619 results about "Canopy" patented technology

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 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.

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 disclosure provides an improved canopy height correction method. The method includes: obtaining GEDI LiDAR data, airborne canopy height data, GDEM with high resolution and land cover product within the selected target area and timeframe; performing quality filtering and spatial-scale filtering on GEDI footprints; extracting laser pointing parameters and waveform parameters; extracting reference canopy height from airborne data for each footprint; extracting laser pointing parameters and waveform parameters; preprocessing the GDEM and calculating topographic parameters, including topographic variability index (TVI); constructing the Laser Pointing and Topographic Index (LPTI) according to the 3D forest-ground geometry model; inputting the waveform parameters, even topographic parameters, TVI and LPTI as independent variables, and the reference canopy height as the dependent variable to modeling an improved forest canopy height extraction, and utilizing the improved canopy height extraction model to correct the twofold influence of topographic on GEDI canopy height extraction.

The invention discloses an apple anthracnose leaf blight recognition five-dimensional data fusion method based on a patrol robot, relates to the technical field of anthracnose leaf blight recognition, and solves the problems of limited sensing dimension, large illumination interference and inaccurate spatial positioning in the prior art. Comprising the following steps: S1, acquiring surface curvature characteristics of apple leaves, collecting RGB-D images of the leaves, constructing a three-dimensional topological structure of the leaves, and positioning scab space distribution; s2, acquiring spectral reflection information of apple leaves, quantifying biochemical components of chlorophyll a and carotenoid, and constructing a vegetation index feature set sensitive to diseases; s3, apple tree canopy temperature information is collected, a transpiration anomaly detection model is constructed, and multi-modal fusion of thermal infrared and point cloud data is realized; and S4, integrating spatial distribution, spectral reflection and thermal radiation information, constructing a five-dimensional phenotypic characteristic matrix model, and comprehensively analyzing apple phenotypic characteristics. According to the method, the recognition accuracy under the conditions of blade shielding, uneven illumination, blade posture change and environment interference can be remarkably improved.

The invention relates to a rice canopy optical and microwave integrated radiation modeling method, which comprises the following steps of: acquiring and uniformly processing rice canopy and environmental parameters, and constructing a same physical parameter system shared by optical and microwave models; the optical part is used for calculating bidirectional reflection factors and optical reflectivity of the rice canopy under different observation conditions based on an aquatic vegetation radiation transmission model; the microwave part is based on a vector radiation transfer equation, adopts a matrix multiplication method to calculate canopy multiple scattering, and simulates underlying surface scattering in combination with an improved integral equation model to obtain a multi-polarization backscattering coefficient; the optical reflectivity and the microwave backscattering coefficient are output under a unified parameter system, and optical and microwave collaborative forward modeling is achieved. According to the invention, a rice canopy optical and microwave integrated collaborative simulation framework is established based on a radiation transfer principle, a physical consistent basis is provided for crop parameter inversion, multi-source remote sensing data application and farmland environment monitoring, and the application prospect is wide.

The invention discloses a double-platform point cloud-oriented bidirectional hierarchical clustering single tree segmentation method, which belongs to the technical field of forestry monitoring, and comprises the following steps of: firstly, preprocessing and filtering an original point cloud, and then vertically layering along a Z axis according to a set slice thickness; in each layer, a clustering radius is adaptively determined by using a local point density estimation result, point cloud clustering is carried out by using a region growing method based on a search radius, and an initial single wood structure unit is extracted; and then, space connection operation is executed between the upper layer and the lower layer, points with continuous structures are assigned to the same target based on triple constraints of adjacent distance, lateral offset and height continuity, and finally accurate segmentation of the complete single-tree point cloud is realized. The BLS method supports a top-down segmentation strategy and a bottom-up segmentation strategy, automatic adaptation can be carried out according to platform types, trunk point clouds are preferentially segmented in TLS data, and a canopy structure is preferentially constructed in ULS data.

The invention relates to the technical field of precision agriculture and grape cultivation, and discloses a vineyard fertilization method based on unmanned aerial vehicle multi-source remote sensing data, and the method comprises the steps: obtaining a canopy multi-spectral image and three-dimensional structure data through an unmanned aerial vehicle; carrying out image preprocessing and finely extracting a grape canopy region; fusing the extracted spectral vegetation index, texture features and three-dimensional structure features, and constructing a multi-source feature vector; constructing and optimizing a nitrogen nutrition inversion model through a machine learning algorithm by utilizing actually measured nitrogen nutrition parameters; generating a nitrogen content distribution diagram based on a model inversion result, calculating the nitrogen deficiency amount and the recommended dressing pure nitrogen amount of each space unit in combination with critical nitrogen concentration diagnosis and target yield, and converting the nitrogen deficiency amount and the recommended dressing pure nitrogen amount into the use amount of a foliage spraying working solution; and generating a variable fertilization prescription map, and converting the variable fertilization prescription map into a nozzle flow control instruction executable by the unmanned aerial vehicle to realize on-demand accurate variable fertilization. According to the invention, closed-loop management from nitrogen nutrition monitoring to variable rate fertilization is realized, and the nitrogen fertilizer utilization efficiency and the fertilization accuracy are improved.

The invention discloses an unmanned aerial vehicle LiDAR-based high-canopy-density crop plant height double-end adaptive inversion method and system, and relates to the technical field of agricultural remote sensing. The method comprises the following steps: acquiring a crop canopy point cloud and extracting density, skewness and original ground elevation characteristics; constructing a near-surface pseudo ground layer thickness correction model, introducing a pseudo ground layer thickness parameter to carry out pressing correction of a physical layer on an original ground elevation, and reconstructing a real bare soil layer ground reference; constructing a canopy end density-form adaptive model, and dynamically calculating the optimal upper boundary percentile by using the point cloud density and the elevation distribution skewness; and calculating the final plant height based on a double-end correction result. According to the method, the problems of systematic overestimation caused by near-surface pseudo ground blocking and poor adaptability of a fixed threshold value to the canopy form in LiDAR ground detection under the high-canopy-density canopy are effectively solved, and high-precision and robust plant height automatic extraction under multiple varieties and different flight heights is realized under the condition that external terrain auxiliary data is not needed.

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 relates to the technical field of agricultural information, in particular to a crop yield prediction method, a verification method, a computer system and a readable storage medium. Standardized moisture productivity, a crop coefficient before canopy aging, a harvest index, a maximum canopy coverage degree, a canopy growth coefficient, a canopy attenuation coefficient, a minimum rooting length and a maximum rooting length are selected as analysis parameters, and the main effect of crop model parameters is quantified through a first-order sensitivity index. The interaction effect of crop model parameters is quantified through the global sensitivity index, and the complexity of a parameter system is reduced; a differentiation calibration strategy is constructed based on sensitivity grading, a traditional all-parameter parameter adjustment mode is replaced, and the complexity of a parameter system is remarkably reduced. The objective of the invention is to solve the problem of how to improve the crop yield prediction precision of a crop model in a climate-variable region.

The invention relates to the technical field of agricultural intelligent planting, in particular to a crop growth analysis and fertilization optimization method fusing multi-source data, which comprises the following steps: acquiring crop canopy reflectivity data, sunlight perpendicular incidence angle data and a soil fertility index in real time; sunlight perpendicular incidence angle data are analyzed into a daily cumulative effective illumination intensity distribution diagram, a three-dimensional fertility gradient model is constructed in combination with a soil fertility index, canopy reflectivity data are recognized as a growth vigor difference quantization map through a machine learning classifier, the three types of data are fused through a space-time matching algorithm, and the growth vigor difference quantization map is obtained. Generating a correlation mapping matrix which takes a plant individual as a minimum unit and marks an illumination and soil deviation superposition influence coefficient, finally calculating the difference degree of fertilizer requirements of adjacent plants based on the superposition influence coefficient, dividing micro-scale dynamic fertilization partitions, and generating a partition plant variable compensation fertilization prescription map. And the variable fertilizer applicator is driven to execute differentiated fertilization operation, so that the growth analysis accuracy and the fertilizer utilization rate are improved.

The invention discloses a crown instance segmentation and maturity evaluation method based on dynamic expansion convolution. The method comprises the following steps: firstly, carrying out radiation correction, geometric correction and histogram equalization preprocessing on a forest RGB image acquired by an unmanned aerial vehicle; then extracting global contour features through an adaptive dynamic expansion convolution module, enhancing local detail capture capability in combination with an edge perception feature pyramid network, and realizing multi-level feature fusion by using a double attention mechanism; then, a density adaptive contour cross suppression algorithm is adopted to optimize the mask, and the distinguishing precision of the dense area is improved through dynamic adjustment of a suppression threshold value and ray method contour cross calculation; and finally, calculating canopy density based on a segmentation result, and constructing a maturity index model by fusing the shape, color and geometric features of the crown breadth. According to the method, the extraction capability of the sub-pixel-level details of the crown edge is remarkably improved, the problems of fuzzy irregular contour segmentation and high-density region misjudgment are effectively solved, and an efficient and accurate technical scheme is provided for forestry resource monitoring.

The invention relates to the technical field of smart agriculture, and specifically provides a crop growth situation prediction method based on YOLO and Transform space-time collaboration. The method aims at solving the problem that in the prior art, prediction precision is insufficient due to splitting of spatial features and time sequence information, and crop phenotypic features (plant height, leaf area index, canopy coverage and the like) in a farmland video stream are extracted in real time through an improved YOLO model; constructing a spatial-temporal feature encoder, and modeling a coupling relationship between historical time sequence environmental data (temperature, illumination and soil moisture content) and phenotypic features through a Transform network; designing a feature collaborative fusion module, and integrating spatial visual features and time sequence environment features by adopting an adaptive weight distribution mechanism; and establishing a growth situation prediction model based on space-time cooperation characteristics, and outputting key parameter probability distribution of a future growth stage.

The invention discloses a forest canopy height remote sensing estimation method based on multi-forest feature fusion and a bidirectional stacking model, and the method comprises the steps: obtaining multi-source remote sensing data of a target region, and employing a multi-stage feature selection method MBF-Control to screen an optimal feature subset for spatial distribution data of different forest types, the multi-source remote sensing data comprises GEDI data, Sentinel-1 / 2 data, Landsat-8 data and DEM (Digital Elevation Model) data; a bidirectional stacking model BS-MFTF of multi-forest type feature fusion is constructed; and inputting the optimal feature subset into the multi-forest feature fused bidirectional stacking model BS-MFTF, and outputting a canopy height estimated value of the target area. Structural diversity and spatial patterns are captured, and canopy height estimation and forest structure mapping are improved.

The invention relates to the field of forest ecological monitoring and operation management, and provides a method for quantifying the complexity of a three-dimensional structure of a forest canopy based on LiDAR and fractal dimension. Aiming at the key bottleneck that traditional canopy structure quantitative indexes are difficult to represent cross-scale three-dimensional space heterogeneity, high-precision three-dimensional point cloud data are acquired by using foundation and tower footing LiDAR, and precise individual tree segmentation is realized in combination with a machine learning clustering algorithm and tree structure features. According to the method, the three-dimensional void rate of the forest stand canopy is calculated, and the fractal dimension of a single tree is calculated by adopting a box counting method. The weight of a single tree is determined by comprehensively considering a single tree structure and a spatial distribution pattern of the single tree structure, the fractal dimension of a stand scale is calculated, a canopy structure complexity index is constructed in combination with a stand three-dimensional void rate, and scale-free quantification of the forest canopy three-dimensional structure complexity is achieved. According to the method, typical temperate zone secondary forest monitoring data is used for simulation and verification, and technical support is provided for forest accurate operation (cutting and complementary planting), carbon sink evaluation, biodiversity protection and the like.

The invention relates to a crown segmentation method based on a CSAF framework, and the method comprises the steps: integrating Fourier transform and a state space model through a GM-Mama module, and effectively improving the extraction capability of a fuzzy crown contour; a forgetting factor is dynamically regulated and controlled by using an MASA-Optimizer module in combination with a multi-agent negotiation mechanism, and continuous self-adaptive learning of dynamic changes of species and phenology is realized; a physical constraint based on a Poisson equation is introduced by means of an MPC-Poisson module, and spectral interference of non-target vegetation is significantly inhibited; according to the method provided by the invention, high-precision crown instance segmentation and counting can be realized in a cross-species and cross-phenological complex scene, accurate calculation of a vegetation index of a single plant level is supported, and a flexible and powerful method is provided for analysis of different types of forest crowns.

The invention provides an automatic illumination adjusting method and system based on seed and seedling cultivation, and relates to the technical field of illumination control and intelligent agriculture, the method comprises the following steps: firstly, collecting seedling chlorophyll fluorescence parameters to construct a light stress index, and accurately identifying a light inhibition risk; then calculating a physiologically permissible light intensity threshold adaptive to the dark response capability of the plant by combining the environment temperature and the CO2 concentration; positioning a shielded weak light area by using three-dimensional point cloud voxelization and a ray tracing technology, and calculating a shadow compensation gain coefficient; and finally, generating a control matrix in combination with a dynamic left-rotation light strategy, and driving an independent addressing LED array and a variable-angle lens to execute local precise light supplement and light vector adjustment. Through physiological feedback and spatial light field reconstruction, the problem that traditional illumination control neglects individual tolerance difference and canopy shielding is solved, photooxidation damage is effectively prevented, and the growth uniformity and the light energy utilization rate of seedling groups are remarkably improved.

The invention relates to the technical field of intelligent agriculture and crop cultivation, in particular to a soybean planting chemical control self-adaptive regulation and control method and system based on canopy risk identification, and the method comprises the steps: constructing a digital twinborn model for dynamically simulating the growth process of a soybean population; monitoring a canopy structure, a leaf area index LAI and a light interception rate of the soybean in the key growth period by using an unmanned aerial vehicle or a sensor, and calibrating the digital twinborn model; future weather forecast data are input into the calibrated digital twinborn model for growth simulation, and light distribution is simulated by means of a pre-established AI model to identify soybean population closing and illumination competition risks; and according to the risk identification result, a chemical control decision is generated autonomously, and the variable pesticide spraying machine is controlled to perform precise variable operation of soybean planting based on the chemical control decision. According to the method, group development can be predicted in a prospective manner, and risks can be accurately quantified, so that an intelligent regulation and control mechanism with prospective and active properties is actively triggered, and active intervention of planting is realized.

The invention provides an intelligent prediction method and device for the nitrogen content of grape canopy leaves, equipment and a storage medium. Relates to the field of agricultural information technology and computer vision crossing technology. The method comprises the following steps: acquiring a grape canopy RGB image and a corresponding total nitrogen content, and constructing an image discrimination data set and a nitrogen content prediction data set after preprocessing; constructing and training an image discrimination model, and discriminating whether the input image is a target image containing a canopy; constructing and training a nitrogen content prediction model, and taking the canopy RGB image as an input and output nitrogen content feature map; during actual prediction, an input image is screened by the discrimination model, if the input image is a target image, the target image is sent to the prediction model, and a nitrogen content prediction result is output. According to the method, efficient and accurate intelligent prediction of the nitrogen content is realized through double-model cooperation.

The invention discloses a landscaping maintenance monitoring and early warning method and system based on computer vision, particularly relates to the field of computer-aided identification, and is used for solving the problem that the existing landscaping maintenance depends on manual patrol and is difficult to find abnormal plant growth in time. The method comprises the following steps: acquiring a plant optical image, segmenting the plant optical image, extracting features such as shadow area proportion, compactness and gray average, and establishing a multi-scale shadow feature sequence; a shadow feature prediction model is trained by combining the environment and phenological data in the sliding time window; performing longitudinal and transverse residual calculation on the real-time observation features and the model prediction result, and fusing to generate an abnormal confidence score; enabling the abnormal confidence coefficient to correspond to a canopy projection region, and recognizing an abnormal region; and finally, the vegetation index difference ratio, texture and edge features of the abnormal region are extracted, the abnormal type is judged through a rule base, an early warning report is generated, and intelligent identification and efficient early warning of garden plant abnormity are realized.

The invention provides a crop growth state intelligent analysis method based on a multi-source remote sensing image, and belongs to the technical field of crop growth analysis based on computer data processing. The method comprises the following steps: firstly, collecting a multi-source remote sensing time sequence image covering the whole growth cycle, and constructing a multi-modal growth characteristic data set with a uniform structure; secondly, designing a multi-channel time sequence modeling structure, and cooperatively extracting dynamic evolution trajectories of core characteristics such as canopy height, leaf area index, SPAD value and the like based on space-based spectrum and foundation form information; then, a current state deviation type is identified through a multi-type track distribution learning model, and problem tracing and explanation analysis are carried out in combination with a diffusion matching mechanism; finally, a variable adjustment strategy is generated based on a trajectory deviation result and a management target, and optimization recommendation of management measures such as irrigation frequency and fertilization intensity is realized. The method can be widely applied to the scenes of agricultural remote sensing monitoring, intelligent agricultural machinery scheduling, precise planting guidance and the like, and has high interpretability and practical value.

The invention relates to an intelligent soil regulation and control method based on soil data and image processing. The method comprises the following steps: acquiring and standardizing soil data; orchard global multispectral image acquisition: acquiring and preprocessing an orchard image, and acquiring an orchard global multispectral image; vegetation region identification and parameter calculation: calculating NDVI based on red light and near infrared bands, distinguishing green manure and fruit tree regions through a threshold segmentation method, and calculating green manure coverage, a green manure normalized vegetation index and a fruit tree canopy area; carrying out soil-vegetation data fusion and parameter extraction; distributing weights based on the three types of core parameters, calculating the application amount and type ratio of nitrogen, phosphorus and potassium fertilizers, and generating an initial regulation and control scheme; and repeating the steps every specified time period to realize dynamic iterative optimization of the scheme. Through deep fusion of soil data and an image processing technology, intelligent soil regulation and control are realized, regulation and control accuracy and timeliness are remarkably improved, and fertilizer waste and environmental risks are reduced.

The invention provides a pine wood nematode disease tree detection method and system based on image analysis, and relates to the technical field of image processing. The method comprises the following steps: acquiring a to-be-analyzed pine image acquired by an unmanned aerial vehicle, and determining a plurality of reference crown areas in the to-be-analyzed pine image; respectively carrying out diseased tree fading analysis and crown form analysis on each reference crown area, carrying out primary anomaly detection on the plurality of reference crown areas, and dividing to obtain a reference sample group and an abnormal sample group; performing clustering analysis based on environment distribution difference on the plurality of reference tree crown areas in the reference sample group to generate a plurality of environment influence tree crown groups; and determining an environment influence crown group to which each reference crown region belongs in the abnormal sample group, and carrying out secondary anomaly detection on the plurality of reference crown regions to obtain a pine wood nematode pest detection result of the to-be-analyzed pine image. According to the invention, precise detection of the pine wood nematode disease tree is realized.

The application relates to a plant protection unmanned aerial vehicle spraying control method, device, equipment and medium, the method comprising: acquiring pose transformation data of each trunk of a pose sensor in a fruit tree canopy in a preset time range, each flight parameter of a plant protection unmanned aerial vehicle and fruit tree canopy image data of a vision sensor in the plant protection unmanned aerial vehicle; determining a mist drop deposition amount in the fruit tree canopy under each flight parameter, constructing a plant protection unmanned aerial vehicle spraying control model according to the mist drop deposition amount and the canopy disturbance prediction model; inputting each flight parameter of the plant protection unmanned aerial vehicle into a pre-trained plant protection unmanned aerial vehicle spraying control model, determining a fruit tree canopy disturbance state corresponding to the flight parameter and a mist drop deposition amount under the fruit tree canopy disturbance state, so as to complete the control of the plant protection unmanned aerial vehicle spraying. The application can greatly avoid the great influence of the wind field generated by the unmanned aerial vehicle rotor on the deposition effect of the pesticide mist drop.

The invention discloses a rice chlorophyll content prediction method, device, medium and equipment, and relates to the technical field of crop growth, and the method comprises the following steps: obtaining canopy spectral data and physicochemical parameters of rice in a to-be-detected area; dividing the rice canopy into three concentric circle areas based on the leaf inclination angles of different leaves of the rice, and obtaining the layered rice canopy; constructing a hierarchical radiation transfer model HRTM for simulating a rice hierarchical spectrum, and inputting the physical and chemical parameters into the hierarchical radiation transfer model HRTM to generate hierarchical spectrum data; constructing a lookup table LUT based on the physicochemical parameters and the layered spectral data, taking the lookup table LUT as an analog data set, improving the analog data set by adopting a quartile method, and determining analog spectral data; based on the simulated spectrum data, constructing an inversion model for acquiring the chlorophyll content of the rice layers layer by layer; and inputting hyperspectral data to be measured into the inversion model, and determining a corresponding chlorophyll content prediction result.

The invention provides a soybean growth monitoring method based on satellite remote sensing, and relates to the technical field of crop growth monitoring, and the method specifically comprises the steps: setting two monitoring nodes according to a soybean growth period, and collecting multispectral and thermal infrared remote sensing images and normal growth leaf area density and stem diameter sample data; a soybean area and a non-soybean area are distinguished by using a vegetation index threshold, cloud / shadow pixels are eliminated by using a multiband combination-threshold method, effective pixels are screened, and two types of remote sensing data are matched. Extracting leaf area density, canopy temperature and collaborative inversion stem diameter from the effective data; calculating the relative growth rate of the two types of indexes based on samples, and constructing a collaborative growth reference equation and a residual standard deviation; and substituting the growth rate of the planting area into an equation to obtain a theoretical value, calculating a deviation value, and judging the soybean growth state by combining the comparison between the deviation and a normal fluctuation interval and the positive and negative characteristics of the growth rate.

The invention discloses a basin hydrological simulation method and system embedded with a canopy interception mechanism, and the method comprises the steps: embedding a revised Gash canopy interception model module in an SWAT model, dividing a rainfall event into a wetting stage, a saturation stage and a drying stage according to the parameters of rainfall, a leaf area index and a tree height, dynamically calculating the canopy interception amount and the interception evaporation amount, and carrying out the calculation of the canopy interception amount and the interception evaporation amount. The effective rainfall capacity is used for replacing original rainfall input in the SWAT model, the adjusting effect of the forest canopy on the rainfall process is reflected more truly, and the basin hydrological simulation precision is improved. According to the method, the response and simulation capability of the SWAT model to the hydrological process of the underlying surface of the forest is effectively improved, the dynamic response to effective rainfall and evaporation terms is enhanced, the method is suitable for watershed hydrological process modeling of various forest types, and the reliability of flood forecasting and water conservation evaluation is improved.

The invention provides an orchard building method and a pruning method of a double-layer dwarf open-center-shaped high-photosynthetic-efficiency cultivation pear orchard, and belongs to the technical field of fruit tree cultivation. According to the orchard building method for the high-photosynthetic-efficiency cultivation pear orchard, multiple factors such as the sun drift angle, the day photosynthetic efficiency change around pear trees, the geographic latitude of the orchard and the growth period of pear varieties are comprehensively considered, and the planting row direction and the high-photosynthetic-efficiency tree form are determined and pruned according to the latitude of the place where the orchard is located and the day photosynthetic efficiency change around the pear trees. By means of the orchard building method, by selecting a high-light-effect tree form and a scientific pruning method, controlling the height and size of a tree crown, thinning and removing over-dense branches on the upper portion and adjusting a fruiting branch group, the ventilation and light transmission conditions in the tree crown can be effectively improved, shielding of the tree crown to lower leaves is reduced, and illumination of the lower leaves is further improved. The method is simple and easy to implement, convenient to popularize and apply, and capable of remarkably improving the pear tree photosynthesis efficiency and improving the fruit yield and quality of pears.