AVNDVI (Accumulative Visible Normalized Difference Vegetation Index), a new type of derivative parameters of NDVI, was set up by improving the computational formulas and importing the spectral information of visible bands after analyzing the construction idea of NDVI and its derivative parameters. Then, the characteristic values of VNDVI (Visible NDVI) were calculated by applying a combinational method of sensitive bands of visible bands. The study carried out the fitting analysis between NDVI, VNDVI, AVNDVI, and LAI (Leaf Area Index). Several conclusions are obtained according to data analysis. Firstly, all of the determination coefficients between NDVI, VNDVI, AVNDVI, and LAI of rapeseed can reach or exceed 0.83. The distribution of their RMSE values ranges from 0.4 to 0.5 and absolute values of RE vary from 0.9% to 2.1%. Secondly, the inversion sensitivity SV of VNDVI and LAI ranges from 0.7 to 1.9 relative to NDVI, and the inversion sensitivity SA of AVNDVI decreases in varying degrees with the promotion of capacity of resisting disturbance accordingly. Its value varies from 0.1 to 0.9. Thirdly, the values of SA remain stable between 0.1 and 0.3 with the increase of NDVI. Applying the inversion model of AVNDVI will be a considerable scheme when faced with a complex environment and many interfering factors.
The inconsistency of chlorophyll content in rapeseed directly affects the quality of seed, so it is necessary to establish a rapid and nondestructive detection technology of chlorophyll in mature rapeseed. In this paper, the content of chlorophyll in rapeseed samples was determined by ultraviolet-visible spectrophotometry, and the near-infrared spectrum data were collected. The partial least squares regression model of chlorophyll content in rapeseed was established based on hyperspectral technology through first derivative and standard normal variable transformation pretreatment. The results of the cross-test showed that the determination coefficient of the validation set of the prediction model of chlorophyll content in rapeseed was greater than 0.85, and the root mean square error (RMSE) of the cross-test was less than 0.3 mg/kg. The results showed that the method could accurately predict the chlorophyll content in rapeseed and provide important technical support for rapid monitoring of seed quality.
At present, people’s living standard and consumption level are increasing day by day, the demand for edible oil crops is increasing. As the main source of oil crops, rape needs to use more accurate management and cultivation techniques, change backward technology, increase overall yield, and ensure the supply of oil crops. By means of artificial intelligence, the spectral segments of plants were analyzed to determine the species of light and the nutrients produced by plants, the monitoring of development and the prediction of crop yield. According to the spectral information of plant absorption and reflection, the chlorophyll level contained in plants can provide data support for rape planting and management, and monitor the real-time status of its growth and development. Mastering the nutrition and demand of rape in the whole process of growth and reproduction is very beneficial to the management and yield improvement of rape. And can adjust the cultivation method in time, play the advantage of hyperspectral technology.
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