Abstract
In order to detect the oleic acid content of rapeseed quickly and accurately, we propose, in this paper, an artificial BP neural networks based model for predicting oleic acid content by using rapeseed’s hyperspectral information. Four types of spectral features are selected for our investigation, namely multifractal index, sensitive band, trilateral parameters, and spectral index. Both univariate variable and multiple variables are considered as our model input. The result shows that the combined feature has higher precision and better stability than when using a single parameter. An interesting finding shows that the combined feature involving multifractal parameters can significantly improve the model performance. Taking the combined feature {MF-h(0), SB-DR574, SPI-NDSI(R575, R576)} as the model input, the constructed BP (back propagation) neural networks model has the highest precision, with the coefficient of determination (R2) 0.8753, root mean square error (RMSE) 1.0301, and relative error (RE) 1.047%. This result provides some experience for the rapid detection of rapeseed’s oleic acid content.
Highlights
Southern Regional Collaborative Innovation Center for Grain and Oil Crops, Hunan Agricultural University, Citation: Liu, F.; Wang, F.; Liao, G.; Abstract: In order to detect the oleic acid content of rapeseed quickly and accurately, we propose, in this paper, an artificial BP neural networks based model for predicting oleic acid content by using rapeseed’s hyperspectral information
We introduce multifractality into spectral feature analysis due to the fractal nature of the hyperspectral data [8,9,10]
Hyperspectral technology technology possesses possesses the the advantages advantages of of being being fast, fast, non-destructive, non-destructive, and highly efficient and, it can play an important role in crop nutrition diagnoand highly efficient and, it can play an important role in crop nutrition diagnosis
Summary
Southern Regional Collaborative Innovation Center for Grain and Oil Crops, Hunan Agricultural University, Citation: Liu, F.; Wang, F.; Liao, G.; Abstract: In order to detect the oleic acid content of rapeseed quickly and accurately, we propose, in this paper, an artificial BP neural networks based model for predicting oleic acid content by using rapeseed’s hyperspectral information. Four types of spectral features are selected for our investigation, namely multifractal index, sensitive band, trilateral parameters, and spectral index Both univariate variable and multiple variables are considered as our model input. Taking the combined feature {MF-h(0), SB-DR574 , SPI-NDSI(R575 , R576 )} as the model input, the constructed BP (back propagation) neural networks model has the highest precision, with the coefficient of determination (R2 ) 0.8753, root mean square error (RMSE) 1.0301, and relative error (RE) 1.047%. This result provides some experience for the rapid detection of rapeseed’s oleic acid content. Discovering methods that can quickly and accurately provide diagnosis of the fatty acid content in rapeseed is a critical job for the improvement of rapeseed fatty acid
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