30 results about "Crop phenology" patented technology

The invention discloses a crop classification method and system. The classification method includes: constructing historical crop marker samples according to the historical multi-temporal mid-segment images and historical crop phenology data; constructing high-precision small-area current crop marker samples according to the current multi-temporal high-resolution images and current crop phenology data; according to ResNet model and PSPNet model to establish the remote sensing network RSNet model; pre-train the remote sensing network RSNet model according to the historical training samples to establish the pre-trained remote sensing network RSNet model; fine-tune the pre-trained remote sensing network RSNet model according to the current training samples to establish a fine-tuned remote sensing network RSNet model; establishing an optimal pre-trained remote sensing network RSNet model and an optimal fine-tuning remote sensing network RSNet model; classifying the crops according to the optimal pre-training remote sensing network RSNet model and the optimal fine-tuning remote sensing network RSNet model. By adopting the classification method and system provided by the present invention, the workload of training sample selection can be reduced, and the classification efficiency can be improved.

The invention relates to the field of agricultural technology, in particular to a growth stage simulation method based on the response and adaptation mechanism of crops to the environment. The method includes: collecting the measured crop phenology data at the research site; obtaining the required days of the development stage, the date sequence of the initial stage of the development stage, and the average temperature of the development stage according to the phenology data; obtaining the growth rate according to the required days of the development stage, and the growth rate is The reciprocal of the number of days required for the stage; calculate the value of the average temperature × initial date sequence; take the growth rate as the dependent variable, take the average temperature, average temperature × initial date sequence as the independent variable, use the binary linear regression method to obtain the regression equation, the The equation is the developmental period simulation formula. The method of the invention not only considers the mechanism of crop response to environmental changes, but also considers the mechanism of crop adaptation to the environment, so it can better simulate the development period than the existing model. And this method is simple, only needs 3 parameters, and the parameters can be obtained directly from the observation data.

The invention belongs to the field of agricultural remote sensing, and specifically relates to a cotton remote sensing monitoring method based on phenological analysis, including: combining regional cotton spectral feature analysis and interference crop phenological analysis to clearly identify key phases of cotton; acquiring remote sensing images of key phenological periods, and performing Preprocessing; multi-scale segmentation of multi-temporal images, hierarchical construction of classifiers, and preliminary extraction of cotton information; secondary segmentation of the preliminary results of cotton information and objects that may be mixed, and construction of classifiers based on cotton spectral features and texture features. Remove the mixed points and supplement the missing cotton information; merge and output the extracted cotton information subcategories. The present invention realizes the automatic extraction of cotton planting information through the phenological analysis of cotton and interfering crops combined with the layered classification method based on remote sensing images, and fully considers the spectral difference and texture characteristics of cotton and other interfering crops, so that the cotton planting information extraction results More accurate and faster, easy to promote and apply.

The invention discloses a crop distribution map, which calculates the area of ​​each type of crop on each grid based on grid-scale crop planting pattern information extracted from remote sensing data and the area ratios of various types of crops in the planting pattern in corresponding administrative units, thereby Make a crop spatial distribution map. The invention combines the crop phenology characteristics identified by remote sensing with the actual crop phenology to determine the spatial distribution of crops, and can achieve large-scale, multi-scale, and high spatial heterogeneity through remote sensing macroscopic, fast acquisition, and multi-scale features. Mapping the spatial distribution of crops.

The invention discloses a remote sensing monitoring method for winter wheat wet-spot damage based on the fusion of satellite-earth multi-source precipitation data, including: collecting and arranging ground rain gauge observation precipitation data sets, satellite inversion precipitation data sets, NDVI data sets, DEM data, crops Phenological information, statistical data of winter wheat planting area and field sampling data of winter wheat planting area, and preprocessing and filtering of NDVI data; use the processed NDVI time series data to extract the spatial information of winter wheat planting area; use geographically weighted regression Klicking method Construct a quantitative model of precipitation information fusion to obtain precipitation data sets; use the precipitation data set and extracted winter wheat planting area, combined with wet water damage indicators, to monitor the area affected by winter wheat wet water damage. The invention utilizes the data to carry out an effective method for large-scale spatial monitoring of crop wetness damage, and has stable technical feasibility.

The invention discloses a remote sensing rating method for the growth of large-scale crops, which includes: A. Obtain MODIS data of time-series resolution imaging spectrometer in the evaluation year, the MODIS data contains quality evaluation information, and perform preprocessing to obtain time-series enhanced vegetation Index EVI data; B, corrected time-series EVI data; C, obtain the time-series EVI data of the comparison year, and analyze the similarity between it and the corrected time-series EVI data, and find the benchmark time-series EVI data for growth evaluation; D, according to the benchmark time-series EVI data, graded evaluation time series EVI data of the specified number of periods in the evaluation year. The invention can adapt to the crop growth monitoring requirements of large-scale and complex planting systems, and can effectively avoid the influence of crop phenological changes and crop planting structure changes compared with the traditional simple historical period comparison method.

The invention discloses a large-scale crop phenology extraction method based on a morphological model method. Obtain the multi-year vegetation index time series curve in the local geographical area; use the double logistic function fitting method to smoothly fit the vegetation index time series curve; use the reference curve and reference point of the agricultural gas station to establish a morphological model; use transformation to fit the morphological model; use The optimal scaling parameter is calculated to obtain the extraction value of the phenological period in the local geographical area. The method of the invention utilizes the macroscopic characteristics of the curve, can reduce the influence of local fluctuation and noise of the curve, and has better extraction accuracy; and can simultaneously extract each phenological period of the crop, without setting different metric standards, and can Applied to the extraction of large-scale crop phenology.

The present invention relates to the field of cultivated land identification, in particular to a method and device for identifying effective cultivated land. The method and device use long-time sequence Landsat TM remote sensing data as a data source, use slope SLOPE data as auxiliary data, and use normalized differential vegetation The index NDVI is used as a classification feature, based on the phenological differences of crops on agricultural land, and reflects the differences in planting period, spectral difference and other characteristics of crop species to the distribution of time-series NDVI data, and designs a normalized difference The object-oriented decision-making classification rules combined with vegetation index NDVI and other classification features can not only extract the spatial distribution information of some crops on agricultural land, especially in West Asia, but also understand their cultivation conditions without considering the complex vegetation of different crops. However, the data source required by this method is easier to obtain, the amount of data to be processed is small, the classification rules are simple, and the work efficiency is high. It is also applicable to areas where the distribution of cultivated land is relatively fragmented.

The invention provides a method and a device for determining the phenological period of crops. The method obtains the target crop phenological curve of the target crop and the reference crop phenological curve corresponding to the target crop phenological curve; according to the reference crop phenological curve, performs waveform adjustment on the target crop phenological curve to obtain the adjusted target crop Phenological curve; determine the time alignment relationship information between the adjusted target crop phenology curve and the reference crop phenology curve; determine the phenological period of the target crop according to the time alignment relationship information. The method utilizes the target crop phenological curve of the target crop and the reference crop phenological curve corresponding to the target crop phenological curve to determine the phenological period of the target crop is more accurate and has more reference value, and avoids subjective evaluation caused by manual evaluation based on experience. It improves the efficiency of determining the phenological stages of target crops.

The invention discloses a method and a system for monitoring crop growth, which relates to the technical field of agricultural monitoring. The method comprises the following steps that a growth period of crops in a monitoring area is obtained; remote sensing data of the current year and historical remote sensing data in the time range of the growth period is obtained and pretreated to obtain vegetation index data of each pixel of the monitoring area; maximum and minimum values synthesis is carried out in the time range of the growth period to obtain the maximum value of the vegetation index in the growth period of the current year and the maximum and the minimum values of the vegetation index in the growth period of the historical year; the optimal vegetation index in the growth period of the monitoring area is obtained by calculation, and the situation of the current crop growth is determined according to the value and the level threshold range of the optimal vegetation index. The influence of unfavorable weather conditions on the vegetation index during the growth period of the crop is effectively reduced and the effect of the phenological migration on the monitoring result of the crop growth is overcome.

The invention discloses a non-point source pollution source intensity estimation method based on crop information remote sensing accurate inversion, comprising steps: 1, obtaining continuous high-time resolution remote sensing images of the same type and the same phenological crop; 2, extracting EVI time series data of the remote sensing images , to obtain the phenological curve of the crop; to obtain the fertilization amount of different fertilization types at different fertilization nodes of the crop; 3, the ESTARFM time-spatial fusion of the remote sensing data with high spatial resolution and high temporal resolution, and extract the EVI time series data of the fused remote sensing image ; 4. According to the fused EVI time series data, the planting area of ​​different fertilization nodes of crops is obtained; 5. According to the amount of fertilizer applied and the planting area of ​​different fertilization nodes of crops, the intensity of non-point source pollution sources is estimated. The invention accurately extracts crop types through remote sensing images, determines the accumulation process of fertilization amount along with crop growth through crop phenological characteristics and fertilization information, and provides a more accurate estimation value for the strength of non-point source pollution sources.