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X-Ray Imaging and General Regression Neural Network (GRNN) for Estimation of Silk Content in Cocoons

Published: 26 February 2015 Publication History

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%.

References

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Dr. K. T. Chandy, Sericulture: Cocoon Marketing and Silk Reeling, Agricultural & Environmental Education Booklet No. 454, SERS- 4
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Cited By

View all
  • (2024)Silkworm Cocoon Assessment Using GRNN on X-Rays2024 1st International Conference on Communications and Computer Science (InCCCS)10.1109/InCCCS60947.2024.10593134(1-6)Online publication date: 22-May-2024
  • (2020)A Comparison Among Three Neural Network Models for Silk Content Estimation from X-Ray Image of CocoonsProceedings of the Global AI Congress 201910.1007/978-981-15-2188-1_37(469-484)Online publication date: 3-Apr-2020
  • (2020)Applications in X-ray TestingComputer Vision for X-Ray Testing10.1007/978-3-030-56769-9_9(375-436)Online publication date: 22-Dec-2020
  • Show More Cited By

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  1. X-Ray Imaging and General Regression Neural Network (GRNN) for Estimation of Silk Content in Cocoons

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        cover image ACM Other conferences
        PerMIn '15: Proceedings of the 2nd International Conference on Perception and Machine Intelligence
        February 2015
        269 pages
        ISBN:9781450320023
        DOI:10.1145/2708463
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        In-Cooperation

        • Dept. of Science and Techn., Government of India: Department of Science and Technology, Government of India

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 26 February 2015

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        Author Tags

        1. 10 fold cross validation
        2. Cocoon
        3. Generalized Regression Neural Network (GRNN)
        4. Image analysis
        5. Morphological features
        6. Silk
        7. Silk content estimation
        8. Soft x-ray
        9. Tasar cocoons
        10. X-ray

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        View all
        • (2024)Silkworm Cocoon Assessment Using GRNN on X-Rays2024 1st International Conference on Communications and Computer Science (InCCCS)10.1109/InCCCS60947.2024.10593134(1-6)Online publication date: 22-May-2024
        • (2020)A Comparison Among Three Neural Network Models for Silk Content Estimation from X-Ray Image of CocoonsProceedings of the Global AI Congress 201910.1007/978-981-15-2188-1_37(469-484)Online publication date: 3-Apr-2020
        • (2020)Applications in X-ray TestingComputer Vision for X-Ray Testing10.1007/978-3-030-56769-9_9(375-436)Online publication date: 22-Dec-2020
        • (2015)Applications in X-ray TestingComputer Vision for X-Ray Testing10.1007/978-3-319-20747-6_8(267-325)Online publication date: 25-Jul-2015

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