Transforming Silk Production: The Future of Sericulture 4.0

Abstract

Sericulture 4.0, the rearing of silkworms for silk producing goods, has an extensive tradition and economic value. Technological approaches, including genetic engineering, automated rearing systems, and enhanced mulberry growing techniques, have substantially strengthened silk quality and production. Biotechnology breakthroughs have provided sustainable techniques, especially artificial meals and regulated environments for silkworms, hence minimizing the industry's ecological influence. Sustainable approaches involving organic sericulture, integrated pest control, and eco-friendly processing treatments are transforming the business into one that cherishes ecology. Sericulture 4.0 can maintain its tradition of luxury and economic importance while moving towards a more ecologically sound future by integrating these advances. Nanotechnology has transformed sericulture practices, increasing the quantity, quality, and potential use of silk. The article explores the impact of nanotechnology on silk fiber quality, processing methods, and the creation of innovative silk-based products. Nano-scale compounds may enhance silk's antimicrobial characteristics, prolong its shelf life, and have applications in medical materials. Nanotechnology also makes it easier to create silk-based materials with unique features including better electrical conductivity, controlled medication release, and biocompatibility. Sericulture, an extensive cottage industry, demands high-quality inputs involving seeds, nourishment, and skilled labor to run and produce effectively. Traditional approaches may involve manual phenotypic characteristic evaluations. High-quality silk fabrication is crucial for attaining sustainable development by 2030. This article emphasizes the significance of utilizing Artificial Intelligence (AI), machine learning, and blockchain technologies for a flourishing future and economy in the Sericulture industry. Integrating intelligent tools is essential for a flourishing future.  This article presents a novel approach for predicting silk quality based on cocoon morphological parameters and the XG Boost algorithm. The technique predicts silk quality with excellent accuracy based on cocoon morphological parameters. The XG Boost algorithm assists in identifying critical characteristics, offering significant insights into the aspects that influence silk quality.