Rice and wheat nitrogen nutrition multispectral diagnosis method for precise fertilization by unmanned aerial vehicles

A rice and wheat nitrogen nutrition multispectral diagnosis method for precise fertilization by unmanned aerial vehicles (UAVs), comprising the following steps:

• step 1, acquiring multispectral UAV remote sensing images and collecting agronomic parameters of rice and wheat, and establishing a nitrogen nutrition spectral diagnosis model based on the remote sensing images and the agronomic parameters of rice and wheat;
• step 1.1, data acquisition:
• acquiring multispectral UAV images of rice and wheat under different nitrogen fertilizer gradients during key fertilization stages of rice and wheat;
• and dividing the multispectral UAV images into regions of interest to obtain a plurality of sub-regions, and using a five-point sampling method to perform destructive sampling on each sub-region;
• step 1.2, agronomic parameter acquisition:
• processing rice and wheat samples to obtain dry biomass, and measuring leaf nitrogen content (LNC);
• step 1.3, multispectral UAV image processing:
• splicing the acquired multispectral UAV images while performing radiometric calibration, and converting a digital number (DN) to reflectance based on a set of reflective plates with different reflectances to generate a tag image file format (TIFF) file of orthoimages for a study region;
• step 1.4, soil background value removal:
• removing soil and water background values from multispectral UAV images of rice and wheat in tillering and regreening stages during the key fertilization stages;
• step 1.5, UAV vegetation index extraction:
• using “band math” of environment for visualizing image (ENVI) and a vegetation index formula to perform band math on the multispectral UAV images to calculate vegetation indices;
• and extracting an average value of vegetation indices within each region of interest, and using the average value to represent a vegetation index for each sub-region;
• and
• step 1.6, data analysis:
• performing correlation analysis on the vegetation indices obtained in step 1.5 and the LNC obtained in step 1.2 to establish a nitrogen nutrition spectral diagnosis model;
• step 2, calculating a critical nitrogen concentration in aboveground parts of rice and wheat based on a critical nitrogen concentration dilution model for dry biomass, and further obtaining a critical nitrogen uptake;
• calculating an actual nitrogen uptake in the aboveground parts of rice and wheat based on the nitrogen nutrition spectral diagnosis model;
• and obtaining a recommended nitrogen topdressing amount for rice and wheat during corresponding stages based on the critical nitrogen uptake and the actual nitrogen uptake, specifically:
• step 2.1, calculating the critical nitrogen concentration of rice and wheat based on the dry biomass in step 1.2 combined with a critical nitrogen concentration dilution curve;
• step 2.2, obtaining an aboveground nitrogen concentration y1 of the plant based on the nitrogen nutrition spectral diagnosis model, and obtaining the actual nitrogen uptake of rice and wheat based on a product of the aboveground nitrogen concentration y1 and a dry biomass y2 of the plant;
• step 2.3, calculating the critical nitrogen uptake of rice and wheat based on a product of the critical nitrogen concentration of rice and wheat obtained in step 2.1 and the dry biomass;
• step 2.4, calculating a difference value between the actual nitrogen uptake of rice and wheat obtained in step 2.2 and the critical nitrogen uptake of rice and wheat obtained in step 2.3;
• and
• step 2.5, calculating a theoretical nitrogen deficiency, i.e., a theoretical nitrogen application amount Nd according to a ratio of the current growth duration of rice and wheat to the total growth duration;
• and
• step 3, saving the recommended nitrogen topdressing amount as an elevation image to generate a digital fertilization prescription map, guiding precise fertilization by agricultural UAVs, specifically:
• step 3.1, creating a new data source in Super map software, and importing a tif file of the multispectral UAV images;
• step 3.2, creating a polygon dataset in Super map software based on the theoretical nitrogen application amount Nd, calculating the required fertilization amount based on the nitrogen content of urea, and drawing a plot requiring fertilization to add data;
• step 3.3, converting a vector polygon dataset to an shp dataset;
• step 3.4, importing shp data into Arcmap and converting the imported shp file to a raster;
• step 3.5, writing the raster converted in step 3.4 to a raster data name with an extension name in a user-defined manner, changing a pixel type to 3_BIT_FLOAT, and changing the band to 1;
• step 3.6, converting the new raster embedded in step 3.5 to tif format;
• step 3.7, importing the tif file into GlobalMapper, selecting 32-bit floating-point samples, deselecting always generating square pixels, and generating a TFW file;
• and
• step 3.8, saving the elevation image according to a storage path of the agricultural UAVs to generate a digital fertilization prescription map recognizable by the agricultural UAVs.