Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 127, Issue -, Pages 716-725Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2016.07.020
Keywords
Cultivar identification; Sparse coding; Locality constraint; Machine vision; Machine learning; Image processing
Funding
- Ministry of Science and Technology of Taiwan [MOST 104-2311-B-002-019-MY3]
Ask authors/readers for more resources
Rice (Oryza sativa L.) is a major staple food worldwide, and is traded extensively. The objective of this study is to distinguish the rice grains of 30 varieties nondestructively using image processing and sparse-representation-based classification (SRC). SRC uses over-complete bases to capture the representative traits of rice grains. In the experiments, rice grain images were acquired by microscopy. The morphological, color, and textural traits of the grain body, sterile lemmas, and brush were quantified. An SRC classifier was subsequently developed to identify the varieties of the grains using the traits as the inputs. The proposed approach could discriminate rice grain varieties with an accuracy of 89.1%. (C) 2016 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available