Vol. 11, Issue 1 (2022)

Author(s):
SK Mundotiya and Monika A Joshi

Abstract:
Rice (Oryza sativa L.) is one of the cultivated cereal plant species, known to have wide diversity and adaptability to growing conditions. An in-depth study of its morpho-physiological characters can improve its yield potential and diversification can be utilized for variety identification under PPV & FR Act 2001. Cultivars are defined by the International Convention for the Protection of New Varieties of Plants (UPOV, 1991). Distinguishing a variety on the basis of classical taxonomic approach is highly difficult because it is time consuming, labour intensive and expensive. Digital image analysis offers an objective and quantitative method for estimation of morphological parameters. Keeping in view the above facts, the present study was initiated with the objective of characterization and to study the variation in phenotypically similar rice varieties using leaf image analysis. The experimental material comprised of twenty-eight extant rice varieties and the sample size consisted of four images for each kind of leaf per side i.e. flag leaf ventral, flag leaf dorsal, penultimate leaf ventral and penultimate leaf dorsal. Thus, total number of images generated were (4 + 4 + 4 + 4) x 28 varieties = 448 images. The generated images were further analyzed for morphological, textural and chromatic features by MATLAB software (version 7.12.0.635, R2011a) and a huge database was created which was further used to generate dendrogram. Cluster analysis was performed based on dendrogram; resulting in seven different clusters of 28 varieties which made differentiation among phenotypically similar rice varieties. Thus, image analysis helped to successfully discriminate the varieties based on the leaf characters of similar rice varieties.