Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

Rice diseases are major threats to rice yield and quality. Rapid and accurate detection of rice diseases is of great importance for precise disease prevention and treatment. Various spectroscopic techniques have been used to detect plant diseases. In order to rapidly and accurately detect three different rice diseases (leaf blight (Xanthomonas oryzae pv. Oryzae), rice blast (Pyricularia oryzae) and rice sheath blight (Rhizoctonia solani)), three spectroscopic techniques were applied, including visible/near-infrared hyperspectral imaging (HSI) spectra, mid-infrared spectroscopy (MIR) and laser-induced breakdown spectroscopy (LIBS). Three different levels of data fusion (raw data fusion, feature fusion and decision fusion) fusing three different types of spectral features were adopted to categorize healthy state of rice. Principle component analysis (PCA) and autoencoder (AE) were used to extract features. Identification models based on each technique and different fusion levels were built using support vector machine (SVM), logistic regression (LR) and convolution neural network (CNN). Models based on HSI performed better than models based on MIR and LIBS, with the accuracy over 93% for the prediction set based on PCA features of HSI spectra. Performance of rice diseases identification varied with different levels of fusion. The results showed that feature fusion and decision fusion could enhance the identification performance. The overall results showed that the three techniques could be used to identify rice diseases, and data fusion strategies have great potential to be used for rice disease detection.