Integrating cluster technology and AI in cassava production: A triple helix approach to innovation and sustainability

Abstract

Cassava is a vital food crop that supports food and economic security in sub-Saharan Africa, Southeast Asia, and Latin America. Its tolerance to poor soils and drought makes it valuable in tropical and subtropical regions. However, cassava production is limited by low yields, post-harvest losses, pests, diseases, climate change, and inefficient value chains. This paper explores the integration of cluster technology and artificial intelligence (AI) within the Triple Helix Model, which promotes collaboration among academia, industry, and government to improve cassava productivity and value-chain efficiency. Cluster technology enhances cooperation among farmers, processors, and researchers through geographic and agro-industrial clustering, reducing costs and improving access to infrastructure and resources. AI complements these efforts through machine learning, remote sensing, real-time disease detection, yield forecasting, and data-driven decision-making. Applications such as drone-based pest monitoring and AI-assisted supply chain management demonstrate the benefits of this synergy. Successful examples include EMBRAPA’s AI-cluster programmes in Brazil, which improved cassava breeding, and blockchain-enabled agricultural clusters in Thailand, which enhanced supply chain transparency. Despite these successes, adoption remains constrained by high implementation costs, limited digital literacy, inadequate infrastructure, and weak policy support, particularly in sub-Saharan Africa. The study concludes that integrating cluster technology and AI within the Triple Helix framework can transform cassava production, strengthen food security, improve economic resilience, and support climate adaptation. Stronger partnerships and enabling policies are essential to unlocking cassava’s full potential as a strategic crop for sustainable global food systems.