Significance of Morphological Features in Rice Variety Classification Using Hyperspectral Imaging

Abstract

Varietal classification of rice seeds is a crucial task in the process of rice crop production, management, and quality control. Traditionally, classification is performed manually which gives slow and inconsistent results. Machine vision technology provides an automated, real-time, non-destructive and cost-effective solution to this problem. Methods that combine RGB and hyperspectral imaging have shown very good results in rice seed classification. In this paper, we demonstrate the significance of morphological and border related features used in addition to spectral information and propose a feature set that provides a substantial improvement in classification results. The proposed approach was successfully tested on a publicly available dataset of 8640 seed samples corresponding to 90 different rice seed varieties, contained in 180 hyperspectral and RGB image pairs, and resulted in an average F1 score of 85.65%.