ABSTRACT
This paper proposes a non-destructive technique for silk content estimation in cocoons. The price of a cocoon is determined by the silk content which is determined manually by visual inspection or feeling the toughness of the cocoon shell. The above methods are subjective, non-repeatable and prone to human error. With such non-transparent conventional methods of silk estimation, the buyers and sellers are unhappy over any transaction. Our proposed non-destructive technique uses soft x-ray image analysis technique backed up by soft computing algorithm to estimate silk content. Advance image processing and analysis techniques have been applied to extract morphological features from the x-ray images of the cocoons and features are fed to GRNN to estimate the silk content. Total 594 tasar cocoons have been analyzed with the developed solution and the results have been validated with human experts. Accuracy of the system for silk content estimation has been calculated as more than 85%.
- Dr. K. T. Chandy, Sericulture: Cocoon Marketing and Silk Reeling, Agricultural & Environmental Education Booklet No. 454, SERS- 4Google Scholar
- R. K. Datta, Global Silk Industry: A Complete Source Book, 2nd Edition, ISBN-10: 8131300870, (2012), 360.Google Scholar
- G. Subramanya, Morphology and life cycle of Bombyx mori, Sericulture and Apiculture, Directorate of Distance Education, Kuvempu University, Jnana Sahyadri, Shankaragatta, (2005), 27--46.Google Scholar
- Ronald P. Haff, Natsuko Toyofuku, X-ray detection of defects and contaminants in the food industry, Sensing and Instrumentation for Food Quality and Safety, Volume 2 (4), (2008), pp 262--273Google ScholarCross Ref
- Qiang Lü, Jianrong Cai, Yongping Li, Feng Wang, Real-time Nondestructive Inspection of Chestnuts Using X-ray Imaging and Dynamic Threshold, World Automation Congress (WAC), ISBN: 978-1-889335-42-1, (2010), 365--368,Google Scholar
- X-ray: http://en.wikipedia.org/wiki/X-ray.Google Scholar
- Bushberg Jerrold T., Seibert, J. Anthony; Leidholdt, Edwin M., Boone, John M., The essential physics of medical imaging. Lippincott Williams & Wilkins,. ISBN 978-0-683-30118-2, (2002), pp. 38Google Scholar
- David Attwood, Soft X-rays and extreme ultraviolet radiation. Cambridge University, ISBN 978-0-521-65214-8, (1999), pp.2 Google ScholarDigital Library
- R C Gonzalez & R E Woods, "Digital Image Processing" Addition- Wesley Publishing Company, (1992) Google ScholarDigital Library
- I.T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, 2nd ed. 2002Google Scholar
- Parinya Sanguansat, Principal Component Analysis Engineering ApplicationsGoogle Scholar
- http://en.wikipedia.org/wiki/Principal_component_analysisGoogle Scholar
- Donald F. SpechtI, A General Regression Neural Network, IEEE TRANSACTIONS ON NEURAL NETWORKS. VOL. 2. NO. 6.,(1991) Google ScholarDigital Library
- Matthias M. Bauer, General Regression Neural Network, GRNN-A Neural Network for Technical Use, A thesis submitted in partial fulfillment of the requirements of the degree of Master of Science(Chemical Engineering)at the University of Wisconsin-Madison, (2000)Google Scholar
- K. Kirtikara, C. Jivacate, A. Sangswang, K. Tunlasakun, B. Muenpinij, N. Patcharaprakiti, System identification with cross validation technique for modeling inverter of photovoltaic system, 2nd International Conference, ISBN :978-1-4244-8102-6, DOI: 10.1109/ICMET.2010.5598430, (2010), 594--598,Google Scholar
- R. Venkatesh, C. Rowland, Hongjin Huang, O. T. Abar, J. Sninsky, .Robust Model Selection Using Cross Validation: A Simple Iterative Technique for Developing Robust Gene Signatures in Biomedical Genomics Applications,; 5th International Conference on Machine Learning and Applications, ICMLA, ISBN- 0-7695-2735-3, DOI: 10.1109/ICMLA.2006.45, (2006) pp.193--198 Google ScholarCross Ref
- Cross Validation: http://en.wikipedia.org/wiki/Cross-validation_(statistics)Google Scholar
- Richard Picard, Dennis Cook"Cross-Validation of Regression Models". Journal of the American Statistical Association 79 (387), doi:10.2307/2288403, (1984), 575--583Google Scholar
- J. D. Rodriguez, A. Perez, J. A. Lozano, .Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation, DOI: 10.1109/TPAMI.2009.187, (2009), 569--575 Google ScholarCross Ref
- R Panneerselvam, RESEARCH METHODOLOGY, 1st Ed. (2012)Google Scholar
Index Terms
- X-Ray Imaging and General Regression Neural Network (GRNN) for Estimation of Silk Content in Cocoons
Recommendations
Vertebrae Segmentation from X-ray Images Using Convolutional Neural Network
IHIP 2018: Proceedings of the 2018 International Conference on Information Hiding and Image ProcessingNOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the IHIP 2018 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published ...
An adaptive image segmentation algorithm for X-ray quarantine inspection of selected fruits
Although X-ray scanners are commonly used in airports or customs for security inspection, practical application of X-ray imaging in quarantine inspection to prevent propagation of alien insect pests in imported fruits is still unavailable. The first ...
A lightweight CNN-based network on COVID-19 detection using X-ray and CT images
Abstract Background and objectivesThe traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether ...
Highlights- A lightweight CNN model LightEfficientNetV2 was proposed for detecting COVID-19, Pneumonia and Normal using X-ray and CT images with a small number of ...
Comments