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Open AccessArticle

A Multi-Sensor System for Silkworm Cocoon Gender Classification via Image Processing and Support Vector Machine

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Department of Electronic Engineering, Shantou University, Shantou 515063, China
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Department of Agricultural and Food Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
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Department of Electronics and Communication Engineering, Dr T Thimmaiah Institute of Technology, Kolar Gold Fields, Karnataka 563120, India
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Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Shantou 515063, China
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Author to whom correspondence should be addressed.
Sensors 2019, 19(12), 2656; https://doi.org/10.3390/s19122656
Received: 16 April 2019 / Revised: 6 June 2019 / Accepted: 10 June 2019 / Published: 12 June 2019
(This article belongs to the Special Issue Sensor Applications for Smart Manufacturing Technology and Systems)
Sericulture is traditionally a labor-intensive rural-based industry. In modern contexts, the development of process automation faces new challenges related to quality and efficiency. During the silkworm farming life cycle, a common issue is represented by the gender classification of the cocoons. Improper cocoon separation negatively affects quantity and quality of the yield resulting in disruptive bottlenecks for the productivity. To tackle this issue, this paper proposes a multi sensor system for silkworm cocoons gender classification and separation. Utilizing a load sensor and a digital camera, the system acquires weight and digital images from individual silkworm cocoons. An image processing procedure is then applied to extract significant shape-related features from each image instance, which, combined with the weight data, are provided as inputs to train a Support Vector Machine-based pattern classifier for gender classification. Subsequently, an air blower mechanism and a conveyor system sort the cocoons into their respective bins. The developed system was trained and tested on two different types of silkworm cocoons breeds, respectively CSR2 and Pure Mysore. The system performances are finally discussed in terms of accuracy, robustness and computation time. View Full-Text
Keywords: multi-sensor; image processing; support vector machine; pattern recognition multi-sensor; image processing; support vector machine; pattern recognition
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MDPI and ACS Style

Joseph Raj, A.N.; Sundaram, R.; Mahesh, V.G.; Zhuang, Z.; Simeone, A. A Multi-Sensor System for Silkworm Cocoon Gender Classification via Image Processing and Support Vector Machine. Sensors 2019, 19, 2656.

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