62 results about "Plant Distributions" patented technology

The present invention discloses a method for monitoring and early warning of wild plant distribution status based on image recognition, relating to the technical field of monitoring and early warning of wild plant distribution status. This method promotes the scientific assessment of ecological protection status of each sub-area by calculating coverage indexes Fgl and population complexity indexes When one of the coverage indexes or the population complexity indexes of a sub-area does not reach a preset threshold, an unqualified mark is marked accordingly. By calculating environmental pressure indexes HJyl, a first early warning instruction is automatically sent when one of the environmental pressure indexes HJyl exceeds a preset first pressure threshold, indicating that the environmental pollution status is unqualified.

The invention belongs to the technical field of remote sensing, and discloses a wolfberry planting distribution high-precision remote sensing monitoring method based on multi-temporal fusion and spectral feature optimization. According to the method, a multi-temporal and multi-spectral satellite image is used as a data source, and preprocessing and multi-temporal image fusion are firstly carried out; the method comprises the following core steps: establishing a time sequence characteristic curve according to a unique phenological period (such as bare soil characteristics in a dormancy period and high vegetation coverage in a rapid growth period) of wolfberry; in a spectral domain, screening out a characteristic spectrum dimension combination with the highest discrimination degree between the wolfberry and other crops through a characteristic wave band optimization algorithm (such as vegetation index difference degree and red edge characteristics); and in combination with an object-oriented classification or deep learning classification model, constructing a space-time coupling classifier, and performing high-precision extraction and distribution mapping on the Chinese wolfberry planting region. The method can effectively solve the problem of confusion classification of Chinese wolfberry and similar ground features (such as other shrubs and orchards), and realizes rapid and accurate monitoring of Chinese wolfberry planting area and spatial distribution.

The invention provides an early warning method and system for greenhouse vegetable diseases and insect pests, and relates to the technical field of agricultural informationization. Environmental parameters and vegetable images are collected, a plant distribution network is established, and the propagation influence degree is calculated; analyzing an environmental suitability index of a pest propagation mechanism based on the environmental parameters; extracting disease features by using multi-source image fusion to determine an initial focus; calculating the propagation probability by combining the propagation influence degree and the environmental suitability degree, and constructing a dynamic propagation path prediction map; determining a prevention and control priority and generating partition early warning information; finally, relevant parameters are optimized according to the actual prevention and control effect, and accurate and timely disease and pest early warning is achieved.

The invention discloses an ecological restoration method for salinized land in a semi-arid region based on a natural solution. The ecological restoration method comprises the following steps: S1, defining a plurality of ecological restoration regions; s2, planting plants; in each ecological restoration area, high-layer plants are planted on a circle of the edge of the ecological restoration area, and a circle of middle-layer plants and a circle of low-layer plants are sequentially planted into the ecological restoration area; s3, arranging an ecological restoration area; according to the invention, an ecological restoration area with multi-layer plant distribution can be formed, the restoration and improvement of biodiversity are facilitated, and through the arrangement of the first groove, the sand barrier and the spacing between the first groove and the sand barrier, the purpose that seeds of plants in each layer stay in or near the first groove along with wind to achieve sowing and growth can be achieved in the subsequent year; multiple layers of plants along the first grooves are formed, the ecological restoration effect is enhanced, subsequent sowing through wind power is facilitated, and manual close planting is not needed any more.

The invention relates to the technical field of image processing, in particular to an unmanned aerial vehicle image processing and target recognition system and method based on deep learning, and the method specifically comprises the steps: collecting greenhouse structure parameters, plant distribution density and unmanned aerial vehicle flight data, building a plant-airflow coupling equation, and outputting an image effective window period; the humidity of the greenhouse is monitored in real time, a humidity-reflection correlation compensation model is established for deformation compensation, and a distortion correction image after reflection suppression is output; based on the distortion correction image, through GAN restoration under topological constraint, processing a plant-shielded area in the image, and outputting a target object candidate image after the shielded area is restored; the target object state is evaluated, and quantitative indexes are provided for work task decision making. According to the invention, the problem of low dynamic target object recognition accuracy in the prior art is solved.

The invention provides a crop planting distribution identification method based on deep learning and a fusion positioning technology. The method comprises the steps of 1, obtaining a camera image, a three-dimensional point cloud and a geographic coordinate of a target area through an acquisition device; step 2, identifying crops in the camera image through a deep learning algorithm, identifying the boundary of each crop planting area at the same time, and outputting crop classification vector pattern spots; step 3, projecting radar point cloud data to a camera image by adopting a PCL, fusing the camera image and the three-dimensional point cloud to obtain an accurate coordinate of each pixel point in the camera image relative to the surveying and mapping point, and outputting a pixel coordinate matrix; and 4, calculating the area of each crop distribution region according to the coordinates of the pixel points on the boundary of each distribution region in the crop classification vector pattern spots, summarizing the same crops, and finally counting the planting area and the distribution condition of each crop.

The invention discloses a peanut planting distribution extraction method and system based on phenological characteristics, and belongs to the technical field of crop phenotype acquisition, and the method comprises the steps: generating a time sequence image set according to the obtained Sentinel-2 image data in a research region; establishing phenological spectral index characteristics for representing crop density and yellowness, adding all characteristic variables to each time sequence image in the time sequence image set to obtain a characteristic variable time sequence image set, obtaining to-be-divided sample data containing characteristic variable information, and performing division and crop type marking on sample points of the to-be-divided sample data to obtain a to-be-divided sample image set; taking image sample data sets of other crops except rice, corn, peanut and soybean as a training set, and training a classifier; and obtaining a feature variable time sequence image set of the to-be-extracted peanut planting area, classifying the feature variable time sequence image set through the classifier, and obtaining the distribution condition of the peanut samples in the research area. According to the method, the precision of peanut planting distribution condition extraction can be improved.

The invention belongs to the technical field of crop growth monitoring and agricultural economy, and particularly relates to a crop production risk assessment method and system based on MODIS time series data and machine learning, and the method comprises the following steps: S1, obtaining MODIS time series data, crop planting distribution data, county-level crop per unit yield statistical data and an administrative division base map, s2, performing mask processing on the MODIS time series data and the crop planting distribution data to obtain an 8-day time series data set corresponding to crops, S3, calculating a per unit yield fluctuation rate, adopting a non-parametric weighted local linear regression method to eliminate a crop per unit yield trend to obtain per unit yield fluctuation data, and then calculating per unit yield fluctuation rates of county-divided crops, S4, constructing a model, and S5, constructing a model. The method includes the steps of S1, establishing a machine learning regression model based on the GPP data set and the LST data set in the step S2 and the unit yield fluctuation rate data in the step S3, S5, crop production risk assessment, and generating a grid-level crop unit yield loss result by utilizing the optimal model in the step S4 and combining an MODIS time sequence grid data set.

The invention belongs to the technical field of three-dimensional measurement data processing, and particularly relates to a corn seedling stage community plant distribution and characteristic parameter extraction method based on point clouds, which comprises the following steps: segmenting a field corn seedling plant community point cloud to obtain a point cloud cluster of a stem part; clustering the projection points of the point cloud cluster of the stalk part on the XY plane based on DBSCAN; adopting a DBSCAN clustering result to initialize K-means clustering, and obtaining a plant distribution diagram; on the basis of the plant distribution diagram, data information between rows and columns is calculated through a threshold value method, and the data information between rows and columns comprises the number of plants, the number of rows and columns and data between rows and columns; based on the plant distribution diagram and the data information between rows and columns, the positions of seedling missing points are judged through a threshold value method; and calculating the plant height based on the distribution map and the dynamic fluctuation of the ground points. The functions of precise detection, plant counting, row-plant spacing measurement and missing seedling detection of densely distributed seedling-stage corn plants are realized, and particularly, the extraction of point cloud data of serious leaf staggering under the condition of dense planting is realized.

The application discloses a kind of quick assessment system and method of the influence of flood disaster on wetland ecosystem, and the system includes, animal image acquisition module, for obtaining animal image data;Animal behavior analysis module, for identifying animal behavior based on animal image data, to obtain animal behavior distribution map and animal behavior timetable;Plant remote sensing module, for obtaining wetland plant distribution map based on wetland remote sensing data;Flood data acquisition module, for obtaining water environment parameters when flood disaster occurs;Influence index evaluation module, for evaluating model to determine ecosystem influence index.The application integrates animal image acquisition and analysis, plant remote sensing identification, real-time acquisition of water environment parameters and other multi-source data, and constructs a unified influence index evaluation model, can quickly generate the comprehensive influence index of wetland animals and plants after flood disaster occurs, so that the assessment of disaster influence is timely and effective.

The present invention discloses a method for optimizing the configuration of an ecological barrier forest and grass belt for plant invasion prevention and control in plateau mountains, and relates to the technical field of ecological barrier construction. The method comprises: collecting basic ecological data of a target research area, generating an ecological thematic map through preprocessing and visual annotation; investigating and counting the distribution data of invasive plants in the target area, and mapping it onto the ecological thematic map to generate an invasive plant distribution thematic map; determining the invasion source and invasion path through spatial analysis and diffusion path analysis; evaluating the level of the protected area based on the invasion distribution data, superimposing the invasion path and risk level information, and determining the scope of the ecological barrier; constructing an ecological barrier forest and grass belt system, outputting a configuration plan, and implementing plant invasion prevention and control. The method solves the technical problem that the existing technology is difficult to accurately prevent and control invasive plants in a complex ecological environment due to insufficient comprehensiveness, and achieves the technical effect of optimizing the prevention and control effect of invasive plants in the target research area.

The present invention belongs to the field of forestry technology, and specifically relates to a seedling raising device. Through the setting of a cultivation mechanism, more seedlings can be cultivated synchronously in a box at one time, thereby improving the efficiency and effect of seedling cultivation; the cooperation of the placement component and the adjustment component can adaptively adjust the planting distribution of the seedlings at different growth stages to meet the growth needs of the seedlings at different stages; the setting of the seedling removal component can quickly and conveniently remove the seedlings after the cultivation is completed, which is more convenient for planting needs.

The invention relates to the field of feed processing, in particular to extraction equipment for active ingredients of a plant extract additive in veterinary feed processing. Comprising an extraction tank and a filter cylinder arranged in the extraction tank and used for storing plants, an extrusion mechanism used for pressing the plants to separate an extracting solution is arranged on the extraction tank, the extrusion mechanism comprises a pressing plate capable of moving in the axis direction of the filter cylinder and a pressure adjusting assembly arranged at the bottom of the pressing plate, and the pressure adjusting assembly is arranged at the bottom of the pressing plate in the working state of the pressing plate. And the pressure adjusting assembly is started to uniformly distribute the plants in the filter cartridge. The filter cartridge is vibrated through the vibration generator to disperse plants in advance, then in the pressing process of the pressing plate, the pressure borne by the fan-shaped plate is detected through the pressure sensing layer, and the fan-shaped plate is driven through the pressure driver to further disperse the plants according to the pressure conditions of different areas; after the plants are uniformly dispersed, the plants are continuously extruded by a complete extrusion surface through the horizontal postures of all the fan-shaped plates, so that uniform pressure is ensured, active ingredients are released to the maximum extent, and the extraction efficiency is improved.

The invention relates to the technical field of agricultural machinery, in particular to a miss-planting detection and complementary planting seedling system and method for a vegetable transplanter, and the system comprises a region sensing array, an edge calculation decision unit, a miss-planting positioning module, an intelligent complementary planting mechanism, and a farmland digital monitoring platform. Wherein the area sensing array is used for constructing a data acquisition network and acquiring seedling states and soil parameters of a transplanting area in real time; the edge calculation decision-making unit is used for processing transplanting area data, identifying a seedling planting missing state through a target detection algorithm and generating a complementary planting instruction; the missing planting positioning module is used for locking the missing planting position in the vegetable transplanting process; the intelligent complementary planting mechanism is used for driving a mechanical arm to perform complementary planting of seedlings according to the complementary planting instruction; the farmland digital monitoring platform is used for monitoring the farmland environment and the crop growth state in real time and displaying the missing planting distribution, the complementary planting progress and the equipment operation state. Therefore, the problems of poor detection signal, single detection algorithm, low seedling survival rate and the like in the prior art are solved.

Embodiments of the present application provide a crop planting distribution plot identification method, device and equipment, wherein the method comprises: acquiring SAR time series data, high-resolution remote sensing image data and time series optical data corresponding to a measured area; determining a suspected crop planting area in the measured area based on the SAR time series data and in combination with the phenological characteristics of different crop planting sub-regions corresponding to the measured area; determining an agricultural planting plot from the measured area based on the high-resolution remote sensing image data; performing superimposition processing on a distribution map of the suspected crop planting area and a distribution map of the agricultural planting plot to obtain a suspected crop planting plot distribution map; and determining a crop planting distribution plot distribution map based on the suspected crop planting plot distribution map and the time series optical data.

This invention discloses a method and apparatus for fine extraction of aquatic vegetation and landscape connectivity assessment in rivers and lakes based on high-resolution remote sensing, belonging to the field of environmental remote sensing monitoring and water ecology assessment technology. It includes: acquiring and preprocessing high-resolution satellite imagery to obtain a standardized remote sensing image dataset; extracting water body masks and aquatic vegetation coverage areas through hierarchical threshold segmentation; constructing a dual-threshold discrimination rule based on the normalized vegetation index and soil-regulated vegetation index to finely divide the distribution areas of floating and submerged plants; constructing a binary raster map of vegetation distribution based on the distribution areas; combining a preset connectivity distance threshold; calculating the landscape connectivity index of vegetation patches based on graph theory; determining the ratio of equivalent connected area to effective connected area; and completing a comprehensive assessment of the ecological effectiveness of the aquatic vegetation community. This invention is mainly used for remote sensing monitoring of aquatic vegetation in urban rivers and lakes and for assessing the effectiveness of water ecology restoration, effectively mitigating spectral interference from complex water body sediment backgrounds.

The invention provides a southern irrigation area fruit tree irrigation method based on space-air-ground integrated monitoring. The southern irrigation area fruit tree irrigation method comprises the steps that space-air-ground facility equipment is used for collecting data of the surface temperature, humidity, fruit tree transpiration intensity and soil moisture content of an irrigation area; constructing a time sequence fusion data set for the space-air-ground multi-source data, and generating a high-precision soil moisture content distribution map; day-by-day weather and fruit tree growth data are obtained, and subarea division is carried out on the fruit tree park according to soil texture and fruit tree plant distribution; constructing a fruit tree water demand model, constructing an LSTM rainfall forecasting model, and constructing an irrigation amount forecasting model based on a water amount balance principle; a decision-making system of feedforward prediction and feedback decision-making is constructed, and an irrigation decision-making instruction is issued to a water taking valve attached to the high-level water tank for irrigation by means of an internet-of-things platform; analyzing the water stress condition of fruit tree canopies according to an unmanned aerial vehicle thermal infrared imaging technology, and optimizing a fruit tree water demand model; and establishing an irrigation expert knowledge base, and iteratively updating an irrigation strategy.

The application is suitable for the field of agricultural monitoring technology, and particularly relates to a corn whole life cycle monitoring method and system based on air-ground cooperation, which comprises the following steps: outputting inferred stalk diameter and top plane coordinates through a UAV; obtaining measured stalk diameter and base plane coordinates through a ground robot; constructing a first plant distribution matrix and a second plant distribution matrix; matching the first plant distribution matrix with the second plant distribution matrix; and according to the matching result, associating the top monitoring data collected by the UAV with the side monitoring data collected by the ground robot to the same corn plant. Through the matching based on the relative position relationship between the plant stalk diameter and the group, the application overcomes the data misplacement problem caused by the positioning drift of the UAV and the ground robot, does not need to rely on expensive high-precision positioning equipment, provides a reliable data basis for planting decisions such as variable rate fertilization and precision pesticide spraying, and improves the accuracy of corn precision management.

The application discloses a natural solution-based ecological restoration method for salinized land in a semi-arid area, which comprises the following steps: S1, demarcating a plurality of ecological restoration areas; S2, planting plants; in each ecological restoration area, high-level plants are planted in a circle at the edge of the ecological restoration area, and medium-level plants and low-level plants are sequentially planted in circles towards the inside of the ecological restoration area; and S3, setting the ecological restoration area; the application can form an ecological restoration area with multi-level plant distribution, which is helpful to restore and improve biodiversity; through the setting of the first groove, the sand barrier and the spacing therebetween, the seeds of plants at various levels can be deposited in the first groove or nearby along with the wind in subsequent years to achieve the purpose of sowing and growth, multi-level plants along the first groove are formed, the ecological restoration effect is strengthened, subsequent sowing by using wind power is facilitated, and manual dense planting is not needed.

The invention relates to the field of agricultural robots, and discloses an autonomous navigation picking and sorting method and system for a blueberry picking and sorting robot, and the method comprises the steps: obtaining the plant distribution and space structure through an environment sensing module, generating a density self-adaptive path and a resource pre-distribution strategy through a global task planner, and carrying out the self-navigation picking and sorting of a blueberry picking and sorting robot; the picking module and the sorting module cooperatively work according to a strategy, the material caching and buffering module dynamically adjusts the rates of the two ends according to a load threshold value, and multi-mode feedback data drives a double-closed-loop PID algorithm to achieve millisecond rate synchronization. The system integrates a laser radar, stereoscopic vision and a near-infrared sensor, is equipped with a flexible clamping mechanical arm, a multi-stage screen sorting mechanism and a conical container cache module, and supports abnormal self-correction and multi-machine cooperative load redistribution. Through a dynamic coupling control framework, the working efficiency is improved, the fruit damage rate is reduced, and the system is guaranteed to continuously and stably run for more than 8 hours.

The invention discloses a mining method for development potential of a photovoltaic zero-carbon park. The mining method comprises the following steps: step 1, acquiring a satellite map of a target area, load data of the park and solar radiation intensity of the target area; 2, calculating the carbon emission of the park according to the characteristics of the park, the load data of different building types and the load data of the park; 3, according to the characteristics of the park and different building types, photovoltaic energy installation area calculation and green plant emission reduction calculation are carried out; calculating carbon reduction emission of the park by combining solar radiation intensity and green plant distribution of the park; and 4, carrying out carbon emission and carbon reduction potential matching evaluation, calculating a development potential index Q = carbon reduction / carbon emission of the zero-carbon park by combining the carbon emission and the carbon reduction potential, and evaluating whether the park has the development potential of the zero-carbon park according to the development potential index Q. According to the method, zero-carbon park development potential evaluation is combined with carbon emission and carbon emission reduction, and guidance opinions are provided for zero-carbon park development in the region.

A method for classifying disturbing plants using drone orthophotos is disclosed, and the method for classifying disturbing plants using drone orthophotos may include: (a) forming a drone orthophoto in which a target area is shown during a preset period; and (b) determining a candidate object of a disturbing plant from the drone orthophoto as the disturbing plant.

The invention discloses a crop harvesting progress monitoring method and system, electronic equipment and a storage medium, and relates to the technical field of crop harvesting progress monitoring, and the method comprises the steps: obtaining a target region satellite image and the same-period agricultural machine track data; processing the crop planting distribution data to generate a mask pattern, and processing an image; carrying out cloud detection; generating a clustering result graph based on the cloud and the crop mask; agricultural machine track points are mapped to a clustering graph, the harvesting state is judged, and a classification result graph layer is generated; and counting the harvested and total planting areas based on the image layer and the crop mask, and calculating the progress percentage. The method introduces the same-period agricultural machine trajectory data to participate in determination, reduces the dependence on an artificial sample and a supervision model, and improves the generalization ability. In combination with multi-source data processing, timeliness is enhanced, day-by-day updating and automatic monitoring are supported, and a reliable basis is provided for agricultural management.

The invention discloses a model for exploring the influence of an environment on plant distribution and application. The plant refers to gentiana dahurica fisch. Based on geographical distribution of gentiana dahurica fisch and ecological factors influencing growth of gentiana dahurica fisch, which are determined by literatures and field investigation, an ecological suitable area of gentiana dahurica fisch is modeled by adopting an enhanced regression tree (BRT). And establishing a relationship between the content of each active component in the Inner Mongolia plant and an ecological factor by adopting a linear regression method. The invention discloses a model for exploring the influence of the environment on the distribution of gentiana dahurica fisch, and discusses the influence of the environment on the growth of gentiana dahurica fisch and the content of active ingredients. The model provides guidance for coordinated development of medicinal plant science.

The invention relates to a method for predicting influence of climate change on plant distribution based on an artificial intelligence algorithm, and belongs to the technical field of plant distribution prediction. Global plant distribution data is obtained from GBIF, cleaned and then subjected to spatial matching with WWF ecological region vector data, species and ecological region IDs are associated, woody and herbaceous plants are distinguished, species names are standardized, the number of species in each ecological region is counted, and the ratio of woody and herbaceous plants is calculated and visualized; according to the method, current and future climate data are fused, a climate characteristic median of each ecological region is extracted, a species-climate relationship model is constructed by using a traditional linear regression and artificial intelligence algorithm, the influence of temperature, rainfall, human footprint and temperature seasonality is analyzed, and finally the distribution pattern of two types of plants in a future climate scene is predicted. Through multi-scale data coupling and nonlinear modeling means, a scientific basis is provided for biodiversity protection and adaptive management under a global change background.

The invention relates to the technical field of smart agriculture, and provides an unmanned aerial vehicle hyperspectral technology-based pecan tree species classification method, which comprises the following steps of: obtaining a hyperspectral image, and performing image cutting on the hyperspectral image to obtain a plurality of block images with specified sizes; analyzing a single hickory tree variety accurately, performing first feature extraction on the block image to obtain first feature information corresponding to the block image, and performing second feature extraction on the spatial feature to obtain second feature information corresponding to the spatial feature; feature information corresponding to the target is accurately collected through two times of feature extraction of different dimensions, the block images are classified based on the first feature information and the second feature information, classification information corresponding to the block images is obtained, and the classification information comprises pecan tree species information; based on the block images and the classification information, the pecan tree species classification image is obtained, an accurate pecan tree species classification result can be obtained, planting distribution of pecan tree species in the forest land is analyzed, and sustainable development of the forest land is promoted.

The present invention discloses a method and device for calculating the planting area of Torreya grandis according to age characteristics. According to the different physiological characteristics of Torreya grandis at different ages, the whole Torreya grandis is divided into two categories: adult forest and juvenile forest. After extraction and merging respectively, the overall distribution of Torreya grandis is obtained. For the adult forest, a sub-meter high-resolution image is used as the base map for annotation, and a deep learning image recognition framework is used for recognition. For the juvenile forest, Sentinel multi-spectral data combined with vegetation coverage is used as the basic data, and the random forest method is used to extract young forests, and the misclassified fields are removed by using the characteristic that the peak vegetation index of the fields is extremely high throughout the year. Finally, the planting patches of Torreya grandis are merged, and the number of pixel points is counted under the Albers equal-area projection. Combining the spatial resolution information and slope information, the planting area of Torreya grandis is obtained, and the planting area of Torreya grandis close to the statistical data rather than the projected area is obtained, which is of great significance for studying the planting distribution of Torreya grandis and guiding the production of Torreya grandis.

The utility model discloses a soybean planting hand -operated formula gyro wheel seed sowing machine belongs to soybean planting technical field, and its technical scheme main points include the main shaft, the middle part on the surface of main shaft is connected with the moving wheel through bearing rotation, the both ends of main shaft all are provided with seeding assembly, the back of seeding assembly is provided with the earthing component, through setting seeding assembly, seeding assembly adopts the design of the cooperation seed groove of carousel, can ensure every time seed falling is single or preset quantity, effectively avoids the phenomenon of missing sowing, and the phenomenon of sowing again, and precision control sowing interval and density make soybean plant distribution even, improve the uniformity of emergence rate and crop growth, through setting earthing component, earthing component can realize the flexible adjustment of earthing plate height, can accurate matching soybean seed's best earthing depth requirement, ensure that seed obtains suitable covering thickness, significantly promotes seed emergence rate and seedling survival rate.

The invention discloses a soil heavy metal pollution detection method and system based on a remote sensing image, and relates to the technical field of heavy metal pollution detection.The method comprises the steps that spectral data of current soil are obtained through the remote sensing image technology, and soil information and plant information during historical detection are obtained from a database; analyzing the relation between the soil detection spectrum data and the actual spectrum data and plant distribution and the relation between the content of each heavy metal and the effective content of each heavy metal, obtaining the effective content of each heavy metal in the current soil, and obtaining the threshold value of the effective content of each heavy metal in the soil from the database; meanwhile, the heavy metal content threshold values of various plants under different soil parameter characteristics and purposes are analyzed, whether the current soil is polluted by heavy metal or not is judged, early warning is conducted when the soil is polluted by heavy metal, and the effectiveness of soil heavy metal pollution monitoring is guaranteed; and the accuracy of heavy metal content monitoring and soil heavy metal pollution analysis is ensured.

The invention provides a near-natural plant community construction method of an engineering slope. The method comprises the steps that the annual precipitation of an area where a to-be-planted land is located and different types of slope parameters are obtained, and the effective precipitation of each type of slope is calculated; taking the effective precipitation of each type of slope as a condition, establishing a region library according to a moisture condition region similar to the to-be-built planting land, and screening and determining at least one typical region; investigating and analyzing plant distribution communities of various types of slopes in the typical area, and determining colonization species and associated species of the plant distribution communities according to importance and availability of each species; and respectively calculating slope surface wettability indexes of the to-be-planted land and various types of slopes in the typical area, and evaluating the suitability of the colonies and the associated species to the to-be-planted land according to the slope surface wettability indexes so as to construct a near-natural plant community of the engineering slope. According to the scheme, reasonable construction of the slope natural plant community of the to-be-built planting land can be realized.