Research on Identification of Grain in the Grain Separator based on Machine Vision

The grain separator is an important machine for separating paddy and brown rice, the precise control of the separating device is one of the decisive factors to improve the separation efficiency. A paddy and brown rice detection device is proposed to detect the types of particles at the discharge end of the grain separator. Taking paddy rice and brown rice grains as the research objects, images of paddy and brown rice were first collected, and their color, morphological, and texture features were extracted. Subsequently, single-feature models and multi-feature combination models were constructed, which were trained and tested using four classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Bayes, and Random Forest. The experimental results show that compared with other feature combinations, the SVM classifier based on texture + shape + color has the highest recognition rate, and the recognition accuracy reaches 97.8%. The method can be applied to the testing device, and provides a theoretical basis for the precise control of the material separation device.