1022 results about "Canopy" patented technology

The invention relates to the technical field of data decision, in particular to an analysis decision method for crop growth monitoring data. The method comprises the following steps: collecting crop phenotypic characteristic data through a multi-source sensing array; dynamically dividing crop growth response stages according to phenotypic characteristics; constructing a growth situation prediction model based on the crop growth response stage; inputting the phenotypic characteristic data into a growth situation prediction model, simulating a crop growth process through a digital twin engine, and generating growth situation evolution data; extracting physiological anomaly characteristics of the growth situation evolution data, and generating stress response signals; obtaining a canopy three-dimensional phenotype map based on the stress response signal; positioning vascular bundle nodes of the canopy three-dimensional phenotype map through morphological topology analysis; according to the invention, through multi-source data acquisition, dynamic growth stage division, accurate model prediction, three-dimensional phenotype monitoring and self-correction decision optimization, the accuracy of analysis decision for crop growth monitoring data is improved.

The invention relates to the technical field of agricultural planting management, and discloses an intelligent agricultural planting method and system. The method comprises the following steps: analyzing nutritional requirements based on environment sensing and plant image data, and generating nutrition allocation data; carrying out growth prediction through seedling image features and formulating a cultivation standard; spectral parameters are extracted according to environment condition analysis and canopy images, and an environment regulation and control scheme is formed; combining growth state evaluation and pest and disease damage characteristics to calculate health indexes; a management strategy is optimized through growth parameter time sequence analysis; and determining the harvesting opportunity and grade evaluation according to the plant maturity and quality characteristics. According to the application, environment monitoring, growth diagnosis, quality evaluation and market analysis are organically combined, so that more accurate and intelligent agricultural planting decision support is provided.

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention belongs to the technical field of intelligent agriculture, and discloses a canopy-atmospheric environment information-based soil water content prediction method and system, and the method comprises the steps: setting a canopy and atmospheric environment monitoring unit in a field, and synchronously collecting the canopy temperature, wind speed, relative humidity, and six-dimensional environment parameters of atmospheric temperature, wind speed and relative humidity; the crown temperature difference is calculated after preprocessing; the method comprises the following steps: constructing a training set by utilizing measured data of soil relative water content, constructing a soil water content prediction model by adopting a random forest algorithm, screening and confirming an optimal prediction model through grid search and cross validation, and screening key features by combining an SHAP value; the water demand is automatically calculated according to a pre-established crop whole-growth-period drought stress threshold table and a dynamic irrigation decision formula, and meanwhile, the adaptation of a general model to a regional specific model is realized by constructing an environmental parameter-actually measured water content database and transfer learning, so that the soil water content state is accurately predicted, and the soil quality is improved. And a scientific basis is provided for precise irrigation.

The invention relates to the technical field of environment regulation, in particular to a corn cultivation environment intelligent regulation method based on multi-sensor control, which comprises the following steps: S1, generating a soil moisture three-dimensional distribution diagram in real time through humidity sensor arrays arranged at different depths of a corn root zone; s2, combining infrared temperature data of the canopy and the three-dimensional distribution diagram of the soil moisture, and calculating a moisture motion mismatch degree through transpiration-permeation conduction collaborative analysis; s3, generating a user-defined irrigation mode based on the water movement mismatch degree, and controlling the irrigation duration, the stop interval and the water flow rate. According to the method, coupling modeling is carried out on the plant physiological water demand behavior and the soil moisture supply characteristic, the limitation that a traditional irrigation system only depends on a soil humidity threshold value or meteorological index judgment is broken through, and the biological adaptability and the aging responsiveness of field moisture regulation and control are improved.

Provided is an individual-tree segmentation method of UAV LiDAR point cloud based on canopy morphology. The method uses woodland data obtained by a UAV LiDAR to: extract initial canopies from a CHM based on a region growing algorithm, determine whether each initial canopy is a correct segmentation canopy according to the number of local density maximum points in each initial canopy, finely segment each wrong segmentation canopy according to canopy morphology to obtain an updated set of tree tops, and finally use each of the updated set of tree tops as a seed point for performing the region growing algorithm to thereby obtain a final individual-tree segmentation result. The method make full use of height information and density information contained in a tree point cloud; and under the guidance of the density information, a wrong segmentation tree can be more accurately identified.

The invention discloses a method and system for estimating irrigation water consumption and water utilization efficiency, and relates to the technical field of water resource management, and the method comprises the steps: collecting the plant stem flow rate, canopy temperature and soil profile water content data of a target area through a multi-source sensing device, and obtaining a gradient and permeability distribution diagram of a geographic information system; dividing the farmland of the target area into sub-areas with different irrigation priorities based on a soil conductivity mutation point detection algorithm; constructing a dynamic water demand model fused with the plant transpiration hysteresis effect, performing space-time correlation analysis on the stem flow rate data and the soil moisture content gradient, and calculating the real-time water demand intensity of each sub-region; the method comprises the following steps: accessing short-time rainfall prediction data of a meteorological radar, dynamically correcting a future irrigation decision by adopting a sliding time window mechanism, and triggering a gradient delay irrigation instruction when the predicted rainfall amount exceeds an irrigation threshold value; and calculating the actual moisture utilization efficiency based on the inversion data of the moisture content of the irrigated soil and the unmanned aerial vehicle multispectral image.

The invention discloses a crop canopy height estimation method based on laser radar and multispectral fusion, and the method comprises the steps: scanning a crop field through the laser radar, and obtaining the field point cloud data; aerial photography is carried out on a crop field through an airborne multispectral camera to obtain field multispectral image data, and after data image splicing, crop region extraction and land parcel segmentation, two point cloud feature parameters with the strongest correlation with crops are extracted from point cloud data to obtain a point cloud feature parameter; the method comprises the following steps of: extracting a vegetation index with the strongest correlation with the crop height from multispectral image data, solving through multiple linear regression, and establishing a height estimation model; according to the invention, the multispectral vegetation index and the laser radar data are combined, so that the crop canopy height monitoring precision is improved, the crop growth monitoring precision and reliability can be improved, the crop growth condition can be comprehensively understood, and an agricultural decision maker can determine the crop canopy height based on the crop canopy structure data. And a more accurate, timely and effective farmland management decision can be made.

The invention discloses a forestry intelligent spraying system and spraying method based on multi-source sensor space perception, and relates to the technical field of intelligent control. A target forest region is scanned through a laser radar and a visual sensor carried by an unmanned aerial vehicle, a three-dimensional forest region map is constructed, and a single tree is identified and positioned by using a tree body identification model; evaluating the leaf density of the canopy; identifying diseases and insect pests by utilizing multispectral imaging, and making a pesticide proportioning decision according to the disease and insect pest types and severity; planning and generating an optimal flight path for spraying of the unmanned aerial vehicle; adjusting nozzle parameters according to the leaf density of the canopy and the severity of diseases and pests, performing variable spraying, and storing the spraying operation parameters of the unmanned aerial vehicle. Through multi-source sensor fusion, an intelligent decision algorithm and precise spraying control, autonomous obstacle avoidance, high-precision map construction, tree body recognition and canopy analysis, pest and disease damage detection and dynamic pesticide dispensing and spraying in a forestry scene are realized, the forestry spraying efficiency is remarkably improved, and pesticide waste is reduced.

A ginkgo canopy chlorophyll content estimation method based on an airborne hyperspectral image comprises the following steps: firstly, acquiring a ginkgo leaf canopy hyperspectral image and a chlorophyll content data set; and establishing a rapid prediction model for predicting the average content of chlorophyll in the canopy by using the hyperspectral image of the ginkgo leaf canopy, and predicting the corresponding chlorophyll content by using the model and taking the hyperspectral image of the ginkgo leaf canopy as input. The model establishment process comprises the following steps of: firstly, preprocessing an image by using multiple scatter correction (MSC); secondly, carrying out characteristic wave band selection on the preprocessed spectral data by adopting a competitive self-adaptive reweighted sampling algorithm CARS; and finally, substituting the screened characteristic wave band spectral data and the actually measured chlorophyll value into an XGBoost-RF machine learning model, and establishing a hyperspectral chlorophyll content estimation model. The method provided by the invention is a nondestructive rapid detection technology, and can monitor the plant health condition in real time.

The invention discloses a monitoring system for forest ecological carbon sink calculation, and belongs to the field of ecological environment monitoring and carbon sink evaluation. Comprising a foundation optical remote sensing module, a crown biomass calculation module, a soil carbon component identification module, a vertical carbon flux detection module, a regional microclimate analysis module, a time sequence big data coupling module, a carbon source and carbon sink model fusion module and a result correction feedback module. The method comprises the following steps: collecting vegetation three-dimensional structure data through a multi-frequency laser radar, calculating unit-area canopy carbon storage acceleration in combination with seasonal climate interference parameters, and fusing turbulent carbon exchange and soil carbon change data to construct a regional carbon flux coupling model; the system collects cross-rosa chinensis carbon change data to construct a multi-dimensional feature tensor diagram, dynamically updates a weight and a fitting function, realizes carbon sink dynamic estimation and feedback optimization based on a biology-climate-time three-dimensional cooperation mechanism, and improves forest carbon sink monitoring precision and stability. The beneficial effect is that scientificity and accuracy of ecological environment monitoring and carbon sink evaluation are improved.

The present invention relates to a method for remotely and / or autonomously harvesting a tree from air. The method comprises detecting by at least one sensor on a UAV (100) a tree to be harvested, positioning a harvesting tool (105) comprising delimbing means (114) carried by the UAV (100), by using information from the at least one sensor, at a predetermined distance above a canopy of the tree to be harvested, releasing the harvesting tool (105) in a first mode from the UAV (100) from the predetermined distance above the canopy of the tree to be harvested, setting the harvesting tool (105) in a second mode when the harvesting tool (105) has the tree top (131) within its tree receiving area (177). At least a portion of the harvesting tool (105) is below the tree top (131) of the tree to be harvested while the harvesting tool (105) has a certain speed >0 in a downward direction. The first and second mode differ with respect to the tree receiving area (177) of the delimbing means (114) in the harvesting tool (105). The tree receiving area (177) refers to the area in between open and closed delimbing means (114).

The invention provides a crop planting area intelligent extraction method and system based on multispectral remote sensing, and relates to the field of remote sensing image processing, and the method comprises the steps: obtaining and correcting a multiband remote sensing image; calculating a vegetation index and constructing a crop growth characterization index to extract canopy features; obtaining a multi-period feature map, calculating a spatial distribution entropy, and establishing an evaluation function to determine a time sequence fusion weight; a spatial constraint function is established based on the spectral distance, and a segmentation criterion is constructed by combining the spatial constraint function with time sequence features for classification iteration. According to the method, the accuracy and the discrimination degree of crop planting area extraction are improved, and crop identification requirements in a complex agricultural environment are met.

The present disclosure provides a decision-making method and apparatus for water and fertilizer stress of crops, and a mobile phone terminal, and belongs to the technical field of intelligent agriculture. The method includes: determining a current crop coefficient and canopy temperature of a crop to be identified on the basis of a first crop image and a second crop image; determining a water stress state and a nutrient stress state on the basis of the current crop coefficient and canopy temperature of the crop to be identified; and processing the water stress state and the nutrient stress state with a fuzzy control algorithm, and determining a water and fertilizer stress decision result of the crop to be identified.

The invention relates to the technical field of intelligent agriculture, in particular to a variable pesticide spraying method and system based on crop canopy recognition. The method comprises the following steps: acquiring canopy structure data; sparse anchor point data are obtained according to the canopy structure data; performing canopy segmentation according to the sparse anchor point data to obtain canopy segmentation data; performing canopy feature extraction on the canopy segmentation data to obtain canopy feature data; performing penetration demand measurement estimation according to the canopy feature data to obtain penetration demand measurement data; obtaining nozzle parameter data; obtaining nozzle control data according to the nozzle parameter data and the penetration demand measurement data; and performing operation monitoring according to the nozzle control data to obtain nozzle operation data so as to perform spraying uniformity feedback to obtain spraying uniformity data. According to the invention, through fusion of canopy structure identification and spraying path modeling, precise matching of the pesticide spraying direction, the spraying amount and the penetration demand is realized, and the spraying uniformity and the pesticide utilization efficiency are significantly improved.

The invention provides a wind power plant dynamic inertia partition evaluation method based on improved clustering, and relates to the technical field of wind power plants. The method comprises the following steps: taking a Canopy clustering algorithm as a preprocessing step through improved Canopy-Kmeans; the mean value point of the collected data set is calculated, and the measuring point closest to the mean value point is selected as the initial clustering center, so that the result is more reasonable and stable. The grouping precision is improved by considering the wake effect and the time delay effect and measuring multi-dimensional wind turbine generator state variables, in order to avoid a large amount of data dimensionality disasters, PCA principal component analysis is adopted for data dimensionality reduction, components with the variance contribution rate being 85% or above are selected as dominant variables, and the complexity of data collection is reduced. According to the method, the improved Canopy algorithm and the k-means algorithm are combined, the optimal classification number k value and the clustering center can be recognized more accurately, and then large-area wind turbine generator partitioning is achieved quickly and accurately.

The invention belongs to the technical field of chlorophyll content inversion, and discloses a canopy chlorophyll content inversion method based on feature extraction and a machine learning model, and the method comprises the following steps: employing a mode of combining an unmanned plane carrying a multispectral camera and ground actual measurement; collecting and processing the chlorophyll content data of the potato canopies, and planting a quilt index; carrying out feature selection by using a Pearson correlation analysis and competitive self-adaptive reweighted sampling algorithm; constructing a chlorophyll content inversion model based on machine learning; and carrying out chlorophyll content inversion model optimization based on a grey wolf optimization algorithm and a sparrow optimization algorithm, and carrying out precision evaluation. According to the canopy chlorophyll content inversion method based on feature extraction and the machine learning model, comparative study is carried out on the single growth period and the whole growth period of potatoes, the optimal inversion model suitable for the single growth period and the whole growth period is determined, and the accuracy and applicability of the study are guaranteed.

An intelligent air-assisted spraying device for orchards includes a traction mechanism, a spaying system, a collecting system, and a processing-controlling system. The traction mechanism is provided with a spray tank. The spaying system includes a main body connected to the spray tank and multiple spraying tubes, first ends of the spraying tube are communicated with the main body, and second ends of the spraying tubes are capable of rotating. The collecting system is configured to collect a contour and a volume of the canopy, and branch and leaf density of a fruit tree. The processing-controlling system is configured to obtain multiple spraying areas, calculate an air outlet angle of each spraying tube, and control a rotation of the second end of each spraying tube according to the air outlet angle. The device can adjust the spraying direction of each spraying tube, and the quality of pesticide application can be improved.

The invention discloses an evapotranspiration satellite remote sensing inversion method directly considering soil humidity stress, and the method comprises the steps: firstly constructing an improved canopy water vapor conductivity model, and then enabling the conductivity model to be coupled into a canopy transpiration module in a P-LSH algorithm, and obtaining an improved canopy transpiration algorithm; then analyzing the sensitivity and uncertainty of framework evaluation parameters, constructing a most effective humidity stress model suitable for bare soil evaporation in a bare soil area, and coupling the humidity stress model into a soil evaporation mode in a P-LSH algorithm to form an improved bare soil evaporation algorithm; and finally, coupling the improved canopy transpiration algorithm and the improved bare soil evaporation algorithm in combination with a water evaporation module in the P-LSH algorithm to obtain a global evapotranspiration remote sensing inversion algorithm directly considering soil humidity stress. According to the method, the stress effect of soil humidity on evapotranspiration is considered, a global evapotranspiration remote sensing inversion algorithm is developed, and a global evapotranspiration product with high precision and high temporal-spatial resolution is generated.

The invention provides a crop yield prediction method and system based on image analysis, relates to the field of yield prediction, and obtains a farmland multi-view remote sensing image through an unmanned aerial vehicle multispectral sensor, and executes aerial triangulation and ortho-rectification to obtain accurate exterior orientation elements and ortho-images. Then, vegetation indexes are extracted and combined with field yield measurement data, a yield regression model is constructed, and rapid prediction and grading of the crop yield are achieved. In order to reduce multi-view brightness inconsistency and give consideration to crop growth characteristics, prior information such as canopy row direction, leaf angle distribution and sealing degree is introduced, iterative correction is performed on the image by adopting a BRDF model, and radiation difference is corrected while geometric accuracy is guaranteed. Particularly, through staged trend analysis and multiple rounds of detection, priori conflicts can be found in time, incremental images can be flexibly collected, and resolving robustness and yield prediction precision are improved. The method can be widely applied to unmanned aerial vehicle remote sensing agricultural monitoring scenes.

The invention provides a rice growth model construction method and system based on salt stress, and relates to the technical field of growth model.The rice growth model construction method comprises the steps that firstly, monitoring points are arranged in a planting sample area, and soil conductivity data of different depths are obtained through a multi-depth soil conductivity sensor and a Kriging interpolation method; collecting root system density, a multispectral remote sensing image and a leaf area index, and calculating a canopy salt stress index; measuring the concentration of related ions to obtain a sodium adsorption ratio, and establishing a salt stress coefficient by combining the soil conductivity and the irrigation volume; fusing canopy and soil stress indexes to generate a stress comprehensive index, and introducing a salinity feedback item to represent the crop compensation capability; and finally, constructing a hybrid machine learning model by adopting a convolutional neural network and a long-short-term memory network to realize rice growth dynamic prediction. According to the method, multi-dimensional salinity stress quantification of a soil-crop system is realized, and decision support is provided for salinization treatment in precision agriculture.

The invention relates to a variable-depth, variable-position and variable-quantity intelligent fertilizer applicator based on the size of a tree crown, and belongs to the technical field of agricultural and forestry machinery. The machine comprises a power device, a fertilizing device, a spiral ditching device, an intelligent identification device and an intelligent control system which are supported on a rack, wherein the rack is arranged on a walking device. The fertilizer applicator can realize integrated operation of intelligent ditching, fertilizer application and soil and fertilizer stirring in agricultural planting environments such as orchards and the like, and meanwhile, can intelligently find an optimal root zone position and adjust the ditching depth, the fertilizer application position and the fertilizer application amount according to the size of a fruit tree crown and the distance of a tree machine, so that the ditching efficiency and the fertilizer utilization rate are improved.

The invention provides a fruit tree canopy three-dimensional model reconstruction method and a fruit tree canopy three-dimensional model reconstruction system based on multi-modal data, and relates to the technical field of fruit tree canopy three-dimensional model reconstruction based on multi-modal data. Sub-pixel-level space registration is achieved by means of ground control points, voxelization division is carried out based on the range of a point cloud bounding box, and the number of point clouds is screened to construct an initial seed set; and adopting a structure-spectrum collaborative growth strategy, taking the adjacent voxels which are not accessed as candidate voxels, calculating a spectrum similarity mean value of the candidate voxels and directly adjacent voxels in the seed set, stopping growth when no new voxels are added in continuous three times of iteration, and outputting a final seed set. Meanwhile, the set is converted into a three-dimensional canopy model fusing space coordinates and full-wave-band spectral reflectivity, voxel-level pathological labeling is achieved based on the chlorophyll sensitivity index and the blue-red slope, and the whole process of reconstruction and analysis is completed.

According to the permanent basic farmland quality intelligent monitoring system and method provided by the invention, accurate monitoring and dynamic management are realized through multi-module cooperation, the terrain gradient and canopy height are calculated based on LiDAR point cloud, the complexity index is generated in combination with meteorological data, and the A * algorithm is improved to optimize the unmanned aerial vehicle inspection path. The method comprises the following steps: extracting hyperspectral data to analyze soil and crop states, calibrating an electrode response curve, calculating a bio-availability index in combination with temperature and humidity, dynamically adjusting an early warning threshold value, identifying abnormal data by adopting an isolated forest algorithm and carrying out grading processing, and constructing a space-time cube by adopting space-time Kriging interpolation and CNMF algorithm and fusing multi-source data. According to the method, indexes such as ecological protection grades and economic values are integrated to generate a graded soil quality index model, a disease thermodynamic diagram is generated, key data are stored in a chaining mode, and farmland quality real-time monitoring, risk early warning and ecological economic balance decision making are achieved through multi-source heterogeneous data fusion, dynamic weight optimization and block chain technologies.

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

The invention discloses a method and equipment for monitoring crop growth, and belongs to the technical field of crop growth monitoring. The method for monitoring crop growth comprises the following steps: acquiring a multispectral image of crop vegetation and environmental data of a crop canopy and below the canopy; processing the multispectral image of the crop vegetation to calculate a vegetation index; selecting a specific vegetation index from the vegetation indexes; obtaining environment data after dimension reduction; environment data and vegetation indexes with high correlation with the CGI are extracted; and inputting the environmental data and the vegetation index into the CGI prediction model to obtain a CGI prediction value of the monitored crop. The equipment for monitoring the growth vigor of the crops can comprehensively monitor the crops in a close planting environment. According to the method, the crop growth is represented by adopting the comprehensive growth vigor grade evaluation index CGI, the crop growth vigor can be reflected more comprehensively and accurately, and the combined CGI prediction model is adopted, so that the method has the capability of accurately predicting the growth vigor of close-planting crops in a complex environment, especially in a complex environment.

The invention provides a cultivated land quality monitoring system based on cultivated land protection. The cultivated land quality monitoring system comprises a multi-source sensing module, a heterogeneous transmission module, a space-time database module, a quality degradation prediction module, a dynamic early warning module, a repair decision module and a quality traceability block chain module. The multi-source sensing module collects data such as soil pH value, organic matter content, heavy metal concentration, crop canopy temperature and vegetation index; the heterogeneous transmission module carries out encryption packaging and format standardization on the data; the space-time database module stores the processed data and space-time associated information; the quality degradation prediction module generates multi-dimensional monitoring data and predicts a degradation risk; the dynamic early warning module generates a multi-stage early warning signal according to the risk value; the restoration decision module outputs soil improvement engineering technical parameters and ecological compensation schemes; and the quality traceability block chain module realizes data verifiable evidence storage. The intelligent level of cultivated land protection can be improved, and comprehensive technical support is provided for sustainable utilization of cultivated land quality.

A sunlight greenhouse suitable for mechanical cultivation of vegetables relates to the technical field of vegetable cultivation and comprises a sunken cultivation bed, a front roof and a rear wall, a rear slope is obliquely arranged at the top of the rear wall, cultivation ridges are arranged on the cultivation bed in the east-west direction, and a vine hanging steel wire is arranged over each cultivation ridge; and an included angle A between a virtual connecting line between the upper edge of the rear slope and the upper edge of the front vertical surface of the cultivation bed and the ground plane is a geographic latitude of-10.5 degrees of the greenhouse. According to the method, the problems that in east-west cultivation ridges in the prior art, due to shielding between canopies, illumination distribution in the ridges and between the ridges is uneven, illumination of leaves on the south side and the north side of plants on the same ridge is uneven, and the light intensity of the north side is generally weaker than that of the south side; the illumination distribution among different ridges is not uniform, and the light intensity of the north ridge is relatively weak.

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 provides an inversion method of total chlorophyll fluorescence in a farmland canopy based on a ray tracing technology. The method comprises the following steps: step 1, performing canopy three-dimensional reconstruction based on point cloud; step 2, acquiring chlorophyll fluorescence proportion distribution in the canopy through forward ray tracing; and step 3, acquiring chlorophyll fluorescence value distribution in the canopy through backward ray tracing. Solar-induced chlorophyll fluorescence has a direct relationship with photosynthesis and is considered as a powerful way for estimating photosynthesis efficiency and vegetation productivity in recent years, and the method for directly inverting total chlorophyll fluorescence through observation data is realized through a brand new thought. And a foundation is laid for accurately and simply estimating the vegetation productivity.