Convolution Neural Network Application in the Simultaneous Detection of Gender and Variety of Silkworm (Bombyx mori) Cocoons

Abstract

Convolution neural network (CNN) has been widely studied in the field of computer vision with good results, and a lot of research is still needed to show its application effect in the near-infrared spectroscopy with 1-D input data and small sample set. In this paper, the near-infrared diffuse transmission spectra of 2386 silkworm (Bombyx mori) cocoons from the 4th day of cocooning to the 13th day before turning into moths were collected. After the mean center preprocessing, CNN, SVM and LDA were used to model and analyze. Experimental results showed that, with the convolution kernel width 100, the convolution kernel num 16, and the stride 50, the good generalization effect of CNN model can be achieved when the simple structure of 6 layers without pooling layer was adopted. For each day, the gender prediction accuracy of cocoons is more than 90% and the highest accuracy is 100% on the 6th day with optimal model CNN. For each day, the variety prediction accuracy of cocoons is more than 98% and the highest accuracy is 100% with optimal model LDA. There is no significant correlation between pupa age and the gender and variety detection accuracy of silkworm cocoons. This can help broaden the pupa gender identification time in the actual production of silkworm egg farms. In the application of simultaneous identification of gender and variety, CNN model has the highest accuracy of 94%, LDA model has the medium accuracy of 92.5%, and SVM model has the lowest accuracy of 89.5%.

Automated Prototype for Bombyx mori Cocoon Sorting Attempts to Improve Silk Quality and Production Efficiency through Multi-Step Approach and Machine Learning Algorithms

“…The prototype described in this paper was conceived by the authors to substitute for selection at the farmers’ site, which is very time-demanding and therefore critical in increasing production costs. The idea is that cocoon producers can transport their cocoons to the drying plant, where they can convey and desiccate their cocoons separately from other farmers (thanks to the traceability process established in the Serinnovation project) and according to the different quality classes (grades); the final aim is to calculate an adequate remuneration for each farmer, according to the accuracy of the work individually performed and, therefore, the quality of their eventual output [ 13 ], and also to transfer an already almost completely sorted product to the reeling plant to diminish selection work on cocoons to be reeled at the industrial place. The prototype was based on a patented idea [ 16 ].…”

“…A further study identified the sex of silkworm pupae using radial-based neural networks (RBF-NNs), through which an accuracy rate of more than 98% was achieved [ 13 ].…”