# Prediction of Oleic Acid Content of Rapeseed Using Hyperspectral Technique
###### 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-DR\({}_{574}\), SPI-NDSI(\(R_{575}\), \(R_{576}\))] as the model input, the constructed BP (back propagation) neural networks model has the highest precision, with the coefficient of determination (\(R^{2}\)) 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. hyperspectral; multifractal; hurst exponent; oleic acid content; BP neural networks +
Footnote †: journal: Applied Sciences  
replaced the traditional infrared spectroscopy technology and has become the main tool in the field of crop growth diagnosis. Hyperspectral remote sensing data provides rich information of the object; however, it has strong mutuality and redundancy between adjacent bands, which may reduce the efficiency and even accuracy. Hence, it is necessary to extract and analyze the original hyperspectral curve. Among the multitudinous methods of hyperspectral feature analysis, the methods based on mathematical transformation (including statistics, physics, computing, etc.) will provide more robust results [7]. As a powerful and novel mathematical tool, fractal theory is always used to explain the nonlinear and complex structure in nature. In this work, we introduce multifractality into spectral feature analysis due to the fractal nature of the hyperspectral data [8; 9; 10]. Since the spectral reflectance curve is not only self-similar but also nonstationary, the application of the multifractal detrended fluctuation analysis (MF-DFA) method into this instance is good choice because this method can deal well with non-stationary measure in various fields [11; 12; 13; 14]. On the other hand, the inversion of oleic acid by using Hyperspectral Information depends on high performance training models. BP neural network model is a multi-layer feed forward neural network that is trained based on the error back propagation algorithm. Owing to the powerful ability of nonlinear mapping and prediction, it is widely used in hyperspectral inversion [15]. Among them, [PERSON] et al. [16] compared the BP neural network model and traditional regression model for estimating the accuracy of wheat biomass based on the hyperspectral vegetation index and found that the BP neural network was the champion among those methods. By using BP-based artificial neural network, [PERSON] et al. [17] considered the red-edge parameter as the input variables to estimate the chlorophyll content of the leaves of French platanus and Populus tometosa. [PERSON] et al. [18] used several characteristic bands, green peaks, and red-edge positions to construct a BP neural network model to invert the pigment content of rice. In this paper, we propose a spectral inversion model for the forecast of rapeseed's oleic acid content. The hyperspectral information of rapeseed is utilized in our considerations. By using three kinds of traditional hyperspectral characteristics together with its multifractal feature calculated by MF-DFA as model input, the spectral inversion model is constructed based on BP neural networks. The rest of this paper is organized as follows.