On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Abstract

Rice is a high valued subsistence crop that feeds more than 3.5 billion of the world population. Its importance can be gauged from the fact that the top five rice exporting countries had a combined net export worth of around 19 billion dollars in 2018. A robust rice grain analysis and classification system can significantly improve performance both in terms of accuracy as well as time. In recent decades, this research area has garnered a lot of attention due to its socio-economic impact. In this paper, we reviewed the work done in image-based rice classification and gradation. The contribution of this study is three-fold. First, it divides the algorithms and techniques of this area into five different approaches namely; geometric, statistical, supervised, unsupervised, and deep learning. Among these, deep learning techniques have shown more promising results and gained attention for future research. Secondly, it divides the rice grain literature historically into three different eras. Thirdly, it summarizes various algorithms and techniques related to rice quality grading and rice disease identification.

Keywords

• Machine learning
• morphological features
• quality grading
• rice diseases
• rice grain