Cocoon quality assessment system using vibration impact acoustic emission processing

Abstract

Cocoons of the mulberry silkworm Bombyx mori L. are the main raw material for the silk production. Currently, at the market, their quality assessment and pricing are done on a few random samples by manual method, which is shaking cocoons with hand and assessing the generated sound, due to the absence of automated systems and time constraint. This manual method is subjective, laborious and prone to errors. A novel automated cocoon quality assessment system is proposed, which not only classifies them into good and defective ones but also subclassifies the later into dried and mute cocoons. A unique vibration impact acoustic emission (VIAE) is generated from each category due to the difference in the physical state of pupa inside the cocoon. In this system, the cocoons were vibrated using a plastic arm attached to a servo motor driven by Arduino board and the VIAE so generated was recorded by two microphones. A computer loaded with a custom-made algorithm preprocess the VIAE and compared its area under the curve of power spectral density against the pre-known threshold values, to identify the cocoon category. This automated system could successfully classify 86 cocoons with 100% accuracy in 4 s (excluding the duration of VIAE recording). This is better than the manual method in terms of accuracy, cost and skilled laborer dependency. This could make it a good replacement for the manual method to ensure the fairer cocoon trade in the market and better silk quality in the reeling centers.