the hyperspectral data sample(x,y,w) of the test sample, and labeling the area as Area A; calculating a spectral average of the ROI on the sample image as SA(w); calculating a spectral average of whiteboard data in the same position as the ROI as WA(w); dividing the two spectral averages, and obtaining a correction coefficient alpha(w)=WA(w). / SA(w); multiplying the whiteboard parameter ratio correction coefficient alpha(w) by a sample reflectance image matrix after whiteboard parameter ratio correction to obtain a final hyperspectral reflectance image matrix REFL(x,y,w)=alpha(w).*sample(x,y,w). / white(x,y,w).

The invention provides a hyperspectral data-based automatic extraction method and a hyperspectral data-based automatic extraction system for tetracalamus mongolicus, and the method comprises the steps: building a data collection platform, collecting the ground canopy hyperspectral reflectivity of each species and the hyperspectral image data of an unmanned aerial vehicle in a target region, and synchronously collecting the geographic coordinates of each shrub; carrying out dimension reduction processing on the ground canopy high-spectral reflectivity data and constructing classification characteristic variables of the tetragos, wherein the classification characteristic variables comprise a vegetation index variable, a continuum removal parameter variable and a high-spectral characteristic parameter variable; screening key feature variables of the classification feature variables of the tetrazygote; a vegetation index threshold value classification method and a classification method based on machine learning are adopted to construct classification models to extract the tetragos in the target area, and an optimal classification model is selected; and based on the optimal classification model, combining the hyperspectral image data of the unmanned aerial vehicle to carry out identification and classification on the quadrangle of the target area, and obtaining space distribution data of the quadrangle. By means of the method, rapid, accurate and automatic extraction of the quadrangle tree can be achieved.

The invention belongs to the field of functional ceramic materials, and relates to a boron nitride fiber-based ceramic material as well as a preparation method and application thereof. The material is prepared by calcining BN powder at 700-900 DEG C for 12-48 hours, and the content of B2O3 is 1t; the BN powder is of a fibrous structure, the radial width of the BN powder is 1-5 microns, and the length-diameter ratio of the BN powder is gt; 10: 1. The preparation method comprises the following steps: dissolving a B source and an N source in a low-hydroxyl-density cosolvent-water mixed solvent, evaporating the solvent or cooling to obtain a precursor product, calcining the product in NH3-N2 to obtain fibrous BN powder, and calcining the molded powder to obtain the BN fiber-based ceramic material. The material has good high-temperature oxidation resistance, thermal shock resistance, heat conduction anisotropy and ultraviolet-visible-near infrared band hyperspectral reflectivity, and can be applied to a thermal protection layer of a spacecraft, a thermal protection layer of a high-temperature pipeline and a reactor or a high-energy laser protection layer.

This invention discloses a method for inverting equivalent water thickness in plant leaves across species. This method selects equivalent water thickness with more explicit physical meaning as a representative indicator of plant leaf water content. Based on traditional vegetation indices, it combines continuous removal of preprocessing parameters to construct absorption feature parameters, thereby reducing noise in hyperspectral data while deeply mining its inherent spectral characteristics. It innovatively introduces one-heat coding technology to explicitly process species information and employs a Bayesian optimized machine learning model, effectively improving the accuracy, stability, and computational efficiency of cross-species water inversion. This invention can accurately invert the water status of crops across species, providing a new method for rapidly acquiring crop water status based on hyperspectral reflectance characteristics. It can be widely applied in intercropping and relay cropping patterns and large-scale survey and analysis scenarios, greatly improving the accuracy and speed of water stress diagnosis.

A non-invasive ophthalmic light-based detection device for detecting pathologies associated with Alzheimer's disease (AD) in the retina of an eye. The device uses two imaging modalities, one of which directs a specific area to be interrogated by the second imaging modality. A hyperspectral reflectance imaging unit detects light reflected and / or backscattered from the retina from a broadband light source, which is used to determine one or more regions of interest. A Raman spectroscopy unit detects light from a laser that is re-emitted by the retina through the phenomenon of Raman scattering, which is targeted to the region of interest determined based on the hyperspectral reflectance information. The detection information from the hyperspectral reflectance imaging unit and the Raman spectroscopy unit is used to determine the presence of the one or more pathologies associated with AD. The detection device allows identification of high-risk groups, diagnosis and tracking of patient response to treatment.

The invention discloses a three-white melon quality detection method and system based on a hyperspectral image and a medium, and relates to the technical field of melon quality detection. Comprising the following steps: screening three-white melon samples meeting quality requirements, obtaining physiological characteristic parameters and appearance characteristic parameters of the three-white melon samples, extracting hyperspectral reflectivity data, and generating a sample detection library; the method comprises the following steps: acquiring a hyperspectral image of a to-be-detected three-white melon, extracting reflectivity data, comparing the reflectivity data with data in a sample detection library, and calculating a similarity index; performing primary classification on the three white melons to be detected based on the similarity index, and performing subsequent classification on the melons meeting the requirements. The appearance characteristic parameters of the three-white melon to be detected are compared with the sample library mean value to obtain a physiological deviation index and an appearance deviation index, and the two indexes are comprehensively analyzed to judge whether the quality of the three-white melon to be detected meets the requirement or not. And the quality evaluation accuracy and effectiveness of the three-white melons are improved.

The application provides a kind of pellet quality detection method and system, related to the technical field of pellet quality detection, the method comprises: obtaining the hyperspectral reflectance image sequence and infrared thermogram sequence of single pellet in the conveying process, extracting the spectral feature vector of each pellet in the target wave band from the hyperspectral reflectance image sequence;From the infrared thermogram sequence, extract the rolling posture data of each pellet, reconstruct the three-dimensional surface temperature field distribution of pellet using rolling posture data, extract temperature gradient features and cooling rate features in three-dimensional surface temperature field distribution;The spectral feature vector is spatiotemporally registered with temperature gradient features and cooling rate features, and a multi-modal feature tensor is formed by fusion. The quality grade is obtained by inputting the multi-modal feature tensor into the pre-trained quality evaluation model. The application can improve the reliability of pellet quality detection results.

The present application belongs to the technical field of agricultural remote sensing and crop yield estimation, and specifically discloses a winter wheat yield estimation method based on a combination of spectral characteristic indexes in key growth periods. The method is based on winter wheat canopy hyperspectral reflectance data, and first constructs spectral characteristic indexes FVI and a spectral characteristic index set. Then, the core indexes related to yield estimation are obtained through progressive screening, and the index combination optimization is carried out in each growth period respectively, and further growth period combination optimization is carried out, so as to determine the target key growth period combination for yield estimation. On this basis, a cross-period dynamic feature is further constructed, and a two-stage XGBoost winter wheat yield estimation model oriented to key growth period information utilization is established, so as to realize accurate estimation of winter wheat yield.

This invention relates to the field of optical monitoring technology for water environment, and discloses a method for analyzing and characterizing eutrophication components of water bodies based on hyperspectral feature inversion. The method includes: retrieving the intrinsic absorption spectrum sequence of pure water as a physical constraint benchmark; calculating the ratio of the hyperspectral reflectance to be measured to the benchmark to generate a modulation vector; determining the fractional-order differential sequence related to the sampling wavelength; extracting trough features through morphological baseline correction; completing the fractional-order differential transformation by combining the order sequence; extracting the characteristic trough depth and skewness parameters; and outputting the concentrations of chlorophyll a and colored soluble organic matter using an unmixing model. This invention utilizes the benchmark to drive the dynamic evolution of the differential operator, achieving topological separation of the scattering background and trace component absorption characteristics in different bands, suppressing local waveform distortion caused by background scattering heterogeneity, and improving the accuracy of component inversion under complex matrices.

The application belongs to the technical field of plant disease nondestructive detection, and discloses a nondestructive detection method and system for citrus Huanglongbing based on hyperspectral reflection and transmission fusion, which comprises the following steps: acquiring hyperspectral reflection spectrum data and hyperspectral transmission spectrum data of a to-be-detected citrus leaf, obtaining single reflection characteristic band data and single transmission characteristic band data after pretreatment and band selection; based on the single reflection characteristic band data, the single transmission characteristic band data and the spectrum data after feature-level fusion of the two, first, second and third prediction calculation results are obtained through respective optimal classification models; the corresponding weights are determined according to the classification accuracies of the optimal classification models, and the first, second and third prediction calculation results are subjected to decision-level fusion calculation to obtain the Huanglongbing grade of the citrus. The method introduces feature-level and decision-level fusion, has the discrimination ability of sample surface physical form and internal structure composition, and can quickly and nondestructively detect the Huanglongbing of the citrus.

The invention discloses a deep learning-based carbon nitrogen ratio estimation method for hyperspectral reflectivity of a leaf, and belongs to the technical field of agricultural phenotype monitoring. The core of the method lies in that a spectrum adaptive multi-scale convolutional neural network is constructed, and a network framework sequentially comprises an integral-differential collaborative feature extraction module used for enhancing local and global features of a spectrum; the multi-scale spectrum convolution module is used for extracting multi-scale absorption characteristics related to the carbon and nitrogen substances; the spectrum adaptive self-attention module is used for modeling inter-band long-range dependence and focusing a key spectrum area; and the KAN module is used for high-precision fitting of the complex nonlinear relation between the spectral characteristics and the carbon nitrogen ratio. According to the invention, full-band spectral information is fully utilized through the architecture, the problems of insufficient feature utilization and low model precision in the prior art are solved, lossless and high-precision estimation of the carbon-nitrogen ratio of the leaf is realized, and the method is suitable for precision agriculture and ecological environment monitoring.

The invention provides a corn biomass hyperspectral estimation method and system based on fractional order differential optimization. The method comprises the following steps: collecting corn planting data of a target area; processing the original hyperspectral gray value image to obtain a multiband surface reflectance image, and obtaining original hyperspectral reflectance through ROI (Region of Interest) extraction and SG filtering; performing FOD fractional order differential transformation on the original hyperspectral reflectivity before and after the NDVI mask processing to obtain FOD spectral reflectivity characteristics under different orders; constructing a plurality of dual-band spectrum indexes and a plurality of three-band spectrum indexes, and performing high-dimensional feature screening on the plurality of dual-band spectrum indexes and the plurality of three-band spectrum indexes to form a candidate feature set; and by taking the candidate feature set as an input variable and the aboveground biomass actually measured on the ground as an output variable, constructing an aboveground biomass estimation model, carrying out training optimization on the aboveground biomass estimation model, and outputting an optimal estimation strategy of the aboveground biomass of the corn.

This invention belongs to the field of environmental monitoring technology and discloses a remote sensing inversion method for dissolved inorganic nitrogen and silicate in estuaries based on salinity synergy. The method includes the following steps: S1. During a field survey in the land-sea interaction zone of the estuary, remote sensing reflectance, salinity, and nutrient data are collected simultaneously. The measured hyperspectral reflectance is simulated as the equivalent reflectance of the satellite band using the spectral response function of the target satellite, which is used to construct the training and validation datasets for the model. S2. A nonlinear inversion model of remote sensing reflectance and salinity is established, and a nutrient mixture model of salinity and nutrients is constructed. The training dataset is used for model training. S3. The satellite remote sensing reflectance data of the estuarine area to be predicted is input into the trained nonlinear inversion model and combined with the nutrient mixture model to generate the spatial distribution of DIN concentration and DSi concentration in the estuarine area. This method achieves high-precision remote sensing inversion prediction of DIN concentration and dissolved silicate DSi concentration in the estuarine area.