Hyperspectral imaging technology can simultaneously acquire the spectral features and image features of the target, and has been widely used in many fields such as agricultural resource survey and environmental monitoring. Combining spectral information with image information, we can monitor the indicators of crop growth more intuitively and accurately from multiple angles. There are significant differences in the image brightness and gray scale distribution of healthy leaves and diseased leaves. The gray value of hyperspectral images can be used as an important parameter to monitor crop growth.
Wheat is an important food crop and is also indispensable in the energy economy. Most of the sampling methods of traditional growth monitoring methods are destructive. This paper takes wheat as the research object. Based on the analysis of the gray value of wheat canopy spectral images at different growth stages, the spectral vegetation index is calculated, which is related to the wheat leaf area index and the above ground. Material weight, leaf nitrogen content measured value fitting model.
1. Experimental design and instrument
1.1 experimental design
A total of five nitrogens (N, N, N3, N4) were added to treat nitrogen, and 0, 150, 300, 450, 600 kg/hm2 of pure nitrogen was applied, and phosphorus (P2O5) 120.0 kg was applied in addition to nitrogen. /hm2, Potassium Fertilizer (K2O) 105.0 kg/hm2, field management is managed according to the standards of high-yield fields, and experimental data is used to construct the monitoring model.
1.1 Experimental equipment
The experimental instrument is a SOC710VP portable imaging spectrometer produced by American SOC Company. The spectral range is 400-1000nm, the spectral resolution range is 1.3nm, the band number is 128-512, and the total weight of the spectrometer is 2.95kg. The spectrometer can be equipped with a test bench and a microscope. It is equipped with a portable tripod and rocker arm for field measurements. This experiment is measured in the field. The SOC710VP imaging spectrometer adopts built-in push-pull mode, which avoids the distortion of the external push-pull pattern and the cumbersome operation. It can automatically correct the dark current and flexibly set the integration time.
2 results and analysis
2.1 Hyperspectral monitoring model of wheat LAI
Vegetation index RVI, DVI, NDVI, EVI, SAVI fitting wheat LAI monitoring model is more accurate, based on the gray value of the map spectral image to calculate vegetation index GR (670 870), GD (670 870), GND (670 870), GE ( 670 870), GSA (670 870). The trend of change is as shown in the figure below. The vegetation index shows a trend of increasing first and then decreasing. From the beginning of the greening period, the vegetation index is gradually increasing, and there is a maximum near the heading period. After the heading period, it begins to gradually decrease, and the filling period has a minimum. . The change characteristics of vegetation index and gray vegetation index were similar to wheat LAI.
After comparison and analysis, the correlation coefficient between GND and wheat LAI is the largest, the fitting model has the highest coefficient, and the study chooses GND as the independent variable. The wheat LAI is the dependent variable to establish the monitoring model y=1.885-15.68X+22.58x2 The coefficient of determination is 0.81 RMSE ( %)=1.01.
2.2 High-spectrum monitoring model of dry matter weight in wheat shoots
The changes of grayscale vegetation index GR (560 810), GD (560 810), GND (560 810) are shown in the following figure: the vegetation index increases first and then decreases throughout the growth period, and the heading period has the maximum value; The change of gray-scale vegetation index was consistent with the vegetation index, which increased first and then decreased, and the heading period had the maximum value.
After comparison and analysis, GND (660 760) in the gray vegetation index has the strongest correlation with the dry matter weight of wheat shoots. The fitting model has the highest coefficient and the model error is small. The model uses GND (660 760) as the independent variable, and the dry matter weight of the wheat shoots is the dependent variable. The optimal monitoring model y=-0.236+2.63x+2.53x2 determines the coefficient 0.78 RMSE (%)=0.5.
2.3 Hyperspectral Monitoring Model of Wheat Leaf Nitrogen Content
The vegetation index increased first and then decreased during the whole growth period. RVI (550 680), NDVI (550 680), and GRVI (560 680) had the highest value near the heading stage, and DVI (550 680) had the maximum near the flowering stage. . The gray-scale vegetation index also increased first and then decreased. GR (550 680), GND (550 680), GGR (550 680) had the maximum flowering period, and GD (550 680) had the maximum heading period. The vegetation index changed relatively gently. Compared with the vegetation index, the gray vegetation index changed a lot, which is closer to the change trend of leaf nitrogen content.
The correlation between GND and wheat leaf nitrogen content was the strongest in grayscale vegetation index. The fitting coefficient was the highest and the model error was small. It is suitable for establishing wheat leaf nitrogen monitoring model. Finally, GND (550 680) was chosen as the independent variable, and the nitrogen content of wheat leaves was the dependent variable. Establish a monitoring model y=38.716-2.41/x with a coefficient of determination of 0.723 RMSE (%)=5.7.
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