342 results about "Leaf area index" patented technology

The invention discloses an object-oriented remote sensing inversion method of a leaf area index of a crop, comprising the following steps of: acquiring multispectral remote sensing data; calculating a biomass spectral index NDVI (Normalized Difference Vegetation Index), a crop nutrient spectral index BRI and a water sensitive spectral index NDWI (Normalized Difference Water Index) of a crop colony by utilizing the acquired multispectral remote sensing data; carrying out object-oriented segmentation and encoding according to the biomass spectral index NDVI, the crop nutrient spectral index BRI and the water sensitive spectral index NDWI of the crop colony by utilizing a mean shift algorithm; sequentially carrying out the original spectral mean calculation of pixels on objects according to an encoding sequence to obtain a spectral index SAVI (Soil-Adjusted Vegetation Index) sensitive to the LAI (Leaf Area Index), and carrying out texture structure calculation; building a regression model of ground LAI observation data, the spectral index SAVI sensitive to the LAI and the texture structure calculation; and carrying out inversion calculation on the object without the ground LAI observation data by utilizing the regression model to obtain the LAI of the object without the ground LAI observation data.

The invention relates to a lossless testing device for nitrogen nutritive index of crop leaves based on canopy reflection spectrum. It belongs to technology field of crop production, which is specially used in online detecting and precious fertilizing guidance of crop field production. It is mainly composed of bracket, spectrum signal collection and host computer. Spectrum signal collection adopts design of 4 waveband and 8 channels. Hardware system is composed of analog signals conditioning module, A/D transfer module, chip module, memory module, display module, keyboard module, communication module and power management module. Software system is programmed by C51 chip language. Detection of nitrogen content, nitrogen accumulation and leaf area index of canopy not only ensures high detecting precision, but also provides three data indexes of different facets to analyze nitrogen nutritive level of crop, which guarantees reliability and stability of detecting results. It can be applied in field operation, online lossless detecting, field online manage and guidance for agricultural production personnel.

The invention provides a method for obtaining a leaf area index based on quantitative fusion and inversion of multi-angle remote sensing data and multi-spectral remote sensing data, which is characterized in that a coefficient of a bidirectional reflectance distribution function (BRDF) of a vegetation type of the best matching pixel level of the multi-angle remote sensing data and a surface reflectivity is adopted, a surface soil reflectivity profile is obtained based on best matching of the multi-spectral data, and a canopy radiation transmission model is driven to obtain the leaf area index with high accuracy and large-scope coverage based on the multi-spectral data. The invention has the advantages that: the ranges of wave bands of the multi-angle data and the multi-spectral data need not to be overlapped, and approximate treatment can be carried out by adopting the similarity of the bidirectional function of the available wave bands; the coefficient of the bidirectional reflectance function and the best matching vegetation type obtained based on the multi-angle data is relatively stable along with changes in the time and the space, time sequence data can be made into a background library to be used as input for inversion of the multi-spectral data, and thus the large-scale leaf area index with high time resolution can be obtained; and the best matching surface soil reflectivity profile obtained based on the multi-spectral data is relatively stable, and historical time sequence data can also be made into a background library. The method can be applied in crop growth monitoring, rapid estimation of crop yields and the like.

The invention relates to a plant leaf area index measurement method based on the laser radar point cloud technology. Compared with the prior art, the defects that the leaf area index measurement efficiency is low, and the environment requirement is high are overcome. The method comprises the following steps of obtaining three-dimensional laser point cloud data of a plant canopy, cutting the point cloud data, conducting point cloud picture classification inside the plant edge contour, calculating the half side plant volume Vhalf of each plant, counting the number of hit points, counting the proportion of breaking points, and utilizing inversion for working out leaf area indexes. According to the plant leaf area index measurement method, by means of the three-dimensional point cloud data generated through a ground laser radar measurement system, with the combination of the computer graphics technology, the leaf area indexes are obtained from point cloud pictures for calculating needed relevant parameters and conducting multiple linear regression treatment, and therefore the leaf area indexes are calculated.

The invention discloses a remote sensing data-based leaf area indexes (LAI) inversion method for winter wheat in different growth periods. The method comprises the following steps of obtaining LAI actual measurement data; obtaining remote sensing data and performing preprocessing; dividing the whole growth period of the winter wheat into three stages, selecting five vegetation indexes NDVI, EVI, EVI2, RVI and OSAVI to perform LAI inversion of the winter wheat in the whole growth period and the different growth periods, and analyzing a relationship, in unary linear, exponential, logarithmic and power function forms, between the LAI actual measurement data and each vegetation index; comparing different index inversion results of the winter wheat in the different growth periods; and obtaining an optimal index inversion and fitting model of the winter wheat in the different growth periods according to the comparison of the different index inversion results of the winter wheat in the different growth periods. The method shows that GF-1 data has a very good application prospect in crop growth remote sensing research, and the situation that Chinese agricultural remote sensing monitoring depends on foreign data for a long term can be effectively improved.

The invention discloses a regional earth surface sensible heat/latent heat flux inversion method based on remote sensing data. The method comprises the following steps that a research area and the remote sensing data are determined; remote sensing earth surface and regional meteorological parameters are prepared according to the remote sensing data, wherein the remote sensing earth surface parameters comprise a normalized difference vegetation index NDVI, vegetation coverage f, albedo, earth surface emissivity Emiss, earth surface temperature Ts and a leaf area index LAI, and the regional meteorological parameters comprise air temperature Ta and relative humidity RH; net radiation flux Rn inversion is conducted according to the remote sensing earth surface and regional meteorological parameters; soil heat flux G inversion is conducted according to the leaf area index LAI, the air temperature Ta and net radiation flux Rn; radiation-convection impedance rae inversion is conducted according to theoretical two-dimensional space of the net radiation flux Rn, soil heat flux G, the vegetation coverage f and the earth surface temperature Ts and a temperature profile equation estimated through sensible heat flux; regional earth surface sensible heat H/latent heat LE flux inversion is achieved according to radiation-convection impedance rae.

The invention provides a leaf area index inversion method and system of merged phenological data and remote sensing data. The method comprises the steps of setting multiple sampling points used for observation in a target area, using a plant canopy analyzer for measuring leaf area indexes of plants in all the sampling points in the target area under the scattered light meteorological condition, and recording the vegetation phenological phase of the target area in the measurement process; averaging each sampling point, and obtaining the true leaf area index of the corresponding sampling point; obtaining multispectral remote sensing images observed during the same time period in the same target area, conducting pre-processing, obtaining true reflectivity images, and calculating the vegetation index of each sampling point; utilizing the vegetation indexes and the true leaf area indexes for conducting relevant analysis, and obtaining a quantitative relation model corresponding to the vegetation phenological phase of the target area in the measurement process; according to the model, conducting inversion analysis on the plant growth state of the target area during the corresponding vegetation phenological phase. By means of the leaf area index inversion method and system of the merged phenological data and the remote sensing data, dynamic monitoring of the large-area long-period vegetation leaf area indexes can be satisfied, the problem of field measurement is solved, and agricultural and forestry application requirements are met.

An irrigation and fertilization system based on crop environment cooperative feedback comprises a meteorological station, a soil humidity sensor, a leaf area index measuring instrument and a controller. After the meteorological station, the soil humidity sensor and the leaf area index measuring instrument obtain the sunshine duration, air pressure, air temperature, air humidity, precipitation amount, small climate environment 2-meter high wind speed, solar radiation, field water holding capacity, field water holding rate, water saturation degree, moisture content of withering point soil, a wilting coefficient and leaf area index parameters related to growth of crops, the parameters are transmitted to the controller for data processing and calculation, the infiltration amount, the potentialinfiltration amount, the potential crop transpiration amount, the soil surface potential evaporation amount and potential evaporation amount parameters are obtained, and a mathematical model is further established through calculation to form an irrigation plan.

The invention relates to a method for constructing a high-yielding cotton population structure of a jujube and cotton intercropping drip irrigation cotton field in Xinjiang. The method includes steps of creating a suitable population environment in the drip irrigation high-yielding cotton field; preparing seeds; sowing the seeds; irrigating; applying fertilizers; chemically regulating the cotton field; controlling diseases, insects and weeds; performing topping; and harvesting and the like. The method has the advantages that the high-yielding cotton population structure of the jujube and cotton intercropping drip irrigation cotton field in Xinjiang can be constructed; and as shown by investigation for a plurality of fields, specific indexes of the high-yielding population structure include that the maturity of selected cotton varieties is 5-8 days earlier than the maturity of local mono-cropping main cotton varieties, the period between a sowing stage and a boll opening stage is 148-156 days, cotton reaches the boll opening stage on 12-20 September under a jujube and cotton intercropping condition, the cotton assuredly reaches the boll opening stage on 12-20 September under the jujube and cotton intercropping condition, the maximum leaf area index is 3.9-4.3, the plant height is 69-77cm, the plant width is 51-58cm, the light interception rate of the cotton at a peak polling stage is higher than 80%, the fruit branch number is 8.6-9.2, the number of unit-plant bolls is 3.9-4.3 per plant, the actual number of harvesting plants is 15380-17450 per mu (net area), and the boll yield is 61581-71337 per mu.

The invention discloses a device for measuring leaf area indexes automatically and fast. The device comprises a laser, a photoelectric sensor, a signal processor, a data acquisition card and a computer, and further comprises a travelling mechanism, a motor driver and a motor control circuit board. According to the invention, the travelling trace of a U-shaped rack on the travelling mechanism is programmed and designed by a virtual instrument Lab VIEW 2010, the working of a stepping motor is controlled according to the output signals of the data acquisition card, and the stepping motor is used for controlling the travelling trace of the U-shaped rack on the travelling mechanism, so as to realize the fast positioning and the multidimensional automatic testing of a testing point. The invention further provides a method for measuring the leaf area indexes automatically and fast, so as to obtain the relation between a voltage value and the number of overlapped leaves by conducting fitting test on the testing point. Compared with the prior art, the invention has the advantage that the device is controlled automatically by adopting the stepping motor, so as to greatly improve the testing efficiency, and save large quantities of man power and testing time.

The invention discloses a method for monitoring the nitrogen concentration of vegetation canopies in a wetland based on a hyperspectral vegetation index. The method comprises the steps of measuring the spectrum, the leaf area index (LAI) and the nitrogen concentration of the vegetation canopies in the wetland; preprocessing a Hyperion hyperspectral remotely sensed image; improving an SAVI (soil adjusted vegetation index) to obtain an SAVI1510; constructing a hyperspectral vegetarian index NDNI/SAVI1510 applied to monitoring on the nitrogen concentration of vegetation canopies in the wetland, wherein the hyperspectral vegetarian index NDNI/SAVI1510 is applied to estimation on the performance of monitoring the nitrogen concentration of vegetation canopies in the wetland; and constructing a model for monitoring the nitrogen concentration of vegetation canopies in the wetland based on the hyperspectral vegetarian index NDNI/SAVI1510. The method for monitoring the nitrogen concentration of vegetation canopies in the wetland based on the hyperspectral vegetation index comprises the beneficial effects that the influence, caused by multiple scattering signals from a complicated background from vegetations in the wetland, on the wetland vegetation canopy nitrogen concentration estimation precision is weakened, and the nitrogen concentration of the vegetation canopies in the wetland can be estimated with high precision.

The invention discloses a method for estimating the leaf area index (LAI) of patch scale winter wheat based on high-spatial-resolution remote sensing satellite imagery, comprising: step 1, obtaining LAI data of a sampling point in a key growth period of winter wheat before heading; step2, obtaining a high-spatial-resolution remote sensing image covering a winter wheat field experimental research area, performing processing, and obtaining a vegetation index corresponding to the sampling point in step 1 through the band calculation; step 3, partially applying the LAI data and the vegetation index to construct models, and using the rest as verification data; step 4, verifying the constructed models with the verification data, obtaining decision coefficients R2 and root mean square errors of the model, and selecting a model of the maximum decision coefficient R2 and the minimal root mean square error as the optimal model; and step 6, applying the obtained optimal model to estimation of theleaf area index of the patch scale winter wheat.

The invention discloses a method for determining a leaf area index, comprising the following steps that: a laser outputs light to irradiate on overlapped leaves; a photoelectric transmitter detects the light energy transmitting through the leaves, and a voltage signal output by the photoelectric transmitter is acquired by a data acquisition card and sent to a computer; the computer judges the number of the overlapped layers according to the magnitude of the voltage signal, and multiplies the number of the overlapped layers by a receiving area of the photoelectric transmitter to obtain the leaf area of a test point; the laser and the photoelectric transmitter are synchronously in translational motion; the region of the leaf layers is scanned, and the leaf area of each test point is accumulated to get the total leaf area of the leaf layers; and the total leaf area is divided by an occupied area to obtain the leaf area index. The invention further provides a device for determining the leaf area index, which is used for implementing the determination method; and in comparison with the prior art, the method and the device are accurate in determination result, the device is simple and the preparation cost is low.

The embodiment of the invention provides a leaf area index inversion method and a device based on remote sensing images. On the basis of numerical values of hot and dark spot vegetation indexes which can reflect leaf aggregation information, leaf area inversion equations of different plant types are determined. Further, according to preset angle indexes, a leaf area index inversion equation which is suitable for all plant types of remote sensing images to be processed is obtained. Therefore, leaf area indexes which are obtained by using the inversion equation have higher inversion accuracy because the influences of vegetation plant types are considered.

A precision agriculture implementation method by UAV systems and artificial intelligence image processing technologies provides an unmanned aerial vehicle (UAV), a wireless communication device, a central control unit, and a spray device and a multispectral camera installed to the UAV. The farming area is divided into an array of blocks. The central control unit controls the UAV to fly over the blocks according to navigation parameters and the multispectral camera to capture a multispectral image of each block. A projected leaf area index (PLAI) and a normalized difference vegetation index (NDVI) of each block are calculated by the multispectral image, and a spray control mode of the spray device of the corresponding block is set according to the PLAI and NDVI. The spray device is controlled to spray a water solution, salt solution, fertilizer solution, and/or pesticide solution to the corresponding block according to the spray control mode.

The invention provides a corn canopy LAI and chlorophyll content joint inversion method and corn canopy LAI and chlorophyll content joint inversion equipment. The corn canopy LAI and chlorophyll content joint inversion method comprises: obtaining actual corn canopy spectral reflectivities of multiple spectrums at each growth period; establishing a lookup table based on a PROSAIL model, wherein theinput parameters in the lookup table comprise the leaf area indexes (LAI) and the chlorophyll contents of corn at each growth period, and the output parameters of the lookup table are corresponding simulated corn canopy spectral reflectivities; establishing a cost function based on a weighted square difference sum form, wherein the cost function is used for calculating the error between the simulated corn canopy spectral reflectivity and the real corn canopy spectral reflectivity; and carrying out inversion on the corn canopy LAI and the chlorophyll content based on the corresponding simulated corn canopy spectral reflectivity in the case of the minimum value of the cost function. According to the present invention, the LAI and the chlorophyll are simultaneously inverted with the one setof the parameters by establishing the joint distribution of the corn canopy LAI and chlorophyll content parameters so as to improve the parameter inversion efficiency.

The invention discloses a leaf area index (LAI) product inversion method and an LAI product inversion system for a global earth surface, and relates to the field of remote sensing data processing. The method comprises the following steps of: inputting original data into a system; preprocessing earth surface reflection index data of a moderate resolution imaging spectroradiometer (MODIS) and earth surface reflection index data of an advanced very high resolution radiometer (AVHRR); performing smoothing processing on MODIS LAI data and CYCLOPES LAI data; converting the CYCLOPES LAI data subjected to the smoothing processing into CYCLOPES LAI real data; obtaining fused LAI data; and obtaining a neural network by utilizing the fused LAI data, the preprocessed earth surface reflection index data of the MODIS and the earth surface reflection index data of the AVHRR and training earth surface grouped data of the MODIS, and thus obtaining LAI products of the global earth surface. By the method, the precision of the LAI products is improved and the requirements on earth system science and application research can be met.

An automatic measuring device for acquiring vegetation canopy structure parameters is composed of three parts, namely, a low-cost digital imaging subsystem, a data aggregation subsystem and a data receiving and receiving server subsystem. The low-cost digital imaging subsystem is connected in a communication mode with the data aggregation subsystem through a ZigBee wireless network. The data aggregation subsystem is connected in a communication mode with the data receiving and receiving server subsystem through an Internet network. According to the invention, an automatic structure parameter measuring system is implemented by combining a traditional crop canopy structure parameter measuring principle and a data acquisition and data transmission technology based on a wireless network. The automatic measuring system enables continuous automatic measurement of leaf area index and canopy mean leaf angle of long-time-series and large-space vegetations to be realized, and has wide application prospects in the technical field of agriculture and ecology.