Effective Management of the Sericulture Industry Based on Artificial Intelligence

Abstract

The sericulture industry remains one of the most significant agro-industrial sectors in many developing countries, contributing to rural employment, export revenues, and sustainable agricultural practices. However, the sector continues to face structural inefficiencies, including low productivity, high susceptibility to environmental changes, disease outbreaks, and limited integration of modern technologies. In this context, artificial intelligence (AI) emerges as a transformative tool capable of revolutionizing traditional sericulture systems through data-driven decision-making, automation, and predictive analytics.

This study investigates the role of AI in improving the efficiency of sericulture management and proposes an integrated framework that covers the entire value chain—from mulberry cultivation to cocoon processing and supply chain optimization. The research applies a combination of system analysis, comparative evaluation, and economic modeling to assess the effectiveness of AI-driven solutions. The findings indicate that AI implementation significantly enhances productivity, reduces operational costs, minimizes risks, and improves sustainability. The paper contributes to the theoretical and practical development of smart sericulture and provides recommendations for policymakers and industry stakeholders.