3540 results about "Forest industry" patented technology

The invention provides a forestry pest and disease prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a dynamic monitoring data set of a target forest region, covering an environment and vegetation physiological parameter sequence collected by a plurality of monitoring nodes, and a space coordinate region corresponding to each node; extracting features of the dynamic monitoring data set to obtain a forestry environment feature set containing vegetation growth, environment fluctuation and potential identification features of plant diseases and insect pests, and analyzing the forestry environment feature set by using a pre-trained plant disease and insect pest prediction model to generate a prediction parameter set representing the occurrence probability and influence range grade of the plant diseases and insect pests; executing dynamic prediction matching, determining a disease and insect pest distribution thermodynamic map and generating a control priority strategy, and finally fusing the disease and insect pest distribution thermodynamic map and the disease and insect pest control priority strategy to generate a forest region control optimization instruction set, and feeding back the instruction set to a forestry monitoring platform to trigger resource scheduling so as to realize efficient disease and insect pest control.

The invention discloses a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, and relates to the technical field of carbon sink monitoring and optimization. According to the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, space-time estimation is carried out on CCER forestry carbon sink project data through the deep learning model, and the dynamic change of the carbon sink amount is accurately predicted. Spatial features are extracted through a convolutional neural network, time sequence data are analyzed in combination with a long-short-term memory network, and a carbon sink prediction model is constructed. And through statistical analysis and a spatial interpolation algorithm, carbon sink quantity change is monitored in real time, an abnormal trend is identified, and early warning is provided. Based on a mixed integer linear programming algorithm, forest land resource allocation and vegetation types are optimized, a carbon sink optimization scheme is generated, and carbon sink benefit maximization is promoted. And through an intelligent carbon sink management scheme, sustainable ecological development and green and economic popularization are assisted.

The invention relates to the technical field of data security management, and discloses a blockchain-based forestry data security management system and method, and the system comprises a data collection unit which is used for comprehensively obtaining a multi-dimensional information flow of forest ecology and resource states; the data processing unit is used for carrying out deep cleaning, standardized regulation and feature value extraction on the originally collected mass heterogeneous data; the block chain storage unit is used for constructing a permanent, tamper-proof and distributed secure storage infrastructure platform of the forestry core data; a verification unit; the safety control unit is used for constructing a covering data transmission, storage and access full-chain depth defense system structure model; a user interface unit; an auditing unit; and a network communication unit. The method is reasonable in design, and the intelligent analysis module is used for processing to form a forest interannual growth trend prediction function distribution map state visual output result set.

The present invention relates to a method and system for estimating forest carbon storage that combines artificial intelligence algorithms and multimodal remote sensing data. This approach comprehensively utilizes laser radar satellites, multi- / hyperspectral satellites, radar satellites, high-resolution optical imagery, etc. A hybrid technical system is employed for different forest coverage areas, resulting in high-precision forest carbon storage mapping with a resolution of 10 meters and area coverage. This provides technical support and assurance for assessing global forest carbon storage and supporting forestry carbon sequestration transactions.

The invention discloses a forest prevention and control method based on disease and pest monitoring, and belongs to the technical field of forestry prevention and control, and the method comprises the steps: dividing a target forest into regions according to vegetation types, collecting historical multi-source data, filling the missing data, analyzing a time sequence and causal relationship to construct a time sequence causal diagram, and simulating a disease and pest spatio-temporal dynamic state in combination with current data. And determining a prevention and control risk level and a key induction factor of each region, executing a prevention and control strategy according to the level, and updating a causal diagram through reinforcement learning according to forest health response data. According to the method, a closed loop is formed from data processing to model construction, accurate prevention and control are achieved, efficiency and scientificity are improved, forest ecological changes can be dynamically optimized and adapted, and the prevention and control effect is guaranteed.

The invention relates to the technical field of forestry informatization, in particular to an ecological public welfare forest monitoring and intelligent management system and method. The method comprises the steps of collecting multi-dimensional ecological data in real time and performing data fusion; based on a data fusion result, establishing an ecological behavior evolution graph and a multi-layer state inversion network by adopting a graph attention mechanism, forming a dynamic ecological twinborn body, and realizing ecological system state prediction, historical disturbance inversion and future behavior simulation; a risk tensor map is constructed in combination with state abnormality, trend instability and intervention ineffectiveness in a three-dimensional mode, a dynamic grading thermodynamic diagram is generated, and a personalized regulation and control strategy is recommended through reinforcement learning; and finally, sensing an actual regulation and control effect through a feedback mechanism, dynamically correcting twin model parameters by using deviation analysis, and constructing a knowledge base based on historical strategies and effects to drive strategy self-optimization. According to the method, a sensing-modeling-regulation-feedback closed loop is formed, and the accuracy and adaptability of ecological management are improved.

The invention discloses a forestry ecological environment real-time monitoring and management method based on big data, and belongs to the technical field of forestry ecological environment management, and the method comprises the following steps: S1, multi-source heterogeneous data collection and sensor network deployment; s2, multi-modal data fusion and intelligent transmission: developing an adaptive communication protocol dynamic switching module, and automatically switching to a satellite communication link in a 4G / 5G network blind area; s3, mass data storage and distributed calculation: constructing a hybrid cloud storage architecture, and storing real-time monitoring data into a Redis cache queue; s4, intelligently extracting ecological environment indexes; s5, dynamically evaluating the ecological bearing capacity; s6, performing multi-level early warning and emergency response; s7, making a precise forestry management decision; and S8, carrying out system self-optimization and closed-loop management. According to the method, the air-space-ground integrated monitoring network is constructed, so that the comprehensiveness and the real-time performance of forestry ecological environment perception are remarkably improved; multi-source data of the unmanned aerial vehicle, the satellite remote sensing and the ground sensor are complementary.

The invention discloses a dynamic three-dimensional reconstruction method based on incremental updating, which belongs to the field of image data processing, and comprises the following steps: acquiring a forestry scene multi-view image to be reconstructed to form an initial data set D0; constructing an initial three-dimensional scene model of the forestry scene based on D0 and 3DGS methods; monitoring a forestry scene to construct an incremental data set Dt of the tth change; during each incremental learning, the previous incremental model is divided into a change area and a non-change area, and the incremental model of the tth incremental learning is obtained by optimizing the change area and the non-change area and constructing a new scene model and is used for image rendering. According to the method, full-scene reconstruction is avoided, and the accuracy of the three-dimensional model can be ensured while the efficiency of three-dimensional reconstruction in a dynamic environment is remarkably improved. And the scene can be updated only by adding a small number of view angle images, so that the cost and time of data acquisition are greatly reduced, and the method is particularly suitable for large-scale monitoring of forestry and the like and scenes needing long-term monitoring and real-time updating.

The invention relates to the technical field of remote sensing surveying and mapping, in particular to a forestry surveying and mapping method based on remote sensing big data, which comprises the following steps: integrating remote sensing images, unmanned aerial vehicle images, temperature sensor data and humidity sensor data to perform space-time alignment processing, filling missing data through time interpolation, and performing abnormal value detection at the same time; and combining the processed data to calculate ecological indexes of the forest area, and outputting a forest health ecological assessment result. According to the method, by integrating remote sensing images, unmanned aerial vehicle images and temperature and humidity sensor data, the accuracy and integrity of data are improved, species identification and ecological function contribution analysis are achieved, key species and contribution of the key species to an ecological system can be accurately identified, and by analyzing the dynamic change trend of forest resources and climate influence, the forest resources can be accurately identified. According to the method, resource configuration can be effectively optimized, the management efficiency and the risk management and control capability are improved, the precision and timeliness of forest resource monitoring, assessment and management are improved, and the sustainable utilization and ecological protection capabilities are enhanced.

The invention discloses an unmanned aerial vehicle real-time vegetation classification system and method based on a lightweight AI model, and belongs to the technical field of vegetation monitoring and analysis. On the basis of an existing processor of the unmanned aerial vehicle, vegetation real-time classification is achieved by optimizing the deep learning model, and the flight path is dynamically adjusted according to the classification result. The method comprises the steps of system configuration and initialization, lightweight AI model development and optimization, real-time data acquisition and processing, adaptive flight path adjustment and feedback optimization. The method has the following advantages: (1) real-time classification of vegetation and adaptive flight path adjustment are realized, and the acquisition efficiency is optimized; (2) the sampling density is adaptively adjusted for different value regions, and the precision and coverage efficiency of vegetation classification are improved; (3) the acquisition strategy is optimized in real time in combination with the battery state, and unnecessary flight time and energy consumption are reduced; and (4) the method is suitable for application fields such as agricultural monitoring, forestry management and ecological protection.

The invention provides an intelligent processing method and system for multi-modal data of an industrial chain of the forest industry, and relates to the technical field of data processing. According to the method, acquisition nodes are arranged in links of seedling raising, planting, tending, felling, transportation, processing, storage and sales, and three types of original data of images, sensors and texts are obtained; forming a time-space aligned image feature, sensor feature and text feature set through unified time correction, space coordinate conversion, anomaly elimination, deletion interpolation and normalization; and further mapping the three types of features to the same feature space by adopting a unified coding mechanism, and constructing a knowledge graph by combining space-time diagram modeling and a forest ontology library to obtain multi-modal fusion feature representation. According to the invention, a high-consistency and extensible data basis is provided for forestry full-life-cycle digital management.

The invention discloses a soil sampling device for forestry surveying and mapping, and relates to the technical field of soil sampling, the soil sampling device comprises an outer cylinder and an inner rod, the outer side of the inner rod is provided with an underground tunnel inner side wall protection mechanism, and the underground tunnel inner side wall protection mechanism comprises a mounting sleeve which is fixedly connected to the outer side wall of the inner rod; adjusting frames are fixedly connected to the outer side wall of the mounting sleeve at equal intervals, and adjusting sliding rails are fixedly connected to the inner walls of the two sides of each adjusting frame. According to the soil sampling device for forestry surveying and mapping, in the process that the sampling device is guided into an underground tunnel through an external lifting mechanism, a third intermittent adjusting hydraulic cylinder drives an inner pressing arc plate to extrude towards the inner side wall of the underground tunnel, preliminary extrusion and reinforcement are completed, and in the extrusion process, the sampling device is not prone to deformation; and the convex pressing rods are gradually pressed into the inner side wall of the tunnel, so that soil on the inner side wall of the tunnel is secondarily extruded and reinforced, the compactness of the soil on the inner side wall of the tunnel is gradually increased, and the collapse risk of the soil is reduced.

The invention belongs to the field of forestry carbon neutralization, relates to a forest fire loss and forest restoration carbon emission accounting method, and constructs a forest fire ecological loss and forest restoration carbon emission accounting framework based on full life cycle evaluation. The framework is divided into two stages of forest operation and fire damage, input and output of each stage are tracked in details, and a life cycle list is formed; an accounting model is developed in combination with an activity emission factor and a biomass conversion coefficient, and the reliability of the model is ensured through sensitivity analysis; the framework fills the theoretical blank of forest fire full-life-cycle carbon footprint accounting, and the accounting transparency and accuracy are improved; the system assists in environment decision making, provides a tool for monitoring fire influence and evaluating recovery benefits for forestry managers, promotes low-carbon management strategy and technical innovation, optimizes a supply chain, and reduces overall carbon emission. The system has a remarkable promotion effect on green economy and sustainable development.

The invention relates to the technical field of intelligent path planning, in particular to an intelligent path planning method and system for hilly and mountain forest machine operation based on satellite remote sensing, and is suitable for intelligent path planning of forestry mechanized operation under complex terrain conditions. Building a multi-dimensional energy consumption prediction model in combination with forest machine parameter data; an initial path is generated by adopting layered progressive path planning, and dynamic adjustment is performed by utilizing real-time environment data so as to adapt to a complex mountain environment; under the scene of multi-machine collaborative operation, the efficiency and safety of the final operation path are ensured through optimization of the adjusted path, the method accurately recognizes the dangerous area, optimizes the passing path, remarkably improves the safety and efficiency of forest machine operation, and is suitable for the complex operation environment of hills and mountains.

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

The invention relates to the technical field of data management, in particular to an intelligent forestry data management system and method based on data fusion. The method comprises the following steps: obtaining forest region sensing data and forest region remote sensing data, and carrying out forest region resource trend analysis to obtain forest region resource trend data; constructing a seasonal forest region resource state model set based on the forest region grid resource state data and the forest region resource trend data; performing forest tree suitable region division according to the seasonal forest region resource state model set to obtain forest tree suitable region data; performing forest growth migration factor calculation according to the seasonal forest region resource state model set to obtain a forest growth migration factor; and performing forest region forest growth health degree evaluation based on the forest growth offset factor to obtain forest region forest growth health degree data, and performing forest region planting strategy optimization to obtain a forest region planting management strategy. The automation level of forest resource management can be improved.

The invention discloses a PagPB gene for regulating and controlling a woody plant root system to absorb and utilize nitrogen and application of the PagPB gene, and relates to the technical field of genetic engineering breeding, and the nucleotide sequence of the PagPB gene is as shown in SEQ ID NO.1. It is found that the nitrogen absorption and utilization efficiency of the poplar root system can be improved through the silent PagPB gene, the nitrogen absorption and utilization efficiency of the poplar root system is reduced through overexpression of the PagPB gene, important gene resources are provided for mining of woody plant nitrogen absorption and utilization key regulatory genes, and the PagPB gene has important application value in the fields of forest genetic engineering and clonal forestry.

The invention discloses a fertilization device for forestry planting, which comprises an arranged support, a storage hopper is arranged on the support, the storage hopper is connected with a temporary storage box through a hose, and the inner side of the temporary storage box is connected with a rotating cylinder in a penetrating manner; a lifting seat is connected to the support through a lifting assembly, a first motor is installed on the lifting seat, the output end of the first motor is connected with a rotating cylinder, a through groove is formed in the side end of the rotating cylinder, a pipeline is arranged in the rotating cylinder and communicates with the through groove, and a ground drilling piece is arranged at the bottom of the rotating cylinder. According to the fertilization device for forestry planting, water and fertilizer are output through cooperation of a pipeline and a ground drilling part, a rotating cylinder drives the ground drilling part to rotate, the water and fertilizer are conveniently conveyed into soil, the problem that the effect is poor due to the fact that the fertilizer is scattered on the surface of the soil is solved, soil loosening and feeding are synchronously carried out, time is saved, and the practicability is high. And the fertilizer can be accurately fed into the soil, so that the fertilization deviation is reduced.

The invention relates to the technical field of forestry management data processing, in particular to a forestry investigation planning design and analysis method based on three-dimensional laser modeling, and the method comprises the steps: collecting laser radar point cloud data, a multispectral image and a thermal infrared image of a target area through an unmanned plane, a ground mobile platform and satellite remote sensing; establishing corresponding analysis packets based on different analysis scenes, adaptively selecting a corresponding algorithm for each analysis packet to perform data compression and dimension reduction, and then fusing the data; for each analysis packet, constructing a corresponding three-dimensional model by using the fused data, and extracting feature parameters required by forestry investigation from the three-dimensional model; and the analysis packets are fused, the extracted characteristic parameters are analyzed and constructed, and a basis is provided for forestry planning design and analysis. According to the method, the limitation of poor adaptability in traditional forestry investigation is solved through a technical link of scene analysis packet construction, differentiation processing and cross-scale fusion.

The invention provides an intelligent forestry monitoring system and method based on edge computing, and the method comprises the steps: obtaining the related data of the topography and landform of a forest region, and obtaining a digital elevation model of the topography and landform of the forest region through the processing of the obtained related data; storing the obtained digital elevation model into a cloud platform; and analyzing the digital elevation model of the forest landform stored in the cloud platform through an artificial intelligence algorithm, determining a sensor deployment position according to an analysis result, and deploying a corresponding sensor according to the sensor deployment position. The method comprises the following steps: acquiring original data through a sensor, connecting edge equipment with the sensor, and acquiring the original data acquired by the sensor in real time; through real-time data acquisition and processing of the edge device and the sensor, monitoring data can be timely transmitted to the cloud platform for further analysis and processing.

The invention is suitable for the technical field of forestry seedling raising, and provides a forestry seedling raising transplanter which comprises an annular fixing plate and a connecting assembly arranged on the annular fixing plate, and further comprises an annular mounting plate rotationally connected to the upper end of the annular fixing plate through a rotary matching assembly, a nursery stock avoiding opening I and a nursery stock avoiding opening II are respectively formed in the annular fixing plate and the annular mounting plate; three arc-shaped guide plates are annularly and evenly fixed to the upper end of the annular mounting plate, guide grooves are formed in the three arc-shaped guide plates, and guide blocks are slidably connected into the three guide grooves. The driving assembly drives the annular mounting plate to rotate in a reciprocating mode, the annular mounting plate drives the three arc-shaped cutting knives to rotate in a reciprocating mode, under the guiding effect of the guiding groove and the guiding block, the synchronous lifting mechanism drives the three arc-shaped cutting knives to stretch into soil, and then the three arc-shaped cutting knives stretch into the soil in a reciprocating rotation mode; therefore, the soil and seedling root cutting effect of the arc-shaped cutting knife is improved.

The present invention belongs to the autonomous and precision agriculture sector, and refers, more specifically, to a system for seedling planting machines with a focus on reforestation and forestry (silviculture) for mechanized planting, in areas of installation, renovation and / or recovery of degraded areas with minimal impact on the environment, self-propelled, which plants from 1,800 to 3,600 seedlings per hour, in a sliding and continuous system, with water or gel irrigation, guaranteeing the quality of planting with an intelligent system, which checks each seedling, evaluating whether there has been drowning of the collection, exposure of the substrate, if the seedling is inclined, indicating even if the seedling was not planted and where it happened, generating a planting map (KML) with the GPS position of each seedling.

The invention relates to the field of forestry pest prevention and control, and discloses a juglans sigillata forest intelligent pest prevention and control system and method.The juglans sigillata forest intelligent pest prevention and control system comprises a data acquisition module used for collecting basic information of a juglans sigillata forest, and the basic information comprises meteorological environment parameters, soil physical and chemical properties and in-forest visual image data; the identification and analysis module is used for deeply analyzing data characteristics of the basic information, discriminating types and severity of diseases and pests, predicting potential occurrence risks of the diseases and pests and forming an evaluation result; and the early warning response module is used for presetting a risk level threshold value and rule logic and generating an early warning signal of a corresponding level according to an evaluation result. A data acquisition module integrating multiple types of monitoring units and communication units is constructed, so that the juglans sigillata forest can be sensed in all directions, the limitation of manual inspection is eliminated, and the problems of inaccurate monitoring and difficulty in data backtracking are solved; and the identification analysis module comprises sample management, model training and data analysis and prediction.

The invention discloses a pine wood nematode disease tree grading detection method based on unmanned aerial vehicle remote sensing and an improved PWD-YOLOv10n algorithm, and relates to the technical field of forestry harmful diseased tree detection, and the method comprises the steps: (1) diseased tree remote sensing image collection; (2) constructing a data set; (3) network model improvement; (4) selecting evaluation indexes; (5) result evaluation: in the step (3), by designing a PWD-YOLOv10 model with high precision and low leak detection, introducing a CAA attention mechanism module into a backbone network of the YOLOv10n model so as to enhance the feature extraction capability; c2fEMSCP modules are introduced into a backbone part and a neck part respectively, the recognition precision of the model is further improved, the omission ratio is effectively reduced, and rapid and accurate grading detection on the pine wood nematode disease tree in a complex environment is realized by training the high-performance PWD-YOLOv10n model.

The invention discloses an intelligent forestry monitoring system, and belongs to the technical field of forestry monitoring, and the system has the beneficial effects in multiple aspects: in data collection, a sensor module accurately collects various environmental data in real time, which is superior to traditional manual collection, helps forestry management personnel comprehensively understand dynamic changes, and image collection realizes visual continuous monitoring; the power supply module solves the problem of power supply, reduces the cost and environmental pressure, ensures stable operation and stable and complete data transmission; the central processing module can predict risks and give an early warning; the system has a stability guarantee mechanism and is good in expansibility; in a word, various advanced technologies are fused; efficiency and precision are improved, cost is reduced, protection capability is enhanced, and sustainable development of forestry is promoted.

The invention relates to a forest cultivation dynamic monitoring method and system based on a remote sensing technology, and the method comprises the steps: collecting point cloud and multi-source topographic data of a complex topographic region through the fusion of an airborne laser radar and a satellite radar technology, and generating a high-precision point cloud and topographic parameter matrix through denoising and registration; a segmentation threshold value is dynamically optimized in combination with topographic features, the canopy point cloud penetration rate of abrupt slope and valley areas is improved, and the problem of canopy missegmentation caused by uneven point cloud density under a complex terrain is solved; constructing a penetration rate compensation model driven by terrain influence factors, correcting laser radar point cloud deviation and inverting high-precision tree height, crown breadth and biomass parameters; and based on fusion analysis of the multi-temporal vegetation index and a machine learning model, early warning of diseases and insect pests and fire hazards and dynamic evaluation of a man-made forest cultivation effect are realized. According to the method, the bottleneck of complex terrain monitoring is broken through, the forest parameter inversion precision and the real-time response capability are remarkably improved, and technical support is provided for precise management of forestry resources.

The invention discloses a forestry fireproof early warning method and system based on the Internet of Things, and relates to the technical field of forestry monitoring, prevention and control, a heterogeneous sensing network is constructed through millimeter wave radar and multispectral imaging, and omnibearing monitoring in a complex environment is realized by combining terrain shielding coefficient Dmxs and signal coverage intensity Xgql analysis. The thermal radiation intensity Rsqd, the combustible drying index Gzzs and the airflow disturbance coefficient Qlys are fused to improve the hidden fire source identification precision; a LoRa relay and a Mesh network are adopted to ensure communication stability of a weak signal area, data transmission is optimized through edge calculation, and high-risk area resources are preferentially scheduled based on hierarchical response of a joint early warning index Lyzb; the risk assessment module calculates a fire danger index Hxzs in real time to predict fire spreading, the intelligent decision-making module comprehensively assesses hidden fire source probability Yhgl and a positioning error Wcsp to generate an early warning strategy, and the emergency linkage module automatically executes hierarchical response to form an intelligent monitoring-early warning-disposal closed-loop management system.

The invention relates to the technical field of forestry investigation, and discloses a forestry investigation system for carbon sink forest management, which comprises a data acquisition module, a data processing module, a data integration module, a model construction and analysis module, a decision and early warning module, an output module and an implementation monitoring module. Through multi-dimensional index investigation, information can be obtained from multiple levels of an ecological system, the carbon cycle process and ecological functions of the carbon sink forest are comprehensively known, more comprehensive data support is provided for scientific management, in addition, more variables influencing carbon sink are introduced, so that the model better conforms to the actual situation, the accuracy and generalization ability of the model are enhanced, and the method is suitable for popularization and application. According to the method, carbon reserve changes of the carbon sink forest under different conditions can be better predicted, finally, effect prediction of multiple management schemes is provided for managers through scene simulation and model analysis, scientific and reasonable management decision making is assisted, and meanwhile, an early warning mechanism can find potential risks in time and guarantee stable development of the carbon sink forest.

The invention provides a forestry pest monitoring method and system based on machine learning, and the method comprises the steps: carrying out the principal component analysis of a forestry pest image, obtaining a plurality of recognition indexes of the pest of a target forestry region, determining an effective feature vector when the image recognition of the forestry pest is carried out according to all recognition indexes, and carrying out the recognition of the effective feature vector. Determining a first-level correlation feature of the pest image in the target forestry region through the effective feature vector; extracting a plurality of environmental influence factors from the monitoring image, and determining secondary correlation features of the pest image in the target forestry area according to all the environmental influence factors; carrying out interactive fusion on the first-level correlation features and the second-level correlation features to obtain multi-level distribution features for carrying out image recognition on the diseases and insect pests; and identifying the current pest and disease distribution of the target forestry area based on the multi-level distribution characteristics through a pest and disease identification model. By adopting the scheme of the application, diversified monitoring of the forestry diseases and insect pests can be realized under a complex background, so that the false detection rate of the forestry diseases and insect pests is reduced.

The invention relates to the technical field of forestry intelligent management, in particular to a forestry plant regeneration cultivation intelligent analysis method and system. The method comprises the following steps: constructing a forestry digital twinborn model; according to the forestry digital twinborn model, carrying out plant individual matrix cultivation time sequence state analysis, and generating plant individual matrix cultivation time sequence state data; performing plant cultivation state comprehensive evaluation processing on the plant cultivation time sequence state characteristic data to generate plant cultivation state evaluation data; performing plant cultivation influence factor analysis based on the plant cultivation time sequence state characteristic data to generate plant cultivation influence factor data; performing plant cultivation state influence factor gain analysis on the plant cultivation state evaluation data and the plant cultivation influence factor data to generate plant cultivation state influence factor gain data; and designing a plant regeneration cultivation intelligent decision based on the plant cultivation state influence factor gain data. According to the invention, more intelligent and efficient forestry plant regeneration cultivation intelligent management is realized.