770 results about "Multispectral image" patented technology

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention discloses a winter wheat LAI and SPAD estimation method based on a lightweight semi-supervised model, and relates to the technical field of agricultural remote sensing monitoring, and the method comprises the steps: obtaining multispectral image data of a winter wheat key growth period, and carrying out the preprocessing of the multispectral image data to generate a standardized multichannel vegetation index image; the method comprises the following steps: constructing a lightweight semi-supervised model MCVI-SANet, and carrying out self-supervised training on the MCVI-SANet by adopting a semi-supervised training strategy driven by VICReg; and inputting the multi-channel vegetation index image into the trained MCVI-SANet, and outputting quantitative estimation results of the LAI and SPAD of the winter wheat. Through combination of a saturation perception mechanism and semi-supervised learning, estimation deviation caused by dense canopy vegetation index saturation and data noise is effectively eliminated, and the estimation precision and generalization ability in a complex agricultural scene are improved while the lightweight deployment characteristic of the model is ensured.

The invention discloses a poplar degenerated forest stand accurate restoration method, and relates to the technical field of poplar degenerated forest stand restoration, and the method comprises the steps: obtaining a multi-temporal satellite remote sensing image and an unmanned plane multi-spectral image of a target area; in combination with soil moisture content, rhizosphere respiration rate and micrometeorological data collected by ground Internet of Things sensing nodes arranged in a forest stand, geographic coordinate registration and time sequence alignment are performed on the data to form a multi-source observation data set; the method comprises the following steps: extracting vegetation indexes, short-wave infrared reflectivity, soil moisture and rhizosphere physiological parameters based on a multi-source observation data set, constructing a degradation diagnosis index fusing spectral change, moisture stress and root activity, calculating the degradation degree of each pixel, and generating a degradation level spatial distribution diagram; the area with the degradation degree reaching a set threshold value is determined as a to-be-repaired area; and selecting representative poplar individuals in the to-be-repaired area, measuring the photosynthetic ability of the individuals, and calculating the individual competition intensity in combination with the three-dimensional point cloud data.

The invention belongs to the technical field of surveying and mapping and geographic information processing, and discloses a geographic entity intelligent identification and reconstruction system based on multi-source surveying and mapping data. The system is composed of a multi-source data acquisition and preprocessing module, a cross-modal feature coding and fusion module, a structural atlas construction and spatial logical reasoning module, a deformable neural modeling module and a physical prior guided collaborative prediction and closed-loop optimization module. According to the method, a cross-modal feature coding and fusion module is arranged, and a modal attention mechanism is introduced to dynamically weight multi-source data, so that heterogeneous information such as a laser point cloud, an inclined image and a multispectral image is fused into a unified coding vector in a high-dimensional space; compared with feature extraction performed by using a static deep network in a comparison file, the method of the invention adopts a minimum residual function to perform modal weight training, has an adaptive feature integration capability, and effectively improves the accuracy of geographic entity recognition and the robustness of boundary segmentation in different scenes.

The invention relates to a tartary buckwheat pest and disease damage dynamic monitoring method, system and device and a medium, and belongs to the technical field of agricultural intelligent monitoring, the dynamic monitoring method comprises the following steps: periodically collecting environmental parameter data through fixed sensor nodes deployed in a farmland, and obtaining leaf vibration signals and multispectral image data at the same time; performing space-time alignment on the blade vibration signal and the multispectral image data, correcting radiation distortion in the multispectral image data, and outputting a registration data set; according to the registration data set, fusing to generate a multi-modal feature vector, inputting the multi-modal feature vector into a pre-trained space-time analysis model, and outputting a risk level distribution diagram with a geographic coordinate mark; generating a control instruction set according to the risk level distribution map, and triggering execution equipment to execute pest control operation; and optimizing weight parameters of the space-time analysis model through the generative adversarial network based on the execution log of the control instruction set and the historical multi-modal feature vector. The scientificity and timeliness of pest control can be improved.

The invention discloses a karst tunnel lining quality intelligent detection and image analysis system, and relates to the technical field of image processing and defect identification, the system comprises a multispectral image acquisition module, a lining defect automatic identification engine and a defect quantitative evaluation platform, the multispectral image acquisition module acquires visible light and near infrared images and carries out fusion enhancement; the lining defect automatic recognition engine adopts a double-branch attention feature extraction network to recognize multiple types of defects, the defect quantitative evaluation platform calculates defect geometric features and evaluates severity, full-process automation of detection, recognition, evaluation and decision is achieved, the detection precision reaches the sub-millimeter level, the recognition accuracy exceeds 95%, and the method is suitable for large-scale popularization and application. And the detection efficiency is more than 10 times that of a manual method.

The invention discloses a multispectral image and LiDAR fusion method suitable for dam body deformation monitoring, and belongs to the field of hydraulic engineering safety monitoring. According to the method, a dam body surface multispectral image and three-dimensional point cloud data are obtained through an unmanned aerial vehicle, and crack spectral features and structural joint geometric features are extracted after processing; constructing a CNN and finite element coupling model, inputting the surface displacement field after feature fusion into the finite element model, and simulating internal stress-strain distribution in combination with material parameters; and analyzing the space-time correlation between the surface displacement and the internal stress, optimizing parameters by combining measured data, and outputting a deformation correlation result. According to the invention, all-directional correlation monitoring of the surface and internal deformation of the dam body is realized, the limitation of a single data source is overcome, the monitoring precision and reliability are improved, and support is provided for safety assessment of the dam body.

The invention discloses a photovoltaic panel surface pollution degree image evaluation system. The system comprises an image acquisition module, an image processing module, a multi-task evaluation module and a pollution degree determination module. Multi-spectral image data of the surface of a photovoltaic panel are collected, after radiation calibration and atmospheric correction processing, spectral reflectivity features and spatial texture features are extracted and fused, a multi-task deep learning model is input, a pollution type classification result and a power generation efficiency loss weight of each pixel region are output, and a pollution type classification result of each pixel region is obtained. And finally, generating a pollution type spatial distribution diagram, calculating the overall power generation efficiency loss percentage, and determining the pollution degree grade. According to the method, the problem that the mixed pollution type cannot be distinguished and the differentiation influence cannot be evaluated in the prior art is solved, accurate quantitative evaluation of the pollution degree is realized, and a reliable basis is provided for fine operation and maintenance of a photovoltaic power station.

The invention relates to the technical field of remote sensing image processing and computer vision, in particular to a hyperspectral remote sensing image reconstruction method based on iterative optimization and depth prior. According to the method, complementary information of a low-resolution multispectral image and a high-resolution panchromatic image is utilized, the spatial resolution and the spectral fidelity of the high-spectral image can be improved at the same time, and the complementary information of the low-resolution multispectral image and the high-resolution panchromatic image is effectively utilized; through a spectrum low-rank decomposition and alternate optimization strategy, the complexity of a high-dimensional optimization problem is reduced, and the stability of iterative solution is improved; and after the deep denoising network is introduced, the denoising capability and the feature expression capability of the subspace coefficient image are further enhanced, so that the generated hyperspectral image is superior to the existing method in the aspects of spatial details and spectral authenticity, and the hyperspectral image has relatively strong robustness and wide application prospects.

The invention discloses an unmanned aerial vehicle multispectral system error calibration and correction method based on a Boolean model, and belongs to the technical field of photogrammetry and remote sensing. Comprising the following steps: acquiring a multispectral image, acquiring original POS data, and performing band registration preprocessing on the image to generate a first orthoimage to confirm a system error magnitude; carrying out bundle method strict adjustment by using ground control points to obtain reference POS data, and solving seven-parameter conversion parameters; correcting the original POS data by using seven parameters and generating a high-precision orthoimage; and generating a simplified multispectral camera calibration and verification formula according to the difference value of the POS data before and after correction. Complex coordinate conversion is simplified into fixed value addition and subtraction operation, one-button calibration and batch correction of system errors of the multi-spectral camera of the unmanned aerial vehicle are achieved, the positioning precision is improved to the centimeter level from the meter level, dependence on ground control points is greatly reduced, and an efficient standardized solution is provided for large-scale engineering data processing.

The invention discloses a high-speed data cable product defect detection method and system based on machine vision. The method comprises the following steps: acquiring multispectral image data; denoising, enhancing and registering the multispectral image data to obtain a standard multispectral fusion image; extracting a region of interest containing potential defects from the standard multispectral fusion image based on an improved Vibe algorithm in combination with texture features of a cable, and removing a defect-free background region to obtain a region of interest image; constructing a multi-dimensional defect feature vector; and inputting the multi-dimensional defect feature vectors into the improved network model, outputting defect positions, types and confidence coefficients, automatically marking and grading cable products with defects according to an identification result, and generating a detection report. Various defects such as fine scratches, hidden oxidation and slight overheating are effectively recognized, and the recognition accuracy is improved.

The invention discloses a ceramic substrate surface defect intelligent identification method and system based on multispectral imaging, and the method comprises the following steps: synchronously collecting multispectral images under a plurality of visible light and near-infrared wavebands, constructing a multispectral image stack, and completing dark field deduction, registration and geometric correction processing; inputting the crack sensitive wave band combination image into a CrackFormer crack identification network, extracting slender structure characteristics and positioning a crack area; inputting the edge enhancement graph into an improved BDCN edge detection network, extracting the contour structure of the ceramic substrate, and identifying the position of a contour gap; a chromatic aberration defect area is extracted through brightness difference calculation; and fusing the identification results of the three types of defects, and outputting a defect mask pattern and structured defect information. According to the method, the multi-band image and the deep learning model are fused, and the accuracy and adaptability of ceramic substrate defect detection are improved.

The invention relates to the technical field of medical instrument detection, discloses a medical whole basket instrument multi-parameter detection system and method, and aims to solve the problem that in an existing CCD image detection technology, a high-brightness reflection area interferes with a detection result due to tiny bloodstain or superposition on the surface of an instrument. The system comprises an imaging module, a light source control module, a data processing module and a carrying platform. According to the method, reflection is reduced through combination of multispectral imaging and an adjustable light source, a reflection area is accurately identified and removed by using an algorithm of combining gradient amplitude and gradient direction entropy, and finally geometric parameters and cleanliness indexes of the instrument are synchronously calculated.

The invention relates to the technical field of industrial vision and artificial intelligence, and discloses an aluminum coating formula prediction method and system based on industrial vision and double-model fusion. The method comprises the following steps: acquiring visible light and near-infrared band images of the surface of an aluminum material coating through multispectral image acquisition, identifying a defect area to generate a binary mask, extracting three types of features of color, texture and spectral reflection, and respectively inputting feature vectors into a random forest regression model and a convolutional neural network model to obtain a target image; and dynamically calculating a fusion weight according to the verification set error, and carrying out weighted fusion on the prediction results of the two models to generate a formula component content prediction value. According to the method, the technical problems of low precision, low efficiency and insufficient generalization ability of a traditional aluminum coating formula determination method are solved, and rapid and accurate prediction of the formula is realized.

The invention discloses a lake cyanobacterial bloom intelligent early warning method based on unmanned aerial vehicle remote sensing and image recognition, and the method comprises the steps: fusing an unmanned aerial vehicle multispectral image and a convolutional neural network, extracting the spectral features of cyanobacteria, and calculating the concentration distribution; image registration and a dynamic model are combined to track water bloom boundary change and drift trajectory, satellite remote sensing is used to verify precision and invert biomass density, early warning levels are divided according to the precision and the biomass density, decision information is generated, and intelligent monitoring and accurate early warning of cyanobacterial bloom are realized. The comprehensive technical effects of water bloom dynamic monitoring, accurate early warning and efficient management are achieved, and a scientific basis is provided for water environment treatment.

The invention discloses a system and a method for rapidly screening illegal additives of health care products based on multispectral imaging, relates to the technical field of health care product detection, and aims to solve the problem of low detection accuracy caused by the fact that an embedding structure hinders spectral signal penetration in the prior art. The system comprises an embedding material characteristic analysis module, a targeted crushing parameter calculation module, a microcapsule crushing execution module, a multispectral imaging module and an illegal additive identification module, the embedding material characteristic analysis module is used for acquiring characteristic parameters of an embedding material in the microcapsule embedding type health care product, and the characteristic parameters comprise hardness, thickness, elastic modulus, thermal decomposition temperature, Poisson's ratio and thermal expansion coefficient of the embedding material; and the targeted fragmentation parameter calculation module is used for receiving the characteristic parameters and calculating the characteristic parameters through a multi-dimensional algorithm to obtain targeted fragmentation parameters. The method has the advantage that illegal additives in health care products can be quickly and accurately screened.

The invention discloses an early-stage intelligent monitoring system for citrus reiculata Blanco canopy diseases and insect pests based on multispectral imaging, and belongs to the technical field of agricultural information. The system comprises a multispectral imaging module, a three-dimensional point cloud acquisition module, a data fusion module, a time sequence data analysis module, an intelligent identification module and a monitoring result output module. The method comprises the following steps: synchronously acquiring a multispectral image and laser radar point cloud data of a citrus reiculata tree canopy, and generating a point cloud model through spatial registration fusion; continuously recording model data of a plurality of time points, and extracting a time sequence feature vector; identifying disease and pest types and severity by using a deep learning model; and outputting a result to the user terminal. According to the invention, the problem that large-range and high-precision early monitoring of diseases and insect pests of citrus reiculata canopies is difficult to realize in the prior art is solved, early discovery, precise positioning and trend early warning of the diseases and insect pests are realized through air-space-ground integrated data fusion and intelligent analysis, and the intelligent level of orchard management and the disease and insect pest control efficiency are effectively improved.

The invention discloses a traditional Chinese medicine detection device based on visual analysis, and relates to the technical field of traditional Chinese medicine visual detection, the traditional Chinese medicine detection device comprises a machine body, an operation table, a display screen, an indicator light assembly, a conveyor belt assembly, a positioning groove, a multispectral image acquisition assembly, a laser displacement sensor and a detection analysis calculation assembly, wherein the detection, analysis and calculation assembly comprises an acquisition module, a processing module, a calculation module and a judgment module, and the calculation module comprises a feature unit, a defect unit and a comprehensive analysis unit. According to the method, defect features are captured in multiple dimensions through the feature unit, accurate initial features are provided, the defect unit is coupled with physical and environmental parameters, similar defects are distinguished from a physical mechanism, the type judgment accuracy is improved, the comprehensive analysis unit is fused with feature strength, type confidence and quality influence parameters, full-dimension quality grade evaluation is achieved, and the method is suitable for large-scale popularization and application. The inherent quality is associated, and a scientific basis is provided for defect grading of the traditional Chinese medicine tablets.

The invention discloses an insulator detection method and system based on an intelligent robot, and relates to the field of image detection, and the method comprises the steps: collecting an image through an inspection robot carrying a visible light and infrared camera, processing a visible light image through employing a dynamic correction algorithm fusing Retinex-HSV, and achieving the registration fusion of a multispectral image through employing an SURF feature point and an RANSAC algorithm; based on an improved Mask R-CNN network, precise segmentation of the insulator is achieved, and then defect features such as surface dirt, structural cracks and core rod deterioration are extracted hierarchically; and finally, generating a defect comprehensive probability by fusing multiple features through a D-S evidence theory, and automatically alarming when the defect comprehensive probability exceeds a threshold value. The method has the advantages that the data quality is improved through multi-spectral image adaptive acquisition, Retinex-HSV dynamic correction and SURF-RANSAC fusion, and efficient and accurate full-automatic defect detection of the power transmission line insulator is realized in combination with lightweight Mask R-CNN segmentation and D-S evidence theory multi-feature decision.

The invention discloses a cross-platform soybean seed protein content estimation method based on a spectral time sequence. The method comprises the following steps: step 1, acquiring high spectral data of a soybean canopy; 2, collecting a soybean multispectral image; step 3, acquiring soybean physiological parameters and grain protein content data; step 4, obtaining soybean photosynthetic parameter data; step 5, calculating a vegetation index for estimation; step 6, constructing a vegetation index time sequence; 7, screening an optimal time window; 8, extracting dynamic characteristics; step 9, establishing a model, estimating the soybean protein content, and performing precision verification; and step 10, carrying out mobility analysis on the model from a near-ground canopy to a satellite platform. According to the method, a theoretical basis and a practical path are provided for realizing remote sensing-based soybean GPC accurate monitoring, wide application of the method on a satellite remote sensing platform can be promoted in the future, and high-precision, high-time-efficiency and large-scale soybean GPC estimation is realized.

The invention discloses a transformer substation insulator pollution positioning method, device and system fusing a multispectral image and a laser radar point cloud, and belongs to the technical field of power equipment multispectral detection and intelligent operation and maintenance. The method comprises the steps that a multispectral imaging device and a laser radar device are arranged in a measurement area facing the outdoor insulator of the extra-high voltage transformer substation, and the laser radar device is controlled to collect three-dimensional point clouds of the insulator and a surrounding structure of the insulator, controlling the multispectral imaging device to acquire multispectral images of the outer insulation surface of the insulator under a plurality of working wavebands; performing spatial registration on the three-dimensional point cloud and the multispectral image based on a calibration plate and geometric constraints, and mapping multispectral spectral information to point cloud sampling points; by introducing a multispectral three-dimensional point cloud and a virtual cleaning operator and combining multi-view joint optimization under an umbrella skirt structure parameter model, the problem that pollution assessment is unstable and not fine due to a single-view image or a simple intensity threshold value in the prior art is solved; and a reliable quantitative basis is provided for substation insulator electrical risk assessment and intelligent operation and maintenance decision.

The invention discloses an orchard unmanned aerial vehicle multispectral image noise restoration method based on a generative adversarial network, and relates to the technical field of multispectral image noise restoration, and the method comprises the steps: synthesizing a noisy sample containing stripes, photon particles, thermal noise, Rayleigh, shielding, compression artifacts, overexposure and waveband crosstalk on a noise-free reference image through a conditional generative adversarial network; a crosstalk matrix T obtained through optical calibration is introduced; based on the canopy / soil / sky semantic partition, main-cooperation-subdivision level superposition is implemented; generating hierarchical labels by taking energy conservation consistency and thermal noise correlation as thresholds; a branch and region self-adaptive repair module corresponding to classification is arranged in the image-to-image generative adversarial network, and joint loss training containing T regularization is adopted; and outputting the repaired image subjected to physical consistency checking. The method gives consideration to visual quality and spectral fidelity, inhibits cross-band pollution, improves the stability of indexes such as NDVI and NDRE, and is suitable for precision agricultural monitoring.

The invention discloses a PCB soldering paste printing three-dimensional defect detection system based on multispectral imaging, and relates to the field of machine vision detection, and the system comprises a multispectral image acquisition module, a four-channel industrial camera based on RGB and near infrared, and a tunable LED light source array; acquiring reflection characteristic data under different penetration depths through a wavelength switching mechanism; the motion control trigger module is used for realizing positioning based on an XYZ three-axis objective table driven by a servo motor in cooperation with feedback of an encoder; a pulse width modulation signal is adopted to coordinate the moving speed of a camera shutter and a platform; according to the method, RGB and NIR four-channel imaging is combined with Beer-Lambert law modeling, so that double verification of material component quantitative analysis and three-dimensional shape reconstruction is realized, and limitation of monocular vision is avoided; structured light projection and binocular stereo matching technologies are adopted, cross-frame data alignment is realized in cooperation with an ICP algorithm, and detail defects can be better detected.

The invention discloses a tire surface microcrack detection method and system based on multispectral imaging, and the method comprises the steps: receiving a multispectral polarization image sequence of a tire surface, and generating an initial stress feature tensor based on Mueller matrix decomposition; performing topological data analysis on the initial stress characteristic tensor, constructing a crack characteristic bar code, and decomposing the bar code structure into a plurality of persistent coherence groups; performing feature enhancement on the persistent coherent group by using a generative adversarial network, and generating a labeled training sample set; performing model training on the labeled training sample set on the basis of a visual Transform architecture to generate a micro-crack detection model; deploying the microcrack detection model, performing online reasoning on a multispectral image acquired in real time, performing crack shape parameterization in combination with a Hough forest algorithm, and generating a microcrack detection report including position, length and width. According to the embodiment of the invention, the accuracy and adaptability of microcrack detection can be improved.

The invention relates to the technical field of computer vision, in particular to a fabric color fastness analysis method and system based on image processing. Comprising the following steps: acquiring a multispectral image of a to-be-detected fabric, and synchronously acquiring multi-factor data; processing the multispectral image to generate an enhanced image; performing image recognition on the gray sample card in the enhanced image to generate a calibration image; performing spectral data projection on the calibration image through a dimension reduction algorithm, and performing color analysis and quantification on color change to generate color difference features; extracting texture features by using a conditional generative adversarial network, carrying out image recognition and calculation on the calibration image through a decoupling algorithm, and generating a region credibility graph; and inputting the chromatic aberration features, the texture features, the multi-factor data and the regional credibility map into an image feature fusion model for mapping, performing analysis through gradient visualization, and outputting an abnormal feature saliency map. According to the method, a color fastness objective analysis closed loop is created, so that the accuracy and the universality of an analysis result are improved.

The invention relates to an intelligent soil regulation and control method based on soil data and image processing. The method comprises the following steps: acquiring and standardizing soil data; orchard global multispectral image acquisition: acquiring and preprocessing an orchard image, and acquiring an orchard global multispectral image; vegetation region identification and parameter calculation: calculating NDVI based on red light and near infrared bands, distinguishing green manure and fruit tree regions through a threshold segmentation method, and calculating green manure coverage, a green manure normalized vegetation index and a fruit tree canopy area; carrying out soil-vegetation data fusion and parameter extraction; distributing weights based on the three types of core parameters, calculating the application amount and type ratio of nitrogen, phosphorus and potassium fertilizers, and generating an initial regulation and control scheme; and repeating the steps every specified time period to realize dynamic iterative optimization of the scheme. Through deep fusion of soil data and an image processing technology, intelligent soil regulation and control are realized, regulation and control accuracy and timeliness are remarkably improved, and fertilizer waste and environmental risks are reduced.

The invention discloses a product image recognition method, device and equipment, a medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a first multispectral image of a first product object, and carrying out the dynamic noise suppression and feature enhancement processing of the first multispectral image, and obtaining a second multispectral image; inputting the second multispectral image into a pre-trained image recognition model to obtain a defect probability thermodynamic diagram corresponding to pixel points in the first multispectral image and uncertainty confidence of defect positioning; determining a defect type and defect position information corresponding to the first multispectral image according to the defect probability thermodynamic diagram and the uncertainty confidence; wherein the image recognition model comprises a global feature extraction module based on a Vision Transform architecture and a local feature extraction module based on a dilated convolutional neural network. According to the technical scheme provided by the embodiment of the invention, the identification capability of sub-pixel-level microdefects can be improved, and the accuracy of a product image identification result is improved.

The invention discloses a living blood vessel and lymph multi-parameter quantitative photoacoustic microscopic imaging system which comprises a high repetition frequency laser source, a photoacoustic scanning probe and a molecular imaging device. Through the cooperation of the multi-wavelength fast switching laser and the high-speed scanning probe, synchronous, dynamic and quantitative imaging of vascular function parameters and lymph concentration in a living tissue microenvironment is realized. The system has a video-level imaging rate and can capture a rapid biological process; through coaxial design of dispersion correction and a photoacoustic focus, high spatial resolution and high signal-to-noise ratio of a multispectral image are guaranteed; and the compact probe design realizes large-range scanning. The technology provides a powerful in-vivo visualization tool for research on life science problems such as tumor evolution and drug metabolism.

The invention relates to a multi-source remote sensing cooperative system based on an unmanned aerial vehicle formation and a control method, and the system can synchronously collect laser radar point cloud data, multispectral images, thermal infrared images and hyperspectral images, and breaks through the limitation of single data dimension caused by the fact that a traditional single unmanned aerial vehicle platform can only carry a single sensor. Meanwhile, the formation control module controls the heterogeneous unmanned aerial vehicle cluster to form a preset formation and perform cooperative flight to ensure spatial position cooperation of the unmanned aerial vehicles in the operation process, and the task allocation module dynamically allocates route tasks and sensor working parameters to the unmanned aerial vehicles based on target area environment information. And the data fusion module performs geometric registration, spectrum fusion and three-dimensional reconstruction processing on the multi-source remote sensing data acquired by each unmanned aerial vehicle to generate a three-dimensional spectrogram of the target area, so that accurate integration of the multi-source data in space and spectrum dimensions is realized, and the multi-source remote sensing data is acquired. Therefore, the system gives consideration to large-range area coverage capability and high monitoring precision.

The invention relates to a fig health status assessment method and system based on multi-modal data fusion, and the method comprises the steps: constructing a multi-modal feature vector through fusing the features of a multi-spectral image, a hyperspectral cube and a visible light image; then proposing a multi-modal fusion suitability evaluation model, pre-processing the multi-modal feature vector, and then carrying out deep fusion and feature association to obtain a fusion feature; and finally, according to the fusion features, outputting an adaptation score and disease and pest classification. Therefore, the characteristic representation integrity is obviously enhanced, the evaluation accuracy and the pest and disease classification precision are improved, and the robustness and the anti-interference capability are enhanced.