175 results about "Disease area" patented technology

The invention discloses a plant disease and insect pest early warning method and system based on plant monitoring, and belongs to the field of plant disease and insect pest early warning, and the method comprises the steps: extracting the contour of a lesion region through an edge detection algorithm, carrying out the smoothing of the contour through the combination of morphological operation, and obtaining a more precise lesion region boundary; according to an image segmentation result and a disease type identification result, a plant health condition evaluation model is established, and the plant damage degree is quantified; environmental data and image data are acquired from a plurality of sensor nodes distributed in a field, and the data are gathered to a regional gateway through wireless transmission; carrying out preprocessing and feature extraction on the converged heterogeneous data in a regional gateway, removing noise data and redundant information, and extracting key features; and carrying out pest detection and counting on the preprocessed image by using a deep learning model, carrying out modeling on a pest number change trend, predicting population density change in a period of time in the future in combination with environmental factors, and generating a detection result.

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 embodiment of the invention provides a rice blast disease grade intelligent evaluation and disease resistance preliminary screening method, system and equipment, and the method comprises the steps: S1, collecting image data of wild rice leaves infected with rice blast, carrying out the instance segmentation of the image data through an improved YOLOv8-Seg segmentation model, obtaining a segmentation result, and carrying out the segmentation of the image data through an improved YOLOv8-Seg segmentation model; the improvement measures comprise a GhostHierachicalNet backbone network, a CAHSFPN neck network and an LSDES Head segmentation head, and the LSDES Head segmentation head is connected with the CAHSFPN neck network and the LSDES Head segmentation head; s2, according to the segmentation result, counting the number and proportion of pixels of diseased leaves and disease areas, and obtaining a disease grade evaluation result in combination with disease resistance identification technical regulations; and S3, screening wild rice materials with strong disease resistance based on the disease grade evaluation results which are repeated for multiple times. The method can effectively improve the accuracy and efficiency of crop disease evaluation, and provides support for large-scale screening of disease resistance of wild rice, thereby promoting the development of rice blast-resistant breeding work.

The invention discloses a BEV-based accurate calculation method for a road disease area ratio, and the method comprises the steps: configuring a vehicle-mounted data collection system, and carrying out the marking and preprocessing of data; constructing a BEV-based disease detection network and training the BEV-based disease detection network; a BEV-based disease detection network is used for generating disease detection results, the results are spliced in a fixed calculation unit, and the disease area ratio is calculated, the top view of the whole road is reconstructed, so that seamless fusion of multi-view information is realized, the problem of dislocation possibly caused by a traditional splicing technology is effectively solved, and the method is suitable for large-scale popularization and application. According to the method, the comprehensiveness and the accuracy of area measurement are ensured, measurement errors caused by view angle differences and repeated calculation of overlapped area disease areas are avoided, the accuracy and the integrity of disease area statistics are ensured, high geometric consistency and accurate representation of disease areas can be kept even under the conditions of complex road conditions and changeable environments, and the method is suitable for large-scale popularization and application. And the efficiency and the credibility of road condition evaluation are greatly improved.

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 discloses a forest tree disease monitoring method and monitoring system, and relates to the technical field of forest tree monitoring, and the method comprises the steps: obtaining the regional topographic features of a forest tree monitoring region; local data calling is performed on the unmanned aerial vehicle, and a monitoring flight path is generated; in the flight process of the forest monitoring area, forest data acquisition is carried out, and multi-modal monitoring data is obtained; performing spatial alignment on the multi-modal monitoring data to obtain a plurality of area monitoring information graphs; carrying out disease area identification frame selection to obtain disease risk area distribution; carrying out disease diffusion prediction according to the disease type, and carrying out frame selection to obtain a hierarchical disease diffusion region. According to the method, the technical problems of single monitoring means, inaccurate disease area identification, difficulty in accurately predicting the disease diffusion trend and low monitoring efficiency in the existing forest disease monitoring technology are solved, and the technical effects of accurately monitoring the forest diseases, efficiently identifying the disease areas and accurately predicting the disease types and the diffusion trend are achieved.

The invention relates to the technical field of road pavement disease recognition, in particular to an intelligent road pavement disease recognition method and system based on a multispectral image, and the method comprises the steps: obtaining road pavement image data collected by a multispectral camera carried by an unmanned aerial vehicle; generating a registered multispectral fusion image; performing enhancement processing on the multispectral fusion image based on a dark channel algorithm to generate an enhanced image; performing shallow feature extraction and deep feature extraction on the enhanced image according to a convolutional neural network to generate an initial disease extraction image; performing segmentation processing on the initial disease extraction image through adaptive threshold segmentation to generate a binary image; performing morphological processing on the binary image to generate a candidate disease image; extracting edge information of a disease area from the candidate disease image based on an instance segmentation technology, and generating a segmented image; the segmented image is subjected to crack refining processing, a crack refined image is generated, and more comprehensive disease information support is provided for collection and fusion of multispectral data.

The invention relates to the field of image cultural relic disease recognition and evaluation, in particular to a cultural relic disease recognition and evaluation system and method based on image analysis, and the system comprises a multispectral image collection module, an image processing module, a disease recognition module, a repair evaluation module, a decision optimization module and a disease database. The multispectral image acquisition module optimizes a light source through a reflective area detection algorithm to obtain a multispectral image; the image processing module uses a multi-band collaborative filtering algorithm to eliminate noise and extract fusion features, and divides disease areas; the disease identification module determines disease positions through a connected region marking algorithm, and performs disease classification by means of a neural symbol model; the repair evaluation module outputs a quantitative score; the decision optimization module weighs the repair effect, the cost and the time by introducing a multi-objective optimization model of a genetic algorithm, and outputs an optimal scheme; according to the invention, the disease identification precision and the automation and scientificity of repair evaluation are improved.

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 present application provides a double-branch network-based pavement disease identification method and apparatus, a medium. The method comprises: carrying out iterative optimization on a preprocessed hyper-parameter of an image by means of a Bayesian optimizer to obtain an optimal preprocessed hyper-parameter of the image; applying the optimal preprocessed hyper-parameter of the image to the image to obtain an optimized image corresponding to the image, and the image and the optimized image corresponding to the image constituting a training image pair; on the basis of the training image pair, training an initial double-branch multi-task deep network model to obtain a double-branch multi-task deep network model meeting standards, wherein the initial double-branch multi-task deep network model comprises an image adaptive preprocessing network model and a freezing disease identification model; and inputting an image to be identified into the double-branch multi-task deep network model meeting the standards, so as to carry out disease identification to obtain a target disease prediction result. The present application aims at improving the accuracy and precision of identifying a disease area by an existing model.

The invention discloses a pavement disease inspection method based on a vehicle-mounted edge device, and relates to the technical field of pavement disease inspection, and the method comprises the steps: 1, obtaining image data, thermal imaging data and road geometric data of a pavement through an information obtaining module; 2, the information processing module processes the image data to obtain a disease area I; matching the image data with the thermal imaging data to obtain a disease area II; performing height change analysis on the geometric data of the road to obtain a disease area III; 3, the information processing module matches the disease area I, the disease area II and the disease area III with the position of the road surface, and when any position of the road surface is successfully matched with more than two disease areas I, disease areas II or disease areas III, the position of the road is marked as a disease part. According to the method, the multi-modal road surface data are adopted, and various light-weight means are adopted, so that the remarkable optimization effect is achieved in the aspects of performance, efficiency, resource utilization and the like.

The invention discloses a patient medical satisfaction evaluation system based on big data, and relates to the technical field of big data analysis. The medical satisfaction evaluation system comprises a data acquisition and processing module, an evaluation index management module, a satisfaction questionnaire survey module, a multi-dimensional evaluation module, a satisfaction grading module and a service quality optimization module. According to the big data-based patient doctor-seeing satisfaction degree evaluation system, through real-time collection and analysis of patient satisfaction degree data including a medical quality evaluation index and a doctor-seeing experience evaluation index, registration waiting time, diagnosis and treatment waiting time and an endemic area environment score are monitored, service bottlenecks and patient dissatisfaction fields are rapidly identified, and the patient satisfaction degree evaluation efficiency is improved. The hospital can adjust resource distribution in time, the overall quality of medical services is evaluated through the patient complaint score and the doctor-patient communication transparency score, effective communication between doctors and patients is promoted, transparency is improved, and therefore the satisfaction degree and credibility of the patients for the medical services are improved.

The invention discloses a pavement disease size accurate quantification method based on binocular vision, and the method specifically comprises the steps: collecting pavement image data through a vehicle-mounted binocular camera, achieving the real-time detection of a disease target through an improved RT-DETR model, and outputting the disease type and detection frame information; after a pavement disease target is detected, pixel-level segmentation is carried out on a disease area in the detection frame based on a semantic segmentation model, and a disease contour mask is extracted; an improved IGEV-Stereo stereo matching algorithm is used for calculating a disparity map, depth information is output based on camera calibration parameters and a disparity estimation result, and conversion from pixel coordinates to three-dimensional coordinates is achieved; and the binocular depth information and a disease detection segmentation result are combined to realize accurate size quantification of typical road surface diseases with different characters. Through the binocular stereoscopic vision technology, automatic detection and precise quantification of pavement diseases can be realized, the cost can be effectively reduced while the disease quantitative evaluation precision is improved, and decision support is provided for road management and maintenance.

The invention discloses a bridge support disease detection system and method based on a rail-mounted inspection robot, and belongs to the technical field of bridge detection. The system comprises a robot walking guide mechanism, the rail-mounted inspection robot and a self-adaptive following system; the rail type inspection robot comprises a chassis, and a mechanical arm is arranged on the chassis; the self-adaptive following system comprises a robot control system, an image processing system and a network communication system; the robot control system comprises a main control unit which is mainly used for controlling movement of a chassis and self-adaptive adjustment of a mechanical arm; the image processing system comprises an image acquisition device, an image processing and storage device and an edge computing device; and the image acquisition equipment is arranged on the mechanical arm and is mainly used for acquiring image data. According to the invention, intelligent identification and adaptive following can be carried out on the bridge support disease image, so that the image acquisition equipment can be ensured to lock and track the disease area from the front side, and high-quality and omission-free photographing detection can be realized.

The invention discloses an unmanned aerial vehicle self-adaptive inspection method based on multi-layer characterization learning, which comprises two stages of automatic coarse inspection and self-adaptive fine inspection, and learns the space structure, component characteristics and disease characteristics of a bridge layer by layer. In the automatic rough detection stage, in combination with a spatial clustering post-processing method and a point cloud semantic segmentation network, instance attributes of bridge components are quickly learned. Then, the point cloud space is compressed and converted by using a simulation view field model and a dimension reduction technology, and the unmanned aerial vehicle is guided to carry out efficient and comprehensive space patrol in plane geometry. In the adaptive fine detection stage, a space-channel two-dimensional optimized hybrid convolution structure Light-PVIT is utilized to learn and extract disease features in the automatic coarse detection stage as prior information. The information guides the unmanned aerial vehicle to carry out fine detection on a disease area in a small field of view. According to the unmanned aerial vehicle self-adaptive inspection method, the bridge inspection efficiency and precision are remarkably improved, and reliable technical support is provided for bridge maintenance work.

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 discloses a turbid water area bridge pile pier disease intelligent detection method based on fluorescence radiography, and the method comprises the steps: obtaining a sound wave reflection signal, and judging whether a bridge pile pier has a disease or not; constructing an injection parameter prediction model of the fluorescence injection device, and determining injection parameters of the fluorescence injection device; obtaining a fluorescence distribution image of the surface of the bridge pile pier, and extracting a region contour of a disease region in the fluorescence distribution image; constructing a disease diagnosis model of the disease area, and judging the disease type and the disease severity of the disease area of the bridge pile pier; constructing a structural risk assessment model of the disease aggregation region, and assessing the structural risk degree of the disease aggregation region; calculating a disease area development rate, and generating a preventive maintenance strategy; and according to the bridge pile pier maintenance result, the early warning threshold value and the monitoring strategy are continuously adjusted. By combining a nondestructive testing technology, a fluorescent spraying technology and intelligent analysis, efficient detection and maintenance optimization of bridge pile pier diseases are realized.

The invention discloses a typical bridge disease characterization method, and relates to the technical field of bridge detection, and the method comprises the steps: firstly constructing a degraded disease sample library, and building a surrounding environment model and a bridge BIM model; secondly, constructing a virtual bridge scene by using a UE5 engine, and obtaining a virtual disease sample; performing data enhancement by adopting an improved double-branch generative adversarial network in combination with real and virtual samples, generating a similar real degradation sample, and expanding real sample data; carrying out disease area identification and extraction on the detection image by using an improved Unet network trained by using real sample data; and finally, mapping an identification result to a BIM model to realize three-dimensional visual representation. According to the method, the problem of insufficient disease samples is solved through the generative adversarial network, the fine-grained disease recognition precision is improved by utilizing the improved Unet, full-process digital representation of bridge diseases from data acquisition, intelligent recognition to three-dimensional dynamic display is realized, and the accuracy and visualization effect of disease detection are remarkably improved.

The invention discloses a leaf vegetable disease detection method based on few sample learning and prototype attention. The method comprises the following steps: S1, preprocessing and enhancing data; s2, performing multi-scale feature extraction on the image; s3, global feature modeling is carried out; s4, target detection and semantic segmentation; and S5, carrying out few-sample optimization and fine tuning. According to the leaf vegetable disease detection method based on few-sample learning and prototype attention, a few-sample learning network architecture is introduced for solving the problem of agricultural disease data sample scarcity, rapid and accurate disease area identification and segmentation can be realized under a limited data condition, target detection and semantic segmentation tasks are combined, and the detection efficiency is improved. Disease positioning and fine-grained segmentation are carried out, the detection precision and segmentation performance are improved, prototype extraction and an attention mechanism are fused, the learning ability of the model under the low-sample condition is effectively improved, the method can be applied to disease detection and control of the vegetable planting area in real time, and efficient disease management of farmers is supported.

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 cable apparent disease detection method and device based on computer vision, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a plurality of initial images of a to-be-detected object at different angles; cutting and splicing the initial images according to the displacement distance and the shooting distance corresponding to the initial images of the adjacent shooting positions to obtain a to-be-detected image; determining disease regions in the to-be-detected image based on the disease position detection model, and for each disease region, performing disease evaluation on each disease region based on a disease degree evaluation index to obtain a disease degree corresponding to each disease region; and determining a to-be-repaired area according to the disease degree corresponding to each disease area, and determining a target repair position of the to-be-repaired object based on the position of the to-be-repaired area in the to-be-detected image. By adopting the method, the accuracy of determining the repair position of the sling can be 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 intelligent maintenance, in particular to an intelligent pavement disease sorting platform based on AI recognition. The platform takes a multispectral acquisition module and an edge calculation module as front acquisition and identification modules, and further comprises a disease sorting decision module and a disease interactive display module, and can acquire a pavement disease image frame in real time to perform frame extraction preprocessing and single-frame delay control disease area identification so as to obtain a disease position area in a pavement image single frame; performing crack disease 12-dimensional feature analysis and maintenance emergency evaluation on the disease position area in the single frame of the pavement image to obtain a maintenance emergency degree index corresponding to each single frame of the pavement image; and carrying out maintenance emergency sorting planning based on the maintenance emergency degree index corresponding to each pavement image single frame, and issuing a maintenance disposal instruction on the man-machine interaction terminal to execute corresponding pavement disease maintenance motorcade scheduling planning work. According to the method, the detection efficiency and the sorting accuracy can be improved, resources are optimized, and the investment of unnecessary maintenance is reduced.

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 provides a rice disease identification method and system, and the method comprises the following steps: S100, carrying out the labeling of a disease region of a rice leaf image, and obtaining a segmented label image; s101, performing feature extraction on the rice leaf image; s102, enhancing partial feature boundary information to obtain a first enhanced feature; s103, searching partial feature targets to obtain second enhanced features; s104, receiving a part of the second enhancement feature and the first enhancement feature, and outputting a decoding feature; s105, receiving a part of decoding features, and outputting a semantic prediction result; s106, extracting the boundary of the segmented label image to obtain a boundary label image; s107, receiving the first enhanced feature and a part of the second enhanced feature, and constructing boundary supervision loss; s108, receiving a boundary prediction result and a semantic prediction result, and constructing regular term loss; and S109, training the constructed loss by using a rice disease identification model, realizing boundary information of a concerned disease area, obtaining an accurate boundary segmentation effect, and improving the rice disease detection precision.

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 provides a leaf vegetable disease detection, regulation and control method and system based on deep learning. And collecting a growth image and environmental parameter data of the leafy vegetables, performing disease detection by using the improved DETR model, and outputting a bounding box and a disease category of a disease area. Segmenting by adopting a weak supervision segmentation method based on a bounding box to obtain a pixel-level segmentation map of each disease region, extracting color, shape and area characteristics of the disease regions, and carrying out grading quantification on the diseases to generate a disease grade score. The optimal planting strategy is obtained through the deep Q network model, the environment parameters such as illumination, temperature and humidity are adjusted through the execution mechanism, the leaf vegetable growth environment is optimized, and the production efficiency is improved. According to the method, deep learning and intelligent regulation and control technologies are combined, efficient disease recognition and fine environment regulation and control can be achieved, loss caused by diseases is reduced, the yield and quality of leafy vegetables are improved, manual intervention is reduced, and the method has high practicability and popularization value.

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.