8 results about "Enhanced vegetation index" patented technology

The invention relates to the field of agricultural yield prediction, and particularly discloses a crop yield prediction method and system based on artificial intelligence, and the method comprises the steps: S1, recognizing crop types through the spectral features of a remote sensing image and field sampling data, and dividing corresponding key growth stages; s2, collecting multi-source feature data for each key growth stage, wherein the multi-source feature data comprises remote sensing image data, an extracted normalized difference vegetation index NDVI, an enhanced vegetation index EVI and a topographic factor; acquiring hourly temperature, humidity, precipitation and illumination intensity of each sub-region through a distributed sensor according to micro-meteorological data; obtaining plant height, leaf area index and fruit development parameters through unmanned aerial vehicle three-dimensional imaging according to crop phenotype data; by adopting the technical scheme of the invention, the terrain, microclimate and crop dynamic growth characteristics can be accurately fused, and the problem of non-uniform sample distribution is solved, so that the accuracy and regional adaptability of yield prediction are improved.

The invention discloses a vegetation coverage index algorithm and system based on an unmanned aerial vehicle. The algorithm comprises the following steps: S1, data acquisition and multi-source data preprocessing; s2, dynamic environment correction and multi-source data fusion; s3, red edge enhanced vegetation index calculation and terrain correction; s4, vegetation coverage intelligent prediction and precision verification; and S5, result visualization and decision support: generating a vegetation coverage spatial distribution map and a statistical report, and supporting a resource management decision. By integrating the unmanned aerial vehicle, the multispectral imaging sensor, the dynamic environment correction model and the data fusion algorithm, efficient and high-precision technical support is provided for precision agriculture, ecological resource management and disaster monitoring.

The application discloses a kind of estimation method and system of vegetation canopy combustible moisture content based on meteorology and remote sensing data, comprising the following steps: first, combustible moisture content and various meteorological data and remote sensing data and other several kinds of subsidiary data are selected as combustible moisture content estimation data.Meteorological data includes air temperature, relative humidity, rainfall and wind speed.Remote sensing data includes two vegetation indexes: enhanced vegetation index and normalized vegetation index.Subsidiary data includes: root zone soil moisture, vapor pressure difference, drought index, fire weather factor.Then the long time sequence characteristics of meteorological data are extracted.The size of time window is determined first, and the experimental results show that the correlation coefficient of most sites is relatively high under the time window of 90-210 days.90 days, 150 days and 210 days of time window are selected respectively to extract the time characteristics of four kinds of meteorological data.Secondly, the samples of experimental area are divided into five vegetation classifications, which are closed shrub, sparse shrub, multi-tree tropical grassland, tropical savanna and grassland.Finally, the data sets of the five different vegetation types are sequentially adjusted to obtain the respective estimation model.

This application provides a method and system for identifying the operational boundary of an agricultural drone. When the index difference value between vegetated and non-vegetated areas in farmland is lower than the hard scenario threshold of the agricultural drone, vegetation endmember spectra and soil endmember spectra are extracted from the multispectral image. Based on the vegetation and soil endmember spectra, spectral mixing decomposition is performed on each pixel in the multispectral image to obtain the vegetation endmember abundance of each pixel. A vegetation abundance map is constructed based on all vegetation endmember abundance values. The original vegetation index map is enhanced using the vegetation abundance map to obtain an enhanced vegetation index map. The enhanced vegetation index map is input into a pre-trained semantic segmentation network model, which outputs a binarized operational area mask. The contour of the operational area mask is used as the boundary coordinates of the operational area. Based on the above scheme, spectral identification of crop boundaries in low-contrast farmland scenes can be achieved.

The present application relates to a kind of irrigation farmland crop low temperature cold injury monitoring and diagnosis method, device, equipment and medium, comprising: collecting irrigation farmland multi-source data;Key indicators are calculated based on farmland multi-source data, wherein, key indicators include: land surface temperature LST, enhanced vegetation index EVI, sunlight-induced chlorophyll fluorescence SIF and solar declination delta;The key indicators calculated are calculated by the air temperature estimation model of pre-constructed irrigation farmland to obtain farmland daily average air temperature, and based on the low temperature cold injury determination standard of farmland crop, the monitoring and diagnosis of farmland crop low temperature cold injury are carried out.The present application can be based on the actual situation of irrigation farmland crop growth, utilize multi-source remote sensing data, consider the influence of surface temperature, crop growth, canopy physiology, solar radiation and other multi-factor, accurately estimate the air temperature of irrigation farmland, have strong operability, practicality is strong, it is easy to promote.

The invention discloses a wheat field grain water content prediction method and system based on a PlanetScope image. The method comprises the following steps: acquiring 8-waveband multispectral surface reflectance data of PlanetScope, screening an image of which the cloud cover is lower than a preset percentage from the 8-waveband multispectral surface reflectance data, and calculating an enhanced vegetation index (EVI) pixel by pixel to obtain an EVI time sequence; obtaining the heading period of the winter wheat based on the preprocessed EVI time sequence; based on the heading period of the winter wheat, predicting the physiological mature period of the winter wheat through an effective accumulated temperature accumulation model; an actually measured wheat grain moisture content sample is obtained, and a field grain drying model combining a moisture diffusion mechanism, saturation vapor pressure difference adjustment and Gaussian function constraint is constructed based on the wheat grain moisture content sample; and by taking the predicted physiological mature period as a priori and combining daily meteorological data and a field grain drying model, simulating the dynamic change of the moisture content of the wheat grains day by day, and generating a spatial distribution diagram of the moisture content of the wheat grains. The method can predict the water content of the wheat grains in the field in a high-time-efficiency and high-precision manner.

The invention relates to an urban ecological environment evaluation model construction method. The method comprises the following steps: inputting a remote sensing image, night light annual synthesis data and auxiliary space data; calculating a normalized differential vegetation index and an enhanced vegetation index; calculating the surface temperature; calculating urban heat island intensity as a pressure index; producing land cover data; calculating primary indexes of each criterion layer in the model, and performing gridding and normalization processing; designing primary indexes of the model; carrying out improved normal cloud combination empowerment; comprehensive evaluation based on the closeness of cloud similarity is carried out; and outputting a final comprehensive evaluation product. Performing space-time dynamic analysis and driving mechanism analysis; future scene simulation based on the improved LSTM model is carried out; and outputting a final comprehensive prediction product. The invention also relates to an urban ecological environment evaluation model construction system. According to the method, a new generation of urban ecological environment quantitative evaluation model with cognitive robustness, spatial insight and dynamic predictability can be constructed.

The invention discloses a winter wheat physiological mature period prediction method and system based on a spectrum time sequence. The method comprises the following steps: acquiring multispectral remote sensing data during the growth period of winter wheat in a target area, and calculating an enhanced vegetation index (EVI); calculating the AGDD of the accumulated effective accumulated temperature growth degree days of the winter wheat, and constructing an AGDD-EVI time sequence based on the AGDD of the accumulated effective accumulated temperature growth degree days and the EVI time sequence; based on the AGDD-EVI time sequence, using a double Logistic function and K-means clustering to construct a typical growth curve shape model; performing translation matching on the AGDD-EVI time sequence and the typical growth curve shape model to obtain a matching model, selecting the matching model with the minimum root-mean-square error, and updating the double-Logistic function to obtain a final matching model; based on the final matching model, using a DSLIM (dual-single Logistic curve intersection method) to determine an AGDD value in the physiological maturation period, and converting the AGDD value into a day order after sowing to obtain the physiological maturation period time of the winter wheat. According to the method, efficient and stable intra-season prediction of the physiological mature period of winter wheat is realized.