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Non-Destructive Quality Analysis of JIRASAR Oryza Sativa SSP Indica (Indian Rice) using Feed Forward Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 172 - Number 7 |
| Year of Publication: 2017 |
| Authors: Niky K. Jain, Samrat O. Khanna, Chetna K. Shah |
10.5120/ijca2017915186
|
Niky K. Jain, Samrat O. Khanna, Chetna K. Shah . Non-Destructive Quality Analysis of JIRASAR Oryza Sativa SSP Indica (Indian Rice) using Feed Forward Neural Network. International Journal of Computer Applications. 172, 7 ( Aug 2017), 35-39. DOI=10.5120/ijca2017915186
Abstract
The Carrying out compelling and reasonable agriculture product has turned into an important issue in recent years. Agricultural production needs to stay aware with an ever-increasing population. A key to this is the utilization of present day strategies (for precision agriculture) to exploit the quality in the market. Classification of rice seeds from the exposed human hands is neither savvy nor prescribed. The automatic grading for examination of quality has turned into the need of great importance. This paper prescribes an extra way to deal with quality specialists for the quality investigation of INDIAN JIRASAR Rice using computer vision and soft computing techniques. Computer Vision gives a grading methodology, non-destructive technique, along with multi-layer feed forward neural networking which achieves high degree of quality than human vision inspection.
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