Identification of Rice Varieties and Cultivation Techniques based-on Hyperspectral Image using Multi-output Spectral Xception

The use of deep learning model with hyperspectral image had been developed as a food identification system. This method was known to have a high level of accuracy without damaging the test sample. However, most of the CNN models developed were only capable to identify single target. It was inefficient when used for multiple targets such as identification of rice quality, due to it represents by multiple parameters. The model must be trained separately for each target. In this study, we proposed a model called Multi-output Spectral Xception that could classify objects in multi-class multi-output problems with hyperspectral image input. The proposed model was built by replacing 2D convolution layer with 3D convolution layer. It effectively extracts the spectral and spatial features. The model was evaluated using Indonesian rice with eight varieties and two type of cultivation techniques. Performance evaluations were done by calculate its accuracy using the confusion matrix, then compared it with state-of-the-art models. The result showed that the proposed model achieved the best performance among the other models, which was 97,82% for its average accuracy score.