Weaving the Future: Automation and AI in Sericulture 4.0

Abstract

Spinning, weaving, knitting, braiding, stitching, and dyeing are fundamental textile production processes that have been in use since ancient times. However, contemporary technology have posed new environmental dangers. This chapter investigates current breakthroughs in sustainable textile technology and their implications for the developing world's textile industry. It also looks at the sustainability issues that these modern systems confront in addressing the growing population requirements. Neurobiological research is looking at the brain underpinnings of insect behaviors, which will help us understand biology and have possible applications in robotics and artificial intelligence (AI). Micro-biomics studies insect-microbial connections and proposes novel pest management tactics. Environmental entomology studies the effects of habitat change and climatic variability on insect populations, which are critical for biodiversity conservation. The field is at the forefront of technological advancements and multidisciplinary techniques, which improve our understanding of insects' roles in ecosystems, adaptation, and ecological balance. This future path has intriguing scientific research implications for sustainable ecosystem management and conservation policy. Due to their porous nature, 3D-printed silk fibroin scaffolds provide several benefits in wound healing, including cell infiltration, nutrition exchange, waste disposal, and tissue regeneration. By modifying the printing settings, they guarantee stability and support throughout recovery. AI-driven printing processes increase wound dressing accuracy, customization, and personalization, while also increasing time and cost efficiency and accelerating research and development. AI algorithms improve design and manufacture using patient-specific data, leading in better-fitting dressings, faster production, and better wound healing results. The study examined traditional classifiers such as support vector machines (SVM) and K nearest neighbors (KNN) for detecting the sex of silkworm pupae from different years and species. A CNN model was trained to determine the gender using hyperspectral spectra. According to principal component analysis (PCA), CNN outperformed SVM and KNN in terms of accuracy. The study also found that HSI technology coupled with CNN was effective in detecting the gender of silkworm pupae.