关键词: 油菜, 高光谱成像, 叶绿素含量

Abstract: Chlorophyll is an important factor in crop growth, which can be used to real time monitor the crop growth. Taking conventional higholeic acid rape variety as material, this paper conducted field experiment to study the spectral response of rape leaves at seedling stage and bolting stage under different cultivation measures. And then, it calculated the correlation between the first derivative of reflectance spectrum and its reflection spectrum with SPAD value, selected sensitive bands of rape leaves combining with the stepwise regression and computed the spectral index. The estimation model of chlorophyll content was established by linear regression and neural network. The results showed that the accuracy of the neural network chlorophyll estimation model constructed by spectral index showed relatively high level, and the model constructed by the spectral indices of seedling stage had the highest precision. The coefficient of determination R2 was 0807 0, the root mean squared error (RMSE) was 1131 5. The model accuracy constructed by the first order derivative spectral indices of the bolting stage was the highest. The coefficient of determination R2 was 0873 2, the root mean squared error (RMSE) was 1322 3. Inversion of rape chlorophyll by constructing BP neural network model was better during the seedling and bolting stage. Above results provided a theoretical reference for monitoring the chlorophyll content of rapeseed in a wide range by hyperspectral technology.

Key words: rapeseed, hyperspectral imaging, chlorophyll content