Advances in Research

Sericulture — encompassing the cultivation of mulberry, the rearing of silkworms (Bombyx mori Linnaeus), and the reeling and processing of silk — is one of the world's oldest and most culturally significant agro-industries. Sustaining the livelihoods of tens of millions of farming households across Asia, Africa, and Latin America, the sector nonetheless confronts a range of persistent challenges: recurring epidemic diseases, labour-intensive operations, inconsistent raw silk quality, and limited access to precision management tools. The rapid advancement of artificial intelligence (AI) technologies — including machine learning, deep learning, computer vision, Internet of Things integration, and genomic analytics — offers a compelling and timely opportunity to modernise sericulture practice across the full production chain. This critical review synthesises current and emerging evidence on AI applications in sericulture, examining their potential in silkworm disease detection, mulberry cultivation management, cocoon and silk quality assessment, smart rearing environment control, and genomic breed improvement. Drawing on evidence published predominantly between 2000 and 2026, the review identifies meaningful convergences between advances in precision agriculture and the specific demands of the sericulture sector. Whilst direct AI applications within sericulture remain comparatively nascent, the methodological transfer from crop science, entomology, and textile engineering is accelerating. Key opportunities lie in automating disease diagnosis, optimising rearing conditions through IoT sensor networks, and leveraging genomic data for breed improvement. The review critically appraises barriers to adoption — including data scarcity, digital infrastructure deficits, and the skills gap among smallholder farmers — and articulates a forward-looking research agenda for this underserved but globally significant sector.