Computer-Assisted Real-Time Rice Variety Learning Using Deep Learning Network

Due to the inconsistency of rice variety, agricultural industry faces an important challenge of rice grading and classification by the traditional grading system. The existing grading system is manual, which introduces stress and strain to humans due to visual inspection. Automated rice grading system development has been proposed as a promising research area in computer vision. In this study, an accurate deep learning-based non-contact and cost-effective rice grading system was developed by rice appearance and characteristics. The proposed system provided real-time processing by using a NI-myRIO with a high-resolution camera and user interface. We firstly trained the network by a rice public dataset to extract rice discriminative features. Secondly, by using transfer learning, the pre-trained network was used to locate the region by extracting a feature map. The proposed deep learning model was tested using two public standard datasets and a prototype real-time scanning system. Using AlexNet architecture, we obtained an average accuracy of 98.2% with 97.6% sensitivity and 96.4% specificity. To validate the real-time performance of proposed rice grading classification system, various performance indices were calculated and compared with the existing classifier. Both simulation and real-time experiment evaluations confirmed the robustness and reliability of the proposed rice grading system.