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

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. Im...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-06, Vol.19 (12), p.2656
Main Authors: Joseph Raj, Alex Noel, Sundaram, Rahul, Mahesh, Vijayalakshmi G V, Zhuang, Zhemin, Simeone, Alessandro
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Automation
Classification
Conveyors
Data mining
Digital cameras
Digital imaging
Discriminant analysis
Eggs
Engineering
Females
Gender
Image acquisition
Image classification
Image processing
Machine learning
Methods
multi-sensor
Neural networks
pattern recognition
Seeds
Separation
Silk
Silkworms
support vector machine
Support vector machines
Textiles
X-rays
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19122656