Non-Destructive Quality Analysis of Gujarat 17 Oryza Sativa SSP Indica (Indian Rice) using Artificial Neural Network |
Author(s): |
| Chetna Maheshwari , G.H. Patel College of Engg. and Technology, Vallabh Vidyanagar, India; Kavindra Jain, G.H. Patel College of Engg. and Technology, Vallabh Vidyanagar, India; Vinita Shah, G.H. Patel College of Engg. and Technology, Vallabh Vidyanagar, India |
Keywords: |
| Computer vision, Quality, Image processing, Oryza Sativa SSP Indica (Indian rice), Geometric features, ISEF edge detection, Multi-layer feed forward neural network. |
Abstract |
|
The paper presents a solution for quality evaluation and grading of Gujarat 17 rice using image processing and soft computing technique. In this paper basic problem of rice industry for quality assessment is defined which is traditionally done manually by human inspector. Proposed solution provides one alternative for an automated, non-destructive and cost-effective technique. The proposed method for quality assessment of Gujarat 17 rice using image processing and multi-layer feed forward neural network technique achieves high degree of quality than human vision inspection. The proposed algorithm based on morphological features is developed for counting the number of rice seeds with long seeds as well as small seeds. A trained multi-layer feed forward neural network based classifier is developed for identification of unknown rice seed quality. |
Other Details |
|
Paper ID: IJSRDV1I3082 Published in: Volume : 1, Issue : 3 Publication Date: 01/06/2013 Page(s): 725-728 |
Article Preview |
|
|
|
|
