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

Abstract

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.

Integrated Analysis of Machine Learning and Deep Learning in Silkworm Pupae (Bombyx mori) Species and Sex Identification

“…As shown in Figure 2, each pupa was imaged from the back, abdomen, and side views; therefore, a dataset of 3600 images was collected, representing male and female pupae from 5 species (10 classes with 360 images each). Given the significant differences in pupae weight by species and sex [13,29], weight served as the basis for data partitioning. For traditional approaches, the datasets were split at a ratio of 8:2 for training and testing.…”

“…female pupae from 5 species (10 classes with 360 images each). Given the significant differences in pupae weight by species and sex [13,29], weight served as the basis for data partitioning. For traditional approaches, the datasets were split at a ratio of 8:2 for training and testing.…”

“…Recently, powerful spectral and visual techniques have been proposed to identify pupal sexes. These techniques include magnetic resonance imaging (MRI) [8], X-ray imaging [9], hyper-spectral imaging (HSI) [10,11], near-infrared (NIR) spectroscopy [12], and image recognition [13]. Among these, NIR is frequently used for sex determination based on differences in morphology, gonadal traits such as eggs, and water content between male and female pupae [14][15][16][17][18].…”

A Customized VGG19 Network with Concatenation of Deep and Handcrafted Features for Brain Tumor Detection

“…The traditional DLA considers the SoftMax, which provides a reasonable accuracy with using transfer learning approach. Further, the performance of the DLA can be enhanced by employing appropriate classifiers [50][51][52][53].…”

Deep learning enhanced terahertz imaging of silkworm eggs development

“…The process of sericulture can be divided into many stages, as exhibited in Figure 1A. Automatic detection of cocoon gender classification has been realized (Raj et al, 2019). However, silkworm eggs play an important role in the whole sericulture industry chain, and automatic detection has not been realized.…”