280 results about "Leaf area index" patented technology

The invention relates to the technical field of automatic irrigation control, in particular to a multi-sensor fusion agricultural precise irrigation system which comprises a leaf area analysis module, an environment evaluation module, a task arrangement module, an execution regulation and control module and a feedback regulation module. According to the method, the crop leaf area index is introduced to be compared with the growth stage target value, the dynamic expression of the group moisture demand is realized, the environmental evapotranspiration influence factor is generated in combination with illumination and temperature disturbance parameters, the timeliness of water demand driving force identification is enhanced, and the task execution sequence is judged by adopting regional water pressure disturbance data; a multi-region irrigation resource allocation process is optimized, a water quantity and pressure difference double-factor adjustment mechanism is utilized, the rationality and response accuracy of time length allocation are improved, water supply state analysis and task target deviation comparison are performed, dynamic supplementary irrigation regulation and control are realized, the closed-loop capability and supplementary irrigation accuracy of irrigation period control are improved, and the irrigation period control efficiency is improved. And the stable allocation capability and the irrigation response efficiency of the irrigation system under the influence of multiple variables are enhanced.

The invention relates to the technical field of crop yield analysis, in particular to a crop yield prediction method based on a time sequence remote sensing image, and the method comprises the steps: obtaining an optical remote sensing image and a synthetic aperture radar image in a crop growth period, and constructing a time sequence image through complementary information fusion and under-cloud information recovery; the problems of cloud interference and data missing are solved, a multi-scale time regulation and control convolution module is used for processing a time sequence image, multi-scale time sequence features are extracted, feature fusion is carried out in combination with attention difference jump connection, and a rice distribution map is obtained; determining a rice planting area based on the rice distribution diagram, selecting an optimal assimilation time period in the rice planting area, and assimilating the leaf area index subjected to remote sensing inversion and a crop growth model simulation result to generate a simulated yield after assimilation; based on the multi-scale time sequence characteristics, a parameter dynamic response matrix related to crop growth model parameters is established, and residual error correction is performed on assimilated simulation yield, so that the rice yield prediction precision is improved.

A rapid monitoring and identification method for winter wheat drought levels includes: 1) obtaining multispectral images and ground-measured LAI through UAV-based multi-payload low-altitude remote sensing technology, and calculating vegetation indices such as Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI), Enhanced Vegetation Index (EVI), Optimized soil adjusted vegetation index (OSAVI) and Transformed Chlorophyll Absorption Reflectance Index; 2) establishing regression equations between the calculated vegetation indices and measured LAI for different growth stages, and selecting the optimal model equation for each growth stage; 3) using the optimal model equation to invert the LAI of winter wheat at various growth stages, and calibrating the LAI thresholds for different drought stress levels; 4) acquiring the multispectral images of target plot through real-time monitoring, calculating the required vegetation indices, inverting to obtain the LAI value, and comparing it with the threshold to determine the current drought stress level.

The invention relates to the technical field of agricultural big data processing, and discloses a winter wheat yield prediction method based on XLSTM and assimilation crop growth model, comprising the following steps: data acquisition and data preprocessing; extracting a winter wheat planting area; performing leaf area index inversion; correlation analysis and feature extraction; establishing a crop growth model based on a WOFOST model; performing data assimilation based on ensemble Kalman filtering; establishing a time sequence data set; establishing a yield prediction model based on the extended long-short-term memory neural network; and model prediction: inputting the current remote sensing image and the meteorological data of the future time period into a yield prediction model, and outputting a yield prediction value of the winter wheat in the target area. According to the method, by integrating and fusing multi-source data and combining complex models and algorithms, the problem that fusion of the multi-source data and the complex relation between the multi-source data and the yield are not fully considered due to the fact that only a single feature is concerned or the models are not fully considered is solved.

The invention discloses a daily-scale corn water stress diagnosis method based on an unmanned aerial vehicle technology, and relates to the technical field of agriculture, the method is based on the definition of a water stress coefficient, introduces a Jarvis stomatal conductance model, combines canopy and plant height data obtained by an unmanned aerial vehicle, accurately estimates the aerodynamic impedance and the canopy impedance of a canopy, and improves the accuracy of the canopy stress. The actual evapotranspiration and the potential evapotranspiration are obtained through a multispectral leaf area index inversion model, then a daily scale moisture stress index is calculated, a plurality of groups of unmanned aerial vehicle thermal images are obtained according to four important growth periods of corn, and the moisture stress index is calculated and counted. And comparing the characterization capabilities of the daily scale moisture stress index and the moisture stress index in different time periods in one day on the actually measured moisture index, so as to improve the problems that the instantaneous moisture stress coefficient is insufficient in time representativeness and is easily influenced by the environment, and more accurately characterize the crop moisture stress. A monitoring effect close to a daily-scale moisture stress index is obtained by a easier method, and the practicability of the index is improved.

The invention relates to the technical field of agriculture, and discloses a soil fertility remote sensing inversion method based on machine learning. Acquiring and preprocessing a historical soil database, historical remote sensing image data and target year remote sensing image data, including radiometric calibration, atmospheric correction and geometric correction; calculating a vegetation index and a leaf area index based on the preprocessed historical remote sensing image data, and determining a bare soil window period; creating a fishing net grid covering the research area based on the spatial distribution of the bare soil window period; historical soil parameters and remote sensing image wave band reflectivity values of corresponding window periods are extracted from the fishing net grids, and training samples are constructed; the training samples are divided into a training set and a verification set, and feature selection is carried out through a correlation coefficient method; establishing a soil parameter inversion model based on the training set by adopting a multi-model cooperative training mode; and performing soil fertility inversion on the remote sensing image data of the target year by using the trained model. In conclusion, the prediction precision of the soil fertility can be improved.

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.

According to the unmanned aerial vehicle multi-source quantitative remote sensing rape growth state evaluation method, rape is divided into a seedling stage, a flowering stage and a legume stage according to growth nodes, ground hyperspectral and multi-spectral data are synchronously obtained, resolution is increased and decreased, the data are converted into all-dimensional, multi-scale and multi-temporal data, a growth state factor inversion model of the rape is established, and the rape growth state is evaluated. Evaluating the contribution of different growth state factors to the growth vigor of the rape, and evaluating the growth state of the rape based on comprehensive evaluation indexes of leaf area index, overground biomass and chlorophyll content. The method comprises the following steps: establishing a model based on multi-layer linear regression and partial least square optimization models of three types of growth factors LAI, AGB and CC, multi-mode prediction based on entropy aggregation and hierarchical analysis of entropy loading expert knowledge in combination with expert knowledge and agronomic knowledge, and setting a model with an optimal verification index in different growth periods as a growth state evaluation model in the period. The rape growth state evaluation period is short, the pertinence is strong, and the accuracy is good.

The invention discloses an urban green land carbon sink metering method and system based on multi-source data fusion, and relates to the technical field of environmental monitoring, and the method comprises the steps: collecting urban green land multi-source data, and carrying out the preprocessing; performing parameter completion on vegetation in the building shielding blind area by adopting a dual-channel generative adversarial network to obtain complete green land leaf area index distribution data; simulating a reflected light path of a building glass curtain wall by using a ray tracing algorithm, obtaining a photosynthetically active radiation correction coefficient received by a vegetation canopy, and identifying urban green land carbon sink distribution data through multi-scale data fusion; and obtaining a carbon sink amount error based on the urban green land carbon sink amount distribution data, performing dynamic correction through a Bayesian optimization algorithm, and generating an urban green land carbon sink amount measurement report. According to the method, the adversarial network is generated through two channels, and the cooperative training mechanism of the U-Net generator and the PatchGAN discriminator is utilized, so that the high-precision spatial continuity reconstruction of the leaf area index of the building shielding blind area is realized.

The invention relates to the technical field of unmanned aerial vehicle remote sensing, in particular to a crop leaf area index estimation method based on unmanned aerial vehicle image processing. The method comprises the following steps: acquiring a remote sensing image of an unmanned aerial vehicle monitoring area; identifying a topographic relief area according to the unmanned aerial vehicle monitoring area remote sensing image, and performing orthoimage reconstruction on the topographic relief area to generate a topographic relief area orthoimage; identifying a terrain shielding area based on an unmanned aerial vehicle monitoring area remote sensing image; image stitching distortion detection is carried out on the terrain occlusion area to obtain image stitching distortion data; performing spectral interpolation on the gap region based on the image splicing distortion data, and determining a plant growth stage of the gap region after the spectral interpolation; and performing lens distortion detection based on the image splicing distortion data to obtain lens distortion data. The precision and accuracy of crop leaf area index (LAI) estimation are improved based on the unmanned aerial vehicle remote sensing technology.

The invention discloses a salinization farmland crop yield prediction method, which is applied to the technical field of crop yield monitoring and prediction, and comprises the following steps: based on a vegetation index and a salinity index, inverting salinity by using a partial least square regression method; inverting the soil moisture based on an empirical relation model of the conditional vegetation temperature index and the actually measured soil moisture; inverting the leaf area index based on an empirical relation model of the normalized vegetation index and the actually measured leaf area index; the method comprises the following steps: constructing an HYDRUS-1D-EPIC coupling model considering salinity, carrying out sensitivity analysis by using an extended Fourier amplitude sensitivity test method, carrying out parameter calibration and posterior distribution inference on the coupling model by using an MCMC method, and sampling posterior distribution by using a DREAM method; and assimilating the inverted salinity, soil moisture and leaf area index to the coupling model by using an ensemble Kalman filtering method, and predicting the crop yield of the salinized farmland. According to the method, the yield prediction precision of the salinized crops is effectively improved.

The invention provides a novel method for improving the spatial resolution of the vegetation optical thickness (VOD) by using a random forest method. According to the method, dynamic and static variables related to VOD, such as optical vegetation indexes, terrain parameters, soil humidity and rainfall, are comprehensively considered, and the memory effect (lag VOD) of vegetation optical thickness is introduced. By means of the method, researchers can downscale 0.1 degree VOD data based on the multi-frequency radiometer to the VOD data with the resolution of 0.01 degree. The research shows that the accuracy of the model is remarkably improved by adding the lag VOD variable. Besides, a normalized vegetation index (NDVI), an enhanced vegetation index (EVI) and a leaf area index (LAI) are used for evaluating the downscaled 0.01-degree VOD, a result shows that the downscaled 0.01-degree VOD is highly consistent with the spatial-temporal pattern of the optical vegetation indexes, and spatial-temporal dynamic characteristics of vegetation can be better captured.

The invention discloses a grassland nitrogen content inversion method and system, and relates to the technical field of grassland vegetation quantitative remote sensing. The method comprises the following steps: acquiring measured grassland hyperspectral data and environmental parameter data of a target area, and preprocessing the measured grassland hyperspectral data; according to the preprocessed grassland actually measured hyperspectral data, extracting mapping relation characteristics of hyperspectrum-leaf area indexes in vegetation; extracting a leaf area index-nitrogen content according to the environmental parameter data; and by taking the leaf area index as connection, coupling the extracted leaf area index-nitrogen content with the hyperspectrum-leaf area index, and carrying out inversion on the nitrogen content of the grassland. Through the method provided by the invention, the dynamic change of the nitrogen in the plant growth process can be better captured, and the method has higher stability and transportability so as to quickly and effectively invert the nitrogen content of the grassland, so that the nitrogen of the plant is stably and accurately extracted, and the generalization ability of the nitrogen extraction method is improved.

The invention provides a single tree segmentation-biological parameter estimation method based on a vehicle-mounted LiDAR point cloud, and belongs to the technical field of urban landscaping intelligent monitoring. The point cloud individual tree segmentation algorithm based on tree geometric feature constraint is designed for solving the problem that individual tree segmentation is difficult due to crown overlapping in an urban scene, and the method takes a tree geometric structure as a constraint, combines point cloud reflection intensity information, a clustering algorithm, a main direction index and other means, and obtains the individual tree segmentation algorithm based on the tree geometric feature constraint. And accurate extraction of trunks and crowns in the scene point cloud is realized. Aiming at the problems of high feature redundancy, poor model interpretability and the like in an existing estimation method, a random forest model is taken as a basis, an adaptive feature selection algorithm is introduced to improve variable screening efficiency, hyper-parameters are dynamically adjusted by utilizing pigeon inspired optimization, and generalization and stability of the model are enhanced; an estimation model used for estimating biological parameters such as leaf area index, biomass and carbon reserve is designed.

The invention relates to the technical field of crop growth monitoring, in particular to a method and system for monitoring crop growth through images, and the method comprises the following steps: deploying an unmanned aerial vehicle, flying over a target paddy field, collecting image data, transmitting the image data to a ground station in real time through data transmission, and generating a preliminary image data set; and cutting and correcting the initial image data set. According to the invention, the unmanned aerial vehicle is used for image acquisition, the problem that the visual angle of a fixed ground camera is limited is avoided, and the data hysteresis is reduced through real-time data transmission, so that the monitoring is more timely. In the image data cutting and correcting process, angle deviation and background noise are eliminated, and the leaf area index calculation accuracy is improved. Through introduction of time sequence analysis, dynamic monitoring of crop growth trend is realized, growth evaluation is no longer limited to a single time point, historical data comparison is formed, and the prediction capability of the growth state is improved.

The invention provides a small-region forest carbon sink accurate estimation method and system based on multiple constraints, and the method comprises the steps: respectively generating a canopy height model, a leaf area index and a chlorophyll content through obtaining unmanned plane laser radar data and multispectral data; generating a digital elevation model by adopting a lowest point filtering technology, realizing individual tree segmentation in combination with a gradient map and a watershed algorithm, and extracting individual tree geometric parameters; and constructing a carbon sink estimation model by using the geometric parameters and the physical and chemical parameters, and comprehensively calculating the carbon sink amount of the small region. The problems that a traditional carbon sink estimation method is insufficient in precision and low in automation level are solved, the single-tree segmentation precision and the carbon sink amount calculation precision are improved through deep fusion of geometric information and spectral information, full-process automation of data acquisition, data processing and carbon sink estimation is achieved, and the method is suitable for large-scale popularization and application. The method is suitable for carbon sink monitoring requirements of various forest types and complex terrain scenes.

The invention provides an organic fertilizer utilization effect evaluation system and method based on spectral analysis, and relates to the technical field of organic fertilizer effect evaluation.The method comprises the steps that spectral images of a crop # imgabs0 # before and after organic fertilizer application are collected, and meanwhile the dry matter ratio and the photosynthetic rate of a crop # imgabs1 # before and after fertilizer application are obtained; calculating the chlorophyll content, the nitrogen content, the leaf area index, the transpiration intensity and the plant moisture content of the crop # imgabs2 # before and after fertilization according to the reflected wave data, and calculating the plant growth health index, the moisture utilization efficiency index and the photosynthetic transpiration index; according to the method, a comprehensive evaluation model is constructed, comprehensive evaluation indexes of the crop # imgabs3 # before and after fertilization are respectively generated by utilizing the comprehensive evaluation model, a difference value of the comprehensive evaluation indexes is calculated to serve as a fertilizer efficiency evaluation index, and the utilization effect of the organic fertilizer is evaluated according to the difference value, so that comprehensive monitoring and quantitative analysis of the crop growth condition are realized, and the application effect of the organic fertilizer is objectively reflected.

The invention discloses a method for predicting the irrigation amount of crops in an arid saline-alkali soil area and related equipment. The method comprises the steps that the potential evapotranspiration of a target crop is calculated through a double-source evapotranspiration mechanism model based on key meteorological parameters, obtained through prediction, of a target area, and the leaf area index and the plant height are dynamically updated according to the accumulated temperature process; respectively constructing different stress inhibition functions aiming at the attention salt ions and the pH value in the saline-alkali soil solution, and applying composite inhibition at the stomatal conductance and / or the apparent evapotranspiration resistance site to correct the potential evapotranspiration so as to obtain the ideal crop evapotranspiration corrected by the salinity and the pH value; and based on the corrected ideal crop evapotranspiration, predicting day-by-day irrigation amount and water distribution schedule suggestions in a future preset period. The method can solve the problem that current crop irrigation of saline-alkali soil in an arid inland region depends on experience, influences on different salt types and contents are caused, and quantitative analysis is lacked.

The invention belongs to the technical field of agricultural remote sensing, and provides a regional soil moisture inversion method for inverting canopy parameters, which comprises the following steps: S1, collecting actual measurement data, and dividing the actual measurement data into a training set and a test set; s2, carrying out polarization decomposition on the SAR remote sensing image to obtain polarization decomposition characteristics with mechanical property; s3, screening a polarization decomposition characteristic combination with strong correlation with the leaf area index LAI and the plant water content VWC; s4, training a canopy parameter inversion model based on the screened features, and performing inversion to obtain a regional leaf area index LAI and a regional plant water content VWC; s5, water cloud model parameter calibration; s6, using the calibrated water cloud model, coupling the regional leaf area index LAI and the regional plant water content VWC to invert the soil moisture. According to the method, the limitation of a single-point measurement and optical inversion method on the acquisition of regional crop canopy parameters is overcome, and the water cloud model input parameters are ensured to have relatively high space consistency and accuracy.

The invention discloses a vegetation coverage and management measure remote sensing inversion method combining multi-angle remote sensing data and layered vertical vegetation coverage, and the method comprises the following steps: (1), obtaining a multi-angle remote sensing data set, and carrying out the preprocessing of the multi-angle remote sensing data set; (2) selecting an optimal wave band through Borson correlation analysis and XGBoost feature screening; (3) estimating a C factor and a structured vegetation index S-Cs; (4) simulation of leaf reflectivity and transmissivity is carried out, and then vegetation canopy reflectivity is simulated; (5) converting the high spectral reflectivity of the vegetation canopy into equivalent remote sensing reflectivity of a satellite wave band, calculating vegetation indexes, and selecting the vegetation index with the highest correlation to participate in inversion and modeling of a leaf area index LAI; (6) determining an optimal angle combination and evaluating an inversion result of LAI; (7) establishing a novel quantitative coupling relation model between the multi-angle LAI and the SCs and between the SCs and the C factor, and then inverting the C factor; the invention provides a technical means with high practicability for monitoring the dynamic change and expansion process of soil erosion.

The invention provides a field-level rice yield remote sensing estimation method based on high-resolution total primary productivity and a dynamic harvest index, and the method comprises the following steps: firstly, obtaining a rice growth season time sequence Sentinel-2 image based on a remote sensing cloud platform, calculating a leaf area index, and obtaining meteorological data EAR5 products and CO2 data; then, a self-service sampling method and a multi-model process sensitivity analysis method are used for analyzing the influence rule of the rice growth season environment factors on the GPP, an improved double-leaf light energy utilization rate model is constructed, and high-resolution rice GPP high-precision estimation is achieved. And finally, forming an HIDYM method in combination with the dynamic harvest index to realize the yield estimation of the field-level rice in Jiangsu province for many years. The method can be used for quickly and accurately estimating the yield of the large-range field-level rice.

The invention discloses an urban environment monitoring regulation and control system and method based on global greening data. The system comprises an image acquisition module, a monitoring module, an image analysis module, a sampling screening module, a global speculation module and a strategy generation module. The image acquisition module periodically acquires greening image data; the monitoring module collects vegetation monitoring data through a sensor group; the image analysis module extracts vegetation coverage areas and classifies the areas according to texture feature parameters (leaf area index, canopy height and texture uniformity); the sampling and screening module calculates a standard value of each unit area and screens and optimizes sampling areas; the global speculation module is combined with the sampling data to calculate monitoring data of an uncovered area; and the strategy generation module generates a targeted microclimate regulation and control strategy according to the monitoring data and the classification result. According to the invention, image analysis and sensor data are combined, precise monitoring and intelligent regulation and control of vegetation distribution are realized, and the ecological function and microclimate regulation and control effect of an urban greening area are improved.

The invention discloses an image restoration method for soybean leaf phenotype analysis. The method comprises the following steps: firstly, complementing missing edge information of soybean leaves through an edge generation network; then, extracting a coarse-to-fine layered feature map from the complemented edge information by using a structural auto-encoder, and inputting the layered feature map as a guide feature into an image restoration network to assist a restoration process; the soybean leaf structure information is accurately repaired based on the guiding characteristics; and finally, according to the completely repaired leaf image, obtaining phenotypic parameters such as a leaf area index, a structure texture and a color of the soybean plant. By means of the mode, gradient structured edge information and hierarchical feature guidance are introduced, the repairing effect of the soybean leaf picture can be improved, and therefore support is better provided for accurate and complete analysis of soybean leaf phenotype data.

The invention discloses a carbon sink calculation method based on a space-time fusion technology and flux data, and the method comprises the steps: carrying out the space-time fusion synthesis of a remote sensing image, calculating a leaf area index and chlorophyll concentration data with high space-time resolution, and improving the time and space resolution of carbon sink data; calculating daily net primary productivity data by using a light energy utilization rate model based on the leaf area index data and the meteorological data; correcting parameters of the light energy utilization rate model based on the daily net primary productivity data and the daily flux data; migrating the corrected parameters of the light energy utilization rate model to a flux-free site area based on the ratio of the chlorophyll concentrations to obtain the migrated parameters of the light energy utilization rate model; the migrated parameters of the light energy utilization rate model are transmitted back to the light energy utilization rate model, the net primary productivity is calculated, and the carbon sink data are calculated based on the net primary productivity, so that the carbon sink calculation method is higher in applicability, and reliable technical support is provided for carbon cycle research of an ecological system.

The invention discloses an alpine meadow aboveground biomass monitoring method based on PROSAIL-BP, and belongs to the field of ecological remote sensing information processing. In order to overcome the defect that samples in the alpine region are insufficient and have high precision and high reliability, the method comprises the steps that an alpine meadow mask, remote sensing images and field biomass data in a target region are obtained, the remote sensing images are spliced, the wave band reflectivity is normalized, and the grassland region reflectivity is reserved in combination with mask cutting; predefining a PROSAIL model matched with the region features; performing Sobol global sensitivity analysis to obtain a first-order sensitivity index and a total-order sensitivity index of the parameter; screening a key wave band and four high-sensitivity parameters; uniformly sampling to generate a parameter group, simulating a hyperspectrum through PROSAIL, extracting the reflectivity of a key wave band, and constructing a data set by multiplying a leaf area index by a dry matter content as a target variable; and training a three-layer BP neural network, and processing the reflectivity output pixel biomass of the remote sensing key wave band. The method is applied to a remote sensing information processing system and has high precision and high reliability.

The invention relates to the technical field of information management, in particular to a smart agriculture precision big data information management system, which comprises a growth index extraction module, a stage data clustering module, a nutrient fluctuation identification module, an agricultural material strategy generation module and a scheduling information scheduling module. According to the method, the acceleration criterion is constructed based on the continuous period leaf area index change sequence, transition node identification is completed in combination with the photosynthetically active radiation utilization rate and the nitrogen absorption rate change trend, accurate positioning of crop growth stage change time points is achieved, the stability of stage classification in a space region is enhanced, and the accuracy of crop growth stage classification is improved. The agricultural material putting grade is judged and the fertilization frequency and dosage are blended by analyzing a root zone soil nutrient concentration difference value and a rate fluctuation frequency detection mode in combination with moisture content and root activity conditions, and operation period adjustment is guided in a task schedule in cooperation with a meteorological element fluctuation rate. The precision of farmland crop stage identification and the timeliness of nutrient fluctuation response are integrally improved.

The invention discloses a ground vegetation leaf area index remote sensing inversion method based on a remote sensing image. The method comprises the following steps: data acquisition and preprocessing; performing multi-scale space-time non-local filtering fusion; dynamic feature extraction; carrying out transfer learning fine tuning; time sequence deep learning integration; and model output and post-processing. According to the invention, through multi-source space-time fusion and dynamic feature distribution, vegetation LAI inversion with high resolution and high continuity is realized; the transfer learning and the time sequence deep network enhance the adaptability of the model to a new region and time sequence change; the stability and reliability of large-scale application are guaranteed through full-process automation and uncertainty evaluation, and the model is obviously superior to an existing single-source or static model.

The invention discloses a refined grain crop yield prediction method and system, belongs to the technical field of agricultural yield prediction, and solves the problems that the prediction precision is low and day-by-day prediction and overfitting cannot be realized when the crop yield of a small plot scale is predicted. The method comprises the following steps: obtaining food crop data, wherein the food crop data comprises meteorological data and crop emergence period data; dividing the prediction area into a plurality of parameter adjustment areas, and establishing a simulation algorithm based on a WOFOST crop mechanism model for each parameter adjustment area; performing day-by-day simulation by using a WOFOST-based crop mechanism model simulation algorithm to obtain a crop development process, a leaf weight, a stem weight, an ear weight, an overground part dry matter weight and a leaf area index; and taking the output data based on the WOFOST crop mechanism model simulation algorithm, the meteorological elements, the statistical yield data, the field test data and the agrometeorological observation data as the input of an LSTM algorithm, and obtaining a prediction result of the yield of the food crops. The method is suitable for real-time and refined prediction of the crop yield.

The invention provides a spraying parameter acquisition method based on plant canopy density and a spraying machine, and relates to the technical field of agricultural plant protection, and the method comprises the steps: determining the point cloud number of a to-be-sprayed plant in a plurality of canopy regions according to the canopy point cloud data of the to-be-sprayed plant; determining a leaf area index of each canopy region of the to-be-sprayed plant according to the number of point clouds of the to-be-sprayed plant in the plurality of canopy regions; determining the canopy density of each canopy region of the to-be-sprayed plant according to the leaf area index of each canopy region of the to-be-sprayed plant; inputting the canopy density of each canopy area of the to-be-sprayed plant into the constructed correlation model to obtain a spraying parameter corresponding to each canopy area of the to-be-sprayed plant; the association model is used for representing an association relationship between the canopy density and the spraying parameters. According to the method and the device, the spraying parameters corresponding to each canopy region are accurately obtained, so that the spraying accuracy and efficiency are improved.

The invention provides a sunlight-induced chlorophyll fluorescence reconstruction method and system based on satellite observation data. Comprising the following steps: collecting and processing ground site observation data, air temperature data, leaf area index data, MODIS bidirectional reflectance distribution function nadir correction reflectivity data, enhanced vegetation index data, normalized difference moisture index data, global land cover classification data, and OCO-2 and TROPOMI satellite observation SIF data; performing data reconstruction by using the OCO-2 SIF and the TROPOMI SIF as original data respectively by using a machine learning method; then, an empirical formula between the two kinds of reconstructed SIF data and the site observation SIF is constructed, and estimation parameters on the site are obtained through optimization; then, calculating estimation parameters of the empirical formula on each land coverage type; and finally, substituting the two types of reconstructed SIF data into an empirical formula to obtain a reconstructed SIF with site SIF information.