Automatic Rice Variety Identification System: state-of-the-art review, issues, challenges and future directions

Automatic rice variety identification or quality analysis is a challenging task in image processing and reflects advanced insights into agricultural research with the help of emerging computational technologies. It is the process of identifying the variety of the rice grains by matching them with the training dataset. It is an arduous task because the quality of rice grains is distinct from each other due to the availability of their numerous varieties in the market and unique inherent characteristics. Therefore, customers must identify the superior quality of rice from different available types in the market. This paper demonstrates an exhaustive and transparent perspective on the recent research studies for developing various identification systems using other techniques and a broad view towards this peculiar research area. The paper's main aim is to present in an organized way the related works on identification systems of rice and finally throws exposure on the synthesis analysis based on the research findings. This research study provides valuable and valuable assistance to novice researchers in the agricultural field by amalgamating the studies of various methods and techniques of feature extractions and classification required for automatic variety identification of rice. It is evident from the study that research work carried out on the automated variety identification systems with higher accuracy rates in deep learning using a conjunction of various features of rice is minimal as compared to other techniques and indeed presents a future direction.

Automated summary

Automatic rice variety identification or quality analysis is a challenging task in image processing that utilizes emerging computational technologies for advanced insights into agricultural research. This research study provides valuable assistance to novice researchers in the agricultural field by presenting a comprehensive perspective on the recent research studies for developing various identification systems. It highlights the minimal research work on automated variety identification systems in deep learning using a combination of rice features, presenting a future direction.