108 results about "Disease area" patented technology

The invention discloses a grape disease recognition and early warning method based on the Internet of Things, and relates to the technical field of plant disease recognition, image acquisition equipment and environment sensing nodes are arranged in a vineyard, and leaf images and corresponding temperature and humidity, illumination and soil moisture parameters are obtained; inputting the image into a neural network fusing dilated convolution and a residual attention mechanism, realizing extraction of a disease spot region and a disease spot variation feature, and generating a preliminary recognition result; constructing a multi-factor evolution sample set in combination with the recognition result and the environment state of the time node; constructing a space-time correlation graph model based on a graph neural network, estimating a disease propagation risk path and a diffusion probability, and performing early warning judgment at a gateway end through a multi-factor gating discrimination algorithm; the method disclosed by the invention is high in recognition precision and strong in response timeliness, has adaptive prediction and targeted treatment capabilities, and remarkably improves the intelligence and precision level of grape disease management.

The invention discloses a tunnel lining disease automatic identification method and system based on multi-source data fusion, and relates to the technical field of facility detection, and the method comprises the steps: collecting multi-modal time sequence data, carrying out the time-space alignment, and obtaining a time sequence multi-source data set; reconstructing a tunnel center line based on a vehicle pose and constructing a lining structure consistency coordinate framework, and performing structured projection and distortion correction on alignment data to obtain a multi-modal fusion data set; dividing a two-dimensional structure grid under the coordinate framework, extracting and fusing geometric, texture, depth and energy features, calculating a structure consistency damage index, and extracting a suspected disease area; and calculating a disease credibility index and judging a disease type in combination with multi-modal physical evidence, mapping a suspected disease region back to a three-dimensional space, completing disease boundary extraction and geometric quantization, and outputting structured disease information. According to the method, the structure expression and the structured alignment of the cross-modal data under the unified geometric reference are realized by constructing the consistent coordinate framework of the lining structure.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.

The invention relates to the technical field of space-time prediction, in particular to a brucellosis space-time prediction method and system based on artificial intelligence, and the method comprises the following steps: obtaining case information, calculating an incidence relation, extracting a path sequence, screening a trend direction, and matching a new case to generate prediction data. According to the method, continuous identification of a propagation path is realized by constructing a propagation incidence relation between cases and introducing a spatial directivity index, a non-trend propagation process is screened out through an angle average value and a variance, spatial consistency of path screening is enhanced, and through joint matching of spatio-temporal characteristics of newly-added cases and an existing propagation trend, the propagation path screening efficiency is improved. The sensitivity of prediction to the trend attribution of a new case is improved, the spatial directivity of a potential disease area is enhanced through reverse projection of a trend path and area positioning drop point frequency analysis, and through linkage processing of multi-level path extraction, trend judgment and area coding, the probability of occurrence of the new case is lowered. And the capturing capability of the prediction data on the propagation and evolution characteristics of the brucellosis is improved.

The invention provides an interactive pavement disease segmentation method based on a large model, and the method comprises the steps: constructing pavement disease segmentation models which comprise a disease detection network model, a disease initial segmentation network model and a disease segmentation large model, carrying out the offline training of the disease detection network model and the disease initial segmentation network model, and obtaining a disease segmentation model; performing fine adjustment on the disease segmentation large model; inputting the disease image into a pavement disease segmentation model, and detecting a disease area by using a disease detection network model based on a deep learning algorithm; based on a deep learning algorithm, performing disease initial segmentation on the disease areas by using the disease initial segmentation network model to obtain a disease initial segmentation result corresponding to each disease area, and automatically obtaining a disease positive prompt point and a disease negative prompt point; performing interactive correction on the automatically obtained disease positive prompt point and negative prompt point, and updating the positive prompt point, the negative prompt point and the frame; and based on the updated positive and negative prompt points and the frame, automatically segmenting the disease by using a disease segmentation large model to obtain a final segmentation mask.

The embodiment of the invention provides a tunnel disease detection method and system based on an unmanned aerial vehicle, and belongs to the technical field of defect optical detection. The method comprises the steps that an unmanned aerial vehicle is controlled to fly along a tunnel to collect multichannel image data of the surface of a structure, and the flight attitude is adjusted based on environment illumination information to execute image illumination compensation; performing image alignment of each target anchor point based on a structure anchor point atlas constructed based on historical acquisition images in combination with the flight pose information of the unmanned aerial vehicle and the multi-channel image data after illumination compensation; identifying a disease area in the aligned image, extracting disease features of each target anchor point at this time, and updating the disease of each target anchor point at this time into a time sequence feature data sequence corresponding to each target anchor point; and based on the updated time sequence characteristic data sequence of each target anchor point, Bayesian point change detection is adopted to analyze the disease evolution trend of the tunnel. According to the scheme, the alignment precision, the time sequence comparability and the risk judgment capability of tunnel disease detection are integrally improved.

The invention discloses a bridge disease detection method based on a diffusion model and a bitter fish optimization algorithm, and relates to the technical field of bridge detection. The method comprises the following steps: fixing a visual angle and a distance at an easy-to-peel or crack position of a bridge, and collecting and aligning visible light and near-infrared images; carrying out multi-scale downsampling on the image, and carrying out wavelet denoising, brightness correction and texture smoothing; inputting the preprocessing result into an improved diffusion model, weighting the edge during forward diffusion, and reversely generating and applying texture and contour smoothing; the multi-source feature channel and the reconstructed image are combined and input into a deep segmentation network, shadow and stain are eliminated by using a local difference function, and global search is performed on a segmentation threshold, a noise coefficient and the like based on a disease detection rate, a false detection rate and the like by using a bitter fish algorithm; training and correcting the high-noise area again according to the optimal parameters; and uniformly marking disease areas. According to the method, the recognition recall rate of tiny spalling and irregular cracks in an extreme environment can be greatly improved, and the intelligent level and the practical effect of bridge disease detection are improved.

The invention relates to a potato leaf disease area positioning method, system and device.The method is applied to the potato leaf disease area positioning device.The method comprises the steps that a target infrared image of a potato leaf is segmented into a normal area and an abnormal area according to the environment temperature through a disease segmentation model obtained in advance; determining the water content of the target infrared image according to the leaf spectrum information through a pre-obtained water content prediction model; the leaf emissivity characteristic is calculated according to the water content through a pre-obtained temperature correction model, the temperature of the abnormal area is corrected based on the emissivity characteristic and the water content characteristic, and the temperature correction model is obtained based on potato leaf characteristic training; and through a pre-trained disease identification model, identifying a disease area from the abnormal area after temperature correction.

The invention relates to the technical field of road detection, in particular to a multi-dimensional road disease detection method and system based on a ground penetrating radar. The method comprises the following steps: acquiring original ground penetrating radar data, performing DC component removal, gain adjustment and background denoising on the original ground penetrating radar data, positioning a suspected disease area and defining the suspected disease area as a disease entity; extracting a time domain feature, a frequency domain feature and a spatial context feature of each disease entity in parallel to form a multi-dimensional feature vector; constructing a road disease knowledge graph according to the multi-dimensional feature vectors, and distributing an initial weight for each graph relation; and performing node matching operation according to the multi-dimensional feature vector and a knowledge graph, performing logical reasoning according to a graph relation path, fusing similarity and reasoning confidence, and outputting a diagnosis tag signal and a confidence signal. According to the method, the positioning accuracy of the suspected disease area is improved, comprehensive utilization of multi-dimensional information is realized, and a visual causal link is provided for diagnosis.

The invention relates to the technical field of material analysis, in particular to an intelligent inspection device and method for bridge structure diseases, and the method comprises the steps: determining a plurality of disease suspected regions through obtaining a plurality of frames of unmanned aerial vehicle images of a bridge structure, carrying out the feature analysis of each disease suspected region, and determining the feature value of each disease suspected region; according to different candidate time interval lengths and characteristic values, determining interval change fluctuation degrees representing the possibility of rain and sewage flow marks corresponding to different candidate time intervals in each disease suspected area, and carrying out feature distinguishing on the rain and sewage flow marks and crack diseases; according to the interval change fluctuation degree, determining interval division fitness, comprehensive identification capability and timeliness of different candidate time intervals; according to the interval division fitness, the target interval change fluctuation degree corresponding to each disease suspected area is determined, so that the crack disease area is determined for intelligent inspection, the recognition accuracy is improved, and the intelligent inspection effect is ensured.

The invention relates to the technical field of tea production, in particular to a method for distinguishing tea leaves with diseases and insect pests based on an imaging hyperspectral technology, which comprises the following steps of: obtaining disease sensitive wavebands according to reflectivity distribution conditions of different spectral wavebands in a leaf spectral image; calculating a disease spectrum ratio index of the leaf spectrum image based on the disease sensitive wave band, obtaining a disease spectrum ratio index image, further obtaining a disease area mask, finally separating a disease area based on the disease area mask, and obtaining a classification result through a preset disease and pest classification model. According to the method, efficient and accurate distinguishing of tea leaves with diseases and insect pests is achieved through the imaging hyperspectral technology, the visual expression of early weak lesions is remarkably enhanced based on disease sensitive band extraction and spectral ratio index calculation, the problem that a traditional method is insufficient in early disease recognition sensitivity is solved, and the method is suitable for large-scale popularization and application. A multi-category disease diagnosis result can be quickly output in combination with the preset disease and pest classification model, and the problem of low tea disease detection efficiency in the prior art is solved.

The invention discloses a tunnel disease detection method based on illumination compensation and dynamic feature fusion. The tunnel disease detection method is specifically implemented according to the following steps: step 1, data preprocessing; step 2, constructing an illumination adaptive compensation module; step 3, constructing a hierarchical attention fusion module; and step 4, constructing an adaptive spatial pyramid rapid pooling module. According to the method, the problem of feature aliasing caused by uneven illumination, background interference and scale mismatching in tunnel disease detection can be effectively relieved, accurate recognition and positioning of multi-type and multi-size disease areas are achieved, and the robustness and universality of a detection system in an actual engineering scene are improved.

The invention relates to the technical field of image processing, in particular to a pavement disease identification method and system based on multiple deep learning models. According to the pavement disease recognition method based on the multiple deep learning models, a unified disease recognition input standard is established through image normalization preprocessing; carrying out disease image classification by adopting an improved ResNet50 model; a trained first-stage target detection model is adopted to preliminarily mark a disease area in a road image, a second-stage target detection model is adopted, dense small frame marking training is combined with intersection operation, refined disease recognition is performed on a disease image, and the accuracy is improved. According to the pavement disease recognition method and system based on the multiple deep learning models, the robustness of the system to illumination, noise and complex disease forms is enhanced, the efficiency of the detection process is optimized, the accuracy and reliability of pavement disease detection are remarkably improved, and efficient and accurate technical support is provided for road maintenance and management.

The invention discloses a crop disease intelligent identification method and system based on an unmanned aerial vehicle image, and belongs to the technical field of intelligent agriculture, and the method comprises the steps of data multi-dimensional collection, early disease identification, disease space-time deduction and crop management suggestion. According to the method, early disease recognition based on the spectrum sensitive vegetation index and the attention guidance double-branch network is adopted, the spectrum sensitive vegetation index capable of amplifying weak disease spot features is constructed, the weak disease features are highlighted through multispectral information enhancement and time sequence feature inhibition, meanwhile, the attention mechanism is utilized to focus on potential disease areas, and the disease recognition accuracy is improved. Therefore, the accuracy and stability of early disease recognition are remarkably improved. Disease space-time deduction combining propagation potential energy and a space-time diagram convolutional network is adopted, on the basis of considering factors such as environmental conditions, disease time sequence evolution and spatial neighborhood influence, a disease propagation path is dynamically simulated, future risks are quantified, and a visual risk prediction map and a propagation vector diagram are generated, so that agricultural management is assisted to be optimized.

The invention discloses a bridge intelligent inspection unmanned aerial vehicle integrated measurement and control system, and relates to the technical field of unmanned aerial vehicle measurement and control, and the system comprises a reference construction module, a pose acquisition module, a pose matching module, a coordinate generation module, and a disease locking module. According to the method, the reference three-dimensional model and the space reference control point are constructed, so that accurate registration of the real-time video stream and the historical model is realized; driving the unmanned aerial vehicle to actively adjust the pose and lock the disease area through visual servo control; the absolute three-dimensional space coordinates of the diseases are calculated through reverse ray tracing, integration of disease recognition, active observation and accurate positioning is achieved, the automation degree of bridge inspection is improved, and the problems that in the prior art, bridge disease recognition and space positioning links are disjointed, cross-cycle data cannot be aligned, and an active observation mechanism is lacked are solved.

The invention relates to the technical field of image analysis, in particular to a pinus massoniana disease model training method based on image analysis, and the method comprises the following steps: collecting a multi-time sequence image, constructing a boundary sample through gray scale and Sobel, obtaining a gradient deviation through CNN through outer normal gradient increment, and combining contour comparison, differential sequence and clustering recognition mutation points and marking to form a marking set; according to the marking and transition cluster, convolution kernel weight is adjusted to optimize boundary representation, the model is iteratively updated to improve the pinus massoniana disease identification precision, the time sequence stability is enhanced, the robustness in a complex scene is improved, the edge detail recovery capability is improved, and the detection consistency is enhanced. According to the method, contour gradient operation is carried out by extracting image gray and combining Sobel, subtle change is identified and time sequence information is fused to capture disease development, gradient response and error comparison are carried out to improve detection precision, mutation points are identified through differential clustering analysis and disease areas are marked, and the model is optimized to reduce boundary deviation.

The invention discloses a tomato leaf disease detection method, which is based on an improved lightweight target detection model MWD-YOLO11n to improve the accuracy and efficiency of disease identification. According to the method, three key improvements are carried out on the basis of an original YOLO11 model: firstly, a multi-scale dynamic attention mechanism (MSDA) is introduced, and the perception ability of the model to different-scale disease features is enhanced; secondly, wavelet convolution (WTConv) is adopted to replace part of a traditional convolution structure, and the capturing capacity of the model for disease edges and texture details is effectively improved; and finally, integrating a dynamic up-sampling operator (DySample), optimizing the resolution of the feature map, and further enhancing the characterization capability of the disease area. The method has high precision and low calculation amount, is suitable for deployment of edge equipment, shows excellent performance in tomato leaf disease detection tasks, and has good application prospects and popularization value.

The invention discloses an outer wall disease detection and health assessment method based on a fusion-segmentation joint network, and the method comprises the steps: collecting an infrared thermal image and a visible light image of an outer wall disease, and constructing an infrared image fusion data set after preprocessing; constructing a fusion-detection joint network comprising an image fusion network module and a segmentation detection network module, and realizing collaborative optimization through a joint loss function; pre-training is carried out by using an MSRS data set so as to obtain the best parameters of the dynamic pixel fusion weight and the loss function weight item; freezing parameters of the image fusion network module, and optimizing the segmentation detection network module in combination with a self-established fusion data set; using the trained fusion-detection joint network to carry out disease segmentation detection on the building exterior wall image; counting the disease area and distribution condition according to the segmentation detection result, and comprehensively evaluating the health degree of the outer wall. According to the invention, automatic disease segmentation detection and external facade health assessment are realized, and the disease detection accuracy is improved.

The invention relates to a crop disease identification method based on wavelet transform and residual network fusion. According to the method, a CBAM attention mechanism, wavelet transform and a residual network are fused, and a disease identification model (CropNet) for non-specified crop types is provided. According to the CropNet, firstly, Haar wavelets are utilized to perform four-stage decomposition on disease images, frequency domain features are deeply extracted, and the frequency domain features and spatial features extracted by a residual network are continuously fused; then different weights are given to the fused feature layer by using CBAM, and the attention of the model to a disease area is increased; and finally, a dual transfer learning training model is utilized to improve the accuracy and generalization of the model for identifying diseases of non-specified crop types. The identification accuracy of the CropNetA is 99.76%, the identification accuracy of the CropNetA is 99.85%, and the identification accuracy of the CropNetA is 99.86% on the PlantVillage data set, the identification accuracy of the CropNetA is 99.85% on the AI Challenger 2018 data set and the identification accuracy of the CropNetA on the self-built data set. The result shows that the method can obtain clearer and more sufficient disease characteristics while reducing the noise, improves the disease recognition precision, and provides reference for intelligent agriculture and precise recognition, prevention and control of crop diseases.

The application discloses a three-dimensional detection and diagnosis simulation verification method and platform for tunnel apparent diseases and a storage medium. The method comprises the following steps: collecting point cloud data and image data of a tunnel model, performing difference analysis based on the two types of data to obtain an apparent detection result, and positioning a suspicious disease area of the tunnel model according to the apparent detection result. A machine learning model is used to analyze a disease diagnosis result corresponding to the suspicious disease area, and the disease diagnosis result is compared with preset real disease information of the tunnel model, and a comparison result obtained can be used to optimize the machine learning model. The scheme avoids the limitations of real vehicle verification and non-entity verification, and effectively improves the verification effect and verification efficiency of the machine learning model.

The invention provides an expressway foreground image disease detection method and system, and relates to the technical field of expressway disease detection, and the method comprises the steps: obtaining an expressway foreground image, carrying out the recognition according to a preset neural network model to obtain a disease region and a disease type, and generating a Grad-CAM thermodynamic diagram for the disease region through a Grad-CAM technology; performing grid division on the Grad-CAM thermodynamic diagram containing the disease area to obtain standard detection frames, and counting the proportion of abnormal pixels in each standard detection frame; judging whether the abnormal pixel proportion exceeds an abnormal threshold value or not; if yes, the disease area in the standard detection frame is an effective disease area, the accurate boundary of the effective disease area is drawn to quantify the disease size, the disease position and the disease number of the expressway foreground image are obtained according to the accurate boundary, and disease detection is achieved in combination with the disease category. According to the invention, the detection convenience, the identification precision and the size standardization are improved.

The present invention discloses a leafy vegetable disease detection method based on few-shot learning and prototype attention, comprising the following steps: S1, preprocessing and enhancing data; S2, performing multi-scale feature extraction on images; S3, global feature modeling; S4, target detection and semantic segmentation; S5, few-shot optimization and fine-tuning. The present invention adopts the above-mentioned leafy vegetable disease detection method based on few-shot learning and prototype attention. Aiming at the problem of scarcity of agricultural disease data samples, the few-shot learning network architecture is introduced. It can realize fast and accurate disease area identification and segmentation under limited data conditions, combines target detection and semantic segmentation tasks, performs disease location and fine-grained segmentation, improves detection accuracy and segmentation performance, integrates prototype extraction with attention mechanism, effectively improves the learning ability of the model under low-sample conditions, can be applied in real time to disease detection and control in vegetable planting areas, and supports farmers in efficient disease management.

The present invention discloses a high-frequency electrotherapy protection device and method. This device can directly sample data from the treatment circuit, providing direct data support for treatment efficacy. This allows for differentiated treatment of different treatment sites, enabling precise, quantified treatment of diseased areas, ensuring both treatment effectiveness and accuracy. It also enables early prediction of treatment mishaps, providing dual protection against potential safety hazards. The device comprises a high-frequency electric field therapy unit, a treatment energy monitoring unit, and a control unit.

The invention provides a concrete apparent disease dynamic detection method, system and device based on a YOLOv8 improved model and a medium, and belongs to the technical field of computer vision detection.The method comprises the steps that concrete apparent disease images are collected, data enhancement is conducted on the disease images, the position and category of the disease are marked through a marking tool, and the position and category of the disease are marked; obtaining a disease image data set; a YOLOv8 model is improved, the disease image data set is input into the YOLOv8 improved model, network parameters are initialized through transfer learning, and an SGD optimizer is adopted to train a training set part in the concrete disease data set in stages; and inputting the concrete apparent disease image into the YOLOv8 improved model so as to monitor, position and classify the concrete apparent disease area. The method effectively improves the detection precision and detection efficiency of concrete apparent diseases, and especially has significant advantages in a dense disease scene.

The invention belongs to the technical field of road surface detection, and relates to a multi-dimensional radar map construction method based on electromagnetic signals, which comprises the following steps: S1, scanning a target area through high-frequency three-dimensional ground penetrating radar equipment, acquiring electromagnetic reflection signals, and preprocessing the acquired signals; s2, converting the time domain data of the preprocessed electromagnetic reflection signal into spatial domain data, and generating a three-dimensional data model; s3, blank data in the three-dimensional data model are complemented through an interpolation method, and high-resolution three-dimensional grid data are generated; s4, extracting a disease area based on a reflection intensity threshold value and performing type identification; and S5, constructing a three-dimensional disease visualization model, and mapping disease characteristic parameters by adopting color coding. According to the method provided by the invention, the depth resolution and the spatial positioning precision of disease detection are improved, and the real distribution and form of the diseases in the pavement can be reflected more comprehensively.

The application discloses a defect detection method for rail fasteners based on multi-scale feature enhancement, and is implemented according to the following steps: step 1, selection and preprocessing of an existing rail fastener defect dataset; step 2, construction of a feature extraction module of a rail fastener defect detection network; step 3, construction of a multi-scale feature enhancement and fusion module for rail fastener defect detection; and step 4, construction of a detection prediction and result output module. Through the function processing procedures of layered feature response highlighting, local texture sensitive enhancement, global context semantic aggregation, inter-layer heterogeneous feature collaborative fusion and dual-domain saliency modulation, the application differentiates and collaboratively enhances the shallow texture information, the middle structure information and the high-level semantic information, thereby effectively improving the representation ability, the category discrimination ability and the background suppression ability of the disease area, and improving the stability and reliability of the detection result.

The invention discloses a road disease area estimation method based on image transformation, and relates to the technical field of road disease detection, and the method comprises the following steps: S1, obtaining an original road image; s2, judging whether the original image reaches the standard or not; s3, calculating the actual area of the effective area and cutting the effective area; s4, performing perspective transformation on the effective area image; s5, carrying out disease detection on the orthographic image, and carrying out anchor frame positioning on the disease; s6, an optimal equal division point is calibrated in the anchor frame, and a disease in the image is segmented by using an SAM model; and S7, estimating the area value of the disease according to the area relation between the effective region area and the disease pixel points, and storing the area value in a database. According to the method, 27 equal division points generated by the diagonal lines, the quartered lines, the octant lines and the like in the anchor frame systematically cover the geometric center and the edge key transition area of the disease anchor frame, so that the prompt points of the SAM can point to the core morphological characteristics of the disease, and wrong segmentation of the SAM caused by background interference is reduced.

The present invention provides a rare disease image classification method, device and medium based on small sample learning. By cleverly constructing a feature backtracking fusion encoder, the next layer of features is used to generate an attention mask for the current layer to reduce useless noise information in the low-level features, thereby better integrating the spatial detail information in the low-level features into the high-level features, effectively improving the model's classification performance for gastrointestinal disease areas; secondly, the multi-level prototype reconstruction network further captures the semantic relevance between the support set and query set samples to enhance the distinguishing areas on the support image representation, and generates a calibration class center suitable for the current query sample for each query sample. The classifier based on Euclidean distance outputs the classification result of the query sample, and the cross-entropy function is used to guide model optimization to ensure the accuracy of the classification result. Ultimately, the rare disease image classification model based on small sample learning is able to output the classification result of each image after rigorous training and testing.

The grape leaf disease area image segmentation method provided by the application cooperates the frequency domain and the spatial domain, adopts a global processing module in the image segmentation model, the global processing module performs frequency domain transformation and spatial selective aggregation in the window synchronously through the frequency domain and spatial domain cooperative attention mechanism, realizes the deep cooperation of the global perception in the frequency domain and the accurate positioning in the space, effectively suppresses the interference of the complex field background on the disease feature extraction while enhancing the feature discrimination ability, adopts a hierarchical feature constructor during feature extraction, captures the fast scale context information through hierarchical capture, effectively processes the significant scale change of the disease area, and the adaptive feature fusion module can dynamically integrate the multi-scale heterogeneous features adaptively, enhances the feature fusion capability of the network, and thus can effectively ensure the segmentation accuracy of the final grape leaf disease area.

The invention belongs to the technical field of disease monitoring, and discloses a remote-sensing-based urban edge road disease monitoring and evaluation method and a remote-sensing-based urban edge road disease monitoring and evaluation system. Comprising the steps of collecting remote sensing image data and geographic information in real time, and executing data cleaning to obtain an original road assessment data set; performing deformation evaluation, performing semantic analysis on a deformation evaluation result, and outputting a disease tendency label; performing weight adjustment in combination with a disease tendency label, and outputting a score fusion proportion; extracting disease features based on the original road assessment data set, and calculating a disease score in combination with a score fusion proportion; performing trend identification based on the disease score, and outputting a trend field; combining the trend field and the disease score to carry out space aggregation on the corresponding road section to obtain an effective disease area; performing visualization processing on the effective disease area to obtain disease assessment visual data, and sending the disease assessment visual data to a preset monitoring terminal; the semantic intelligibility and the score adaptability of urban edge road disease monitoring evaluation are improved.