499 results about "Tree species" patented technology

The invention relates to the technical field of remote sensing space information, in particular to a large-scale forest green carbon inversion method and system based on multi-source remote sensing data, and the method comprises the steps: resolving remote sensing parameters, and obtaining the crown breadth and height of a single-plant crown of a sample plot; taking the crown breadth and the height of the single tree crown of the sample plot as independent variables, and establishing different-speed growth models of different tree species; performing inversion based on original echo signal waveform data of the satellite-borne laser radar to obtain forest structure parameters; according to the forest structure parameters, calculating laser spot scale biomass by using a different-speed growth model; and based on the laser spot scale biomass and the multi-source remote sensing image features, using a U-Net deep learning model to obtain large-scale biomass data with continuous space, and obtaining a large-scale forest above-ground biomass distribution map according to the large-scale biomass data. According to the method, high-precision inversion from single-plant crown parameters to large-scale forest above-ground biomass is realized, and the efficiency of forest biomass estimation is improved.

The invention discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology. The method comprises the following steps: multi-source remote sensing data acquisition: respectively acquiring optical remote sensing data and radar remote sensing data; tree species classification and identification: carrying out tree species classification on the forest in the research area to realize tree species scale space distribution information extraction; for different tree species, biomass model parameters obtained through field investigation are combined, a biomass estimation model based on the tree species is established, model input parameters are biomass model parameters, and vegetation biomass is output; calculating the carbon reserve: calculating the vegetation carbon reserve by utilizing the inverted vegetation biomass and combining the carbon content coefficients of different tree species; estimating the soil carbon reserve, and finally obtaining the total carbon reserve of the forest ecosystem: calculating the carbon reserve variation of the forest ecosystem at different time points based on the multi-source remote sensing data of the long-term sequence. According to the invention, large-range, high-precision and continuous forest carbon reserve and carbon sink monitoring and evaluation considering tree species difference is realized.

The invention relates to the technical field of ecological soil remediation, and discloses a moso bamboo ecological soil remediation directional regulation and control method and equipment which are used for solving the problems of insufficient light transmittance, low acidified soil remediation efficiency and the like caused by a fixed proportion of a traditional bamboo and broad-leaved mixed forest. A random forest algorithm is combined to dynamically optimize a mixing proportion and associated tree species configuration, an unmanned aerial vehicle is used for scanning to generate a three-dimensional crown model, intelligent equipment is guided to accurately intermediate cutting and complementary planting, and in cooperation with a pH response type shading film and an acid-resistant phosphate solubilizing bacterium agent, crown light transmittance improvement and acidified soil improvement are achieved. The equipment comprises an unmanned aerial vehicle carrying a laser radar, an intelligent transplanting robot and a solar drip irrigation system, and the concentration of a fungicide and an irrigation strategy can be dynamically regulated and controlled. The porous bamboo charcoal carrier is prepared by innovatively utilizing the moso bamboo waste, and the functional flora is loaded to replace the traditional peat raw material; according to the scheme, the problems of static configuration, manual dependence and resource waste are solved.

The invention discloses a power transmission channel vegetation carbon sink recovery potential assessment method and a system with the power transmission channel vegetation carbon sink recovery potential assessment method, and the power transmission channel vegetation carbon sink recovery potential assessment method comprises the following steps: collecting and fusing multi-aspect data of a power transmission channel planning target area; and establishing a detailed and highly-targeted base database for carbon sink recovery of the to-be-constructed target area of the power transmission channel. The method comprises the following steps: comprehensively considering the change of land use before and after power transmission channel construction, and formulating a typical tree species distribution pattern scheme according with a specific scene and corresponding accounting factors by combining land use types of areas to which land blocks belong; and a power grid safe operation constraint and a carbon sink metering method are combined. The comprehensive influence of power transmission channel construction on vegetation carbon sink can be accurately mastered, reasonable measures are taken to realize maximization of carbon sink benefits, and coordinated development of power transmission channel construction and ecological environment protection can be realized.

The invention discloses a method for generating an aviation lifelike tree sample and identifying a tree species by using an image diffusion model. The method comprises the following steps: constructing a multi-modal data set; training an image-text comparison model and a Unet denoising network model; outputting predicted noise in the trained Unet denoising network model, and finally generating final image reconstruction which is in semantic alignment with the input text description; the obtained single tree crown images are synthesized; and constructing a YOLOv11 detection model, and training the YOLOv11 detection model by using the synthesized forest image to realize tree species identification. According to the method, language semantics and a diffusion model are combined to generate a vivid tree sample for capturing specific features of species and seasonal phenological changes, and the vivid tree sample is synthesized into a high-fidelity forest image, so that tree species identification in an aerial image is enhanced, and effective crown detection and accurate tree species identification are realized.

The invention discloses a secondary forest development stage division method based on forest stand state characteristics, and belongs to the technical field of forest development stage division. Comprising the following steps: selecting an initial index set, performing index forward and standardization processing on sample plot survey data, and forming a data set; classifying the data by adopting a system clustering analysis method, checking and analyzing a state index influencing a classification result, and selecting plt; the index of 0.05 constitutes a secondary forest development stage division index system; carrying out weight assignment, then correcting the weight by adopting an entropy evaluation method, and determining the comprehensive weight of the index by utilizing a Lagrange multiplier method; the sum of the dimension-removed value of the division index and the comprehensive weight value product is a development stage value; according to an equidistant method, the development stage of the secondary forest is divided into a forest gap stage, a renewal stage, a differentiation stage, a built-up stage and a stable stage. Depending on forest age is avoided, and the method is suitable for multi-tree mixed secondary forests; the indexes are easy to obtain, and the division result can accurately guide forest management.

The invention discloses an urban landscaping tree species classification method based on a Pleiades Neo ultrahigh resolution multispectral satellite image. According to the invention, based on the Pleiades Neo image, an NDVI threshold method is adopted to rapidly identify the urban vegetation area; using a Unet < 3 + > model to separate vegetation in the shadow area and the illumination area; a shadow region tree species classification integration model is constructed by combining a superpixel segmentation algorithm and introducing a weighted dictionary and an attention mechanism, so that the classification precision is effectively improved; and carrying out tree species classification on the illumination area by using a deep learning algorithm and making an overall tree species distribution diagram. Aiming at the shadow interference problem in a high-resolution image, a set of tree species classification scheme comprehensively considering vegetation spectrum characteristics of an illumination region and a shadow region is developed by combining a deep learning algorithm to deeply mine potential information, a more accurate and feasible tree species distribution acquisition approach is provided for fine management of urban landscaping, and the urban landscaping quality is improved. And important data support is provided for health assessment and planning of the urban ecosystem.

The invention discloses a construction method, a construction system, a recognition method and a recognition system for a fine tree species recognition model, belongs to the field of forest management and ecological protection, and solves the problems that the overall contribution of the whole group of time sequence features to model performance cannot be comprehensively examined, and due to the resolution limitation of remote sensing images and the randomness of forest species distribution space and quantity, the model performance cannot be comprehensively examined in the prior art. And the performance of the remote sensing tree species classification model is obviously reduced. The method comprises the steps of 1, constructing a high-dimensional multi-source feature data set; 2, performing feature optimization based on the high-dimensional multi-source feature data set to obtain an optimal classification feature set; 3, collecting actual measurement sample data, and constructing a deep learning sample library in combination with the optimal classification feature set; and step 4, presetting a fine tree species identification model, and inputting the deep learning sample library to train the preset fine tree species identification model to obtain the fine tree species identification model. The method is used for obtaining the accurate and newest tree species distribution map.

The invention discloses a tree species identification method based on a bark image and a lightweight deep learning model, and belongs to the crossing field of computer vision and forestry information technology, and the method comprises the steps: S1, collecting an image of a tree bark area; s2, preprocessing the collected data, and positioning and cutting a bark area based on an image segmentation technology; s3, using an improved lightweight deep learning model of a ConvNeXtV2 framework to carry out feature extraction on the preprocessed bark image; s4, after feature extraction is completed, features are integrated by using an adaptive feature fusion strategy, then tree species are classified, and a tree species identification result is output; s5, performing multi-dimensional verification and visual analysis on the model performance; according to the method, the calculation complexity is remarkably reduced, the generalization ability and stability of the model are kept, and reliable technical support is provided for practical application such as ecological monitoring, forest resource management and intelligent forestry.

The invention provides a method for identifying forest tree species by using laser point cloud data, and the method comprises the following steps: collecting three-dimensional laser point cloud data of a forest region, setting an elevation threshold, filtering ground points, and extracting a point cloud sample object; respectively extracting VFH, CVFH and ESF feature descriptors from the sample point cloud, and constructing three types of geometric feature vectors; performing supervised classification on the features by adopting a random forest and a support vector machine learning classifier; the output of each classifier is fused through strategies such as weighted voting, an average method or a stacking method, and a final tree species identification result is obtained; according to the method, three types of global or semi-global feature descriptors of VFH, CVFH and ESF are extracted for a point cloud sample object of a single tree, feature modeling is carried out on tree species from three dimensions of spatial attitude, local scale structure and global shape distribution, the advantage of real restoration of a target structure by using point cloud data is utilized, and the feature modeling efficiency is improved. And the problem of projection distortion of image features under multiple view angles is avoided.

The invention discloses a forest degeneration degree, degeneration type and degeneration process identification method, and relates to the field of forest resource monitoring and ecological environment evaluation.The method comprises the steps that multi-source data obtained in a research area is preprocessed, and the preprocessed multi-source data is determined; selecting multi-dimensional indexes from three aspects of forest degradation structure, composition and function, and constructing a multi-dimensional index system according to the preprocessed multi-source data; the multi-dimensional indexes comprise forest coverage rate, crushing degree, tree variety diversity, aboveground biomass and net primary productivity; generating a forest degradation index according to each index in the multi-dimensional index system; determining a forest degeneration degree according to the forest degeneration index, generating a forest degeneration type graph in combination with land coverage data to reveal degeneration differences of different forest types, analyzing a dynamic change track of a forest degeneration region in combination with a normalized combustion index time sequence, and identifying a forest degeneration process; according to the method, the multi-dimensional characteristics of degradation can be comprehensively revealed.

The invention discloses an intelligent forestry surveying and mapping method and system based on regional feature feedback, relates to the technical field of forest surveying and mapping, and provides the following scheme: extracting spectrum and texture features of a target forest image through satellite remote sensing data, realizing recognition and grading division of feature regions by using an intelligent segmentation algorithm, and obtaining a target forest image. A priority monitoring grid of the content evaluation indexes is generated; and scheduling the unmanned aerial vehicle to scan the priority monitoring grid for the first time, and integrating image data of the unmanned aerial vehicle and the satellite. According to the method, spectrum and texture features of a target forest region are extracted through satellite remote sensing data, a non-uniform priority monitoring grid containing a feature density index is generated, an unmanned aerial vehicle is scheduled to scan a high-priority grid for the first time, satellite multi-spectrum data, an unmanned aerial vehicle high-resolution image and LiDAR point cloud data are fused, and the target forest region is obtained. The tree species are identified, the health index thermodynamic diagram is generated, high-danger areas are rapidly identified, the scanning range of the unmanned aerial vehicle is remarkably compressed, and the monitoring efficiency is improved.

The invention relates to the technical field of forestry management, in particular to a forest and grass resource dynamic monitoring system which comprises the steps that a graph attention network is adopted to conduct high-low weight recognition processing on the structure difference value between node pairs, feature vectors are established through the canopy height difference and canopy density difference between adjacent nodes, and the canopy height difference and the canopy density difference between adjacent nodes are obtained; an edge weight scoring system is constructed in the form of segmented statistics and proportion weighting, node edge pairs with high influence relation strength are dynamically screened, ordered aggregation of spatial communication strength is realized, a graph neural network is introduced in a parameter fusion stage to construct a node parameter representation mechanism, and the spatial communication strength is improved. The tree species proportion, the grade of diameter at breast height and the community vertical structure in a regional sample plot are used as input features, unified mapping of node features of each region is completed in multiple rounds of iteration, cross-regional difference analysis is executed on model output in combination with biomass change frequency, parameter items with the difference proportion lower than a set threshold value are replaced with unified expression, and the model output is obtained. And parameter synchronization is realized to realize space nesting and feature integration.

The invention provides a fast-growing high-yield forest tree species afforestation area establishment method and system, and relates to the field of afforestation. According to the method, ecological environment and social economic two-dimensional information are fused, an improved maximum entropy reinforcement learning model is adopted to construct an ecological environment suitable area prediction model, social economic suitability is judged based on rule modeling, and a final suitable afforestation area is obtained through space superposition. According to the method, intellectualization and precision of afforestation site selection are achieved, scientificity and actual operability of suitability judgment are improved, and the method is suitable for afforestation planning and optimization under large-scale and multi-constraint conditions.

The invention provides an intelligent and rapid pre-examination and diagnosis method and system for tree health, and relates to the technical field of tree management. The method comprises the following steps: constructing a liquid flow database of a standard tree of a target tree species, carrying out model training on an established liquid flow calculation model by utilizing the liquid flow database, then collecting liquid flow data of a to-be-detected target tree in a preset detection time and meteorological and soil information of a growth environment, inputting an optimal liquid flow calculation model, and calculating a theoretical liquid flow value of the target tree, comparing with an actually monitored liquid flow value, calculating a liquid flow deviation degree, grading according to a daily maximum liquid flow deviation degree, and comprehensively evaluating a tree health grade in combination with phenotype information to obtain a diagnosis result; the rapid and intelligent pre-examination diagnosis of the tree health is realized, and the intervention timeliness is improved; and multi-dimensional data fusion is adopted, so that the diagnosis accuracy is improved, and automatic monitoring and real-time early warning are realized.

The invention discloses a power transmission corridor tree species growth prediction method, system, device and medium, and belongs to the technical field of tree growth prediction.The method comprises the steps that tree species data and environment data in a power transmission corridor area are obtained, the tree species data and the environment data are preprocessed, and a training data set is obtained; training a cascade recurrent neural network model based on the training data set to obtain a trained cascade recurrent neural network model; predicting the growth trend of the tree species according to the trained cascade recurrent neural network model to obtain a prediction result, performing error analysis on the prediction result, and optimizing the trained cascade recurrent neural network model according to an error analysis result; performing growth trend analysis according to the prediction result, and determining the growth trend of the tree species; and carrying out early warning on the growth trend of the tree species through the prediction result in combination with a preset safety threshold. According to the method, the tree growth prediction precision is improved through a cascade structure.

The embodiment of the invention provides a tree species identification method, device and equipment based on a dense time sequence image, and the method comprises the steps: obtaining a dense long time sequence multispectral remote sensing image and topographic data of a research area; carrying out image wave band fusion according to the dense long-time-sequence multispectral remote sensing image to construct 12 vegetation spectral indexes; performing land utilization classification on ground features in the dense long-time-sequence multispectral remote sensing image of the research area by utilizing a linear spectrum hybrid model in combination with topographic data, dividing the ground features into buildings, water bodies, other ground features and a forest land data set, and performing pre-classification based on the extracted forest land data set; dividing the forest land data set into an evergreen forest and a deciduous forest by combining an RVI threshold method with a linear spectrum hybrid model; according to the forest land data set, Savitzaky-Golay filtering transformation and spectral differential transformation are carried out, and an NDVI transformation image set is obtained; and performing combination according to the vegetation spectral index NDVI transformation image set as classification features, and discussing the classification effect of the tree species in the research area through a random forest model.

The invention provides a tree species screening method for high-altitude drought afforestation, an afforestation method and application, and belongs to the technical field of forest tree breeding and cultivation. According to the screening method, five physiological and biochemical indexes of soluble protein, malondialdehyde, proline, peroxidase and soluble sugar in the tree species are measured through a gradient drought stress experiment, and comprehensive scoring is performed by adopting a membership function, so that the drought-resistant tree species are scientifically screened. The tree species such as sophora moorcroftiana, rosa serrulata and amorpha fruticosa screened by the method are applied to afforestation in the high-altitude arid region, and the afforestation survival rate and preservation rate can be remarkably increased. The invention provides a reliable technical scheme for solving the long-term problem that the selection of tree species in the difficult site field lacks scientific basis.

The invention provides a multi-tree forest aboveground biomass remote sensing estimation method and system and a storage medium, and the method comprises the steps: obtaining multi-source remote sensing data based on a target region, including airborne LiDAR point cloud, ground survey data and DEM data; constructing a terrain heterogeneity index DGTHI in combination with DEM data, and quantifying terrain complexity through weight fusion of standardized terrain factors; extracting forest characteristic parameters and calculating the overground biomass of the single tree; performing classification modeling on the sampling region based on DGTHI, respectively establishing multiple linear regression models for different tree species, and optimizing feature parameter selection; and evaluating the precision of the model through cross validation, and popularizing the model to a county scale to perform overground biomass space inversion and mapping. According to the method, terrain heterogeneity classification and tree species specificity modeling are fused, so that the estimation precision of the forest biomass in the complex terrain region is remarkably improved, and a technical support is provided for regional carbon sink monitoring and sustainable forestry management.

The invention discloses a forest stand tree species identification method and system based on multi-source remote sensing data fusion, and the method comprises the following steps: obtaining data which comprises forest land remote sensing image data and ground data; carrying out preprocessing on the obtained data; a dual-path CBAM-UNet network model is constructed; inputting the preprocessed data into the double-path CBAM-UNet network model to carry out tree species classification prediction and obtain a tree species classification chart; performing image splicing on the tree species classification map to obtain a tree species classification grid map; the system comprises a data acquisition module, a data preprocessing module, a prediction module and an image processing module. According to the forest stand tree species identification method and system based on multi-source remote sensing data fusion provided by the invention, multi-scale feature capture and feature fusion of multi-source heterogeneous data can be realized through the constructed dual-path CBAM-UNet network model, high-precision identification can be performed on forest stand tree species under complex links, and high-quality data can be provided for forest management.

The invention discloses a power transmission corridor tree classification method, system and device and a medium. The method comprises the following steps: acquiring a hyperspectral image of a power transmission corridor area; performing data preprocessing and data enhancement on the hyperspectral image; creating a deep learning model based on the preprocessed and enhanced hyperspectral image, and extracting multi-level spectral and spatial features; training the deep learning model by adopting an optimization loop technology to obtain an optimal model parameter; and inputting the new hyperspectral image data into the trained deep learning model to obtain a tree category result of the power transmission corridor area. According to the method, the defects of a simple model in hyperspectral data are overcome, and the classification accuracy is greatly improved especially in the environment that tree species are complex or uncertain factors such as illumination variation and interference exist.

The invention relates to the technical field of remote sensing image analysis and processing, in particular to a complex natural scene-oriented land cover remote sensing information acquisition method. The method comprises the following steps: firstly, acquiring all tree clusters, further acquiring a difference separation weight of each environment index in each tree cluster, and adjusting the distance between different tree areas by combining the difference between corresponding environment information of different tree areas under the same environment index to acquire all sub-tree clusters; and finally, carrying out image enhancement, labeling and classification on the tree areas in the sub-tree clusters. The method comprises the following steps: firstly, carrying out preliminary clustering on tree areas based on morphological characteristics of trees, further analyzing differentiation performance of environments where different trees are located in the same cluster, determining the distinguishing capability of each environment index on tree species, then, carrying out reclassification on the trees in the cluster, and carrying out enhancement on the tree areas to different degrees; and the acquisition effect of the remote sensing information of the land cover is improved.

The utility model discloses a tree supporting device, which belongs to the technical field of tree supporting and comprises a tree main body and a supporting device main body, the tree supporting device is novel in design and ingenious, compared with the prior art, the tree supporting device in the technical scheme has higher stability and adjustment flexibility, rapid adjustment can be conducted according to trees of different sizes, continuous and stable supporting force is provided, friction force between the tree supporting device and barks is increased through a buffer pad, the contact area is increased through a double-iron-wire connection mode, and the tree supporting effect is improved. The long-term stability of the supporting device is enhanced, the service life of the device is remarkably prolonged, the device is made of high-strength materials so as to ensure the supporting effect of the device, the wind resistance of trees is enhanced by optimizing the fixing mode, the influence of the external environment on the trees is reduced, and the survival rate of the trees is increased. The device is convenient to store, carry and reuse, meets the requirements of tree species of different sizes, and greatly improves the practicability.

The invention discloses a forest ecosystem carbon reserve determination method based on multi-source remote sensing data, and the method comprises the steps: constructing and analyzing a dominant tree species multi-temporal multi-feature classification data set according to Sentinel-1 and Sentinel-2 multi-source remote sensing data and research area forest resource checking vector data extraction; classifying the dominant tree species according to the multi-feature multi-temporal remote sensing data; and estimating the forest carbon reserves of different dominant tree species according to the optimal classification data set and the forest stock. According to the method, forest multi-feature and multi-temporal feature data are obtained by means of multi-source remote sensing data, and combined dimension reduction and separability analysis are performed, so that advantageous tree species spatial distribution is extracted, a forest stock and biomass linear model is fitted, and carbon reserve spatial distribution and features of a research area are estimated. And basic data and a practical basis are provided for further strengthening forest ecological system protection and implementing forest carbon reserve accurate improvement measures.

The invention relates to the technical field of power line monitoring, and discloses a tree growth prediction method, system and device based on a multi-temporal point cloud and a growth model, and a medium, and the method comprises the steps: collecting the multi-temporal data of a target region, carrying out the vegetation region recognition and single tree segmentation of a vegetation point cloud, extracting the structural features of each tree, and carrying out the prediction of the growth of each tree; generating a time sequence characteristic data set; constructing a tree growth model, taking the time sequence characteristic data set as model input, introducing a point cloud structure parameter as a dynamic constraint item, and performing parameter optimization on the model to obtain a prediction curve of a multi-dimensional growth index of the tree; and based on the prediction curve of the multi-dimensional growth indexes, performing dynamic geometric reconstruction through original point cloud data, and generating an interactive visual three-dimensional model. According to the method, on the basis of multi-source heterogeneous point cloud data, the single-tree-level growth model can be constructed, the time sequence structure change trend of the single-tree-level growth model is analyzed, and the space error and the time stability of a prediction result are quantified.

The invention relates to the technical field of tree species identification, in particular to a tree species identification method and system based on multi-source remote sensing data fusion, and the method comprises the steps: obtaining multi-source remote sensing data in a target identification region, carrying out the cross-domain feature decoupling processing of the multi-source remote sensing data, and obtaining the attribute features of tree species and the environmental interference features; inputting the tree species attribute features into a preset adaptive spatial-temporal feature library to obtain correction features; collecting current real-time environment parameters, and fusing the tree species attribute features, the environment interference features, the correction features and the real-time environment parameters to obtain multi-source coordination features; a tree species identification result is generated according to the multi-source coordination characteristics, tree species identification requirements in different geographical environments can be adaptively matched, the problem of precision attenuation caused by data distribution offset during cross-regional migration of a model system is effectively solved, and the tree species identification robustness in different ecological environments is remarkably improved.

The invention discloses an afforestation device and an afforestation method for precious tree species in a stony desertification area, and relates to the technical field of afforestation devices. Comprising a vehicle body, the vehicle body is provided with a marking mechanism, the marking mechanism comprises a marking material storage box, and the marking material storage box is installed on the vehicle body through a supporting frame; one end of the connecting pipe is connected to the marking material storage box, and the other end of the connecting pipe is provided with the connecting cavity; and the lifting sliding frame is arranged on one side of the vehicle body. By arranging the marking mechanism, marking powder can be supplied into an output head through a marking material storage box, the output head is driven to move downwards based on a lifting control assembly, a second spring gradually returns from a compressed state, when the output head moves downwards by a certain distance, a ball head rod is separated from the bottom of the output head, and the blocking effect is relieved; the marking powder can be output from the output head, the marking purpose is achieved, and the planting position can be judged conveniently.

The invention relates to the technical field of ecological restoration, in particular to a method for restoring a tropical degraded forest based on thinning and complementary planting of nitrogen fixation precious tree species. The method comprises the following steps: dividing a degenerated forest into three types, namely a mild type, a moderate type and a severe type according to canopy density and forest stand composition; for the light tropical degraded forest, a stand structure is built through target tree marking and interference tree felling, stand renewal is driven in cooperation with pruning work and selective felling, and a different-age double-layer forest is cultivated; for the moderate tropical degraded forest, a forest window updating unit is constructed, and survival rate and preservation rate dual monitoring is executed to maintain the forest window updating unit; for the severe tropical degraded forest, native vegetation is reserved through strip cleaning, seedlings are planted in a planting strip to construct a strip recovery substrate, synchronous complementary planting is carried out, and finally the strip succession process is stabilized in combination with continuous tending. According to the method, the problem of repairing the fragmented area is effectively solved, synergistic interaction of soil improvement and economic value improvement is achieved, and the technology generalizability is remarkably enhanced through a standardized operation system.

The invention discloses a tree growth prediction method and system based on a TCN time sequence convolution model, and the method comprises the steps: collecting the historical growth data of trees in a power transmission line corridor, and carrying out the data preprocessing; constructing a neural network model based on the TCN; dividing the preprocessed data into a training set and a test set, and training the TCN model by using the data of the training set; performing performance evaluation on the trained TCN model by using the test set; and deploying the trained TCN model in an actual application scene, and performing real-time growth prediction on the target tree species in the power transmission line corridor area. According to the method, through the model based on the time convolutional network (TCN), the growth condition of the tree species in the power transmission line corridor is accurately predicted. The TCN model captures the sequential relationship in the time sequence by using the convolution operation, thereby avoiding the problem of gradient disappearance or explosion easily occurring when the traditional recurrent neural network processes the long sequence, and improving the prediction accuracy.

The invention discloses a tree species identification method based on a visual converter and ensemble learning, and belongs to the technical field of computer vision and forestry information, and the method comprises the steps: S1, image collection and analysis: collecting bark texture images of different tree species through a mobile device, and carrying out the deep analysis of an input image through a model; s2, model framework improvement: optimizing local and global feature extraction, introducing an integrated learning strategy, and fusing prediction results of different models; s3, model training and optimization: adopting ViT-B-16 as a basic network, and initializing model parameters through a pre-training weight; s4, outputting a result: outputting a tree species identification result, and pointing out a tree species classification corresponding to the input image; according to the method, the stability and uniqueness of the bark texture are utilized, and the advantages of the bark texture in tree species identification are fully exerted; through algorithm improvement, efficient fusion of local and global features is realized, the calculation complexity is reduced, and the generalization ability of the model is enhanced.