| Full text | |
| Author(s): |
Total Authors: 3
|
| Affiliation: | [1] Univ Nove Julho, Ind Engn Postgrad Program, BR-05001100 Sao Paulo, SP - Brazil
[2] Univ Sao Paulo, Escola Politecn, BR-05508010 Sao Paulo, SP - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE; v. 40, p. 84-94, APR 2015. |
| Web of Science Citations: | 10 |
| Abstract | |
Bean constitutes, with rice, the staple diet of the Brazilian people. The quality control of beans includes computing the percentages of different varieties present in a batch of beans. The selling price of the batch depends on these percentages. In this work, we propose a computer system for visual inspection of beans. We use ``correlation-based multi-shape granulometry{''} for the first time to spatially localize each grain in the image, together with its size, eccentricity and rotation angle. Using this technique, our system localized correctly 29,993 grains out of 30,000, even in images where many grains were ``glued{''} together. This is the main contribution of our work, because usually other systems fail to individualize ``glued{''} grains. Probably, the same technique can be used in many other agricultural product inspection systems to segment seeds and grains. After segmenting the grains, the system classifies each grain as one of the three most consumed varieties in Brazil, using a technique based on k-means and k-NN algorithms. This module classified correctly 29,956 grains out of 29,993. These extremely high success rates indicate that proposed system can actually be applied in automated inspection of beans. (C) 2015 Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 14/09194-5 - Intelligent classifier applied to visual quality inspection of bean grains |
| Grantee: | Sidnei Alves de Araújo |
| Support Opportunities: | Regular Research Grants |