Mutus Tech: enhancing pest management tools for Ghanaian farmers with BridgeAI internship support

This case study is published as part of the Innovate UK BridgeAI progra…, under the Independent Scientific Advisor (ISA) offer delivered by The Alan Turing Institute. The ISA offer provides transformative, evidence-based support to SMEs across BridgeAI’s priority sectors, empowering them to harness AI for strategic growth and practical impact.

We gratefully acknowledge the contributions of Tim Li, CEO and Founder, and Qing Xue, Senior Director of Product Management and Strategic Marketing at Mutus, Oakleigh Weekes, Sheffield University PhD student, as well as Dr. Po Yang, Independent Scientific Advisor for BridgeAI at The Alan Turing Institute, whose insights and engagement were invaluable to the development of this case study.

We would also like to express our appreciation to Alexandra Araujo Alvarez, Senior Research Community Manager for BridgeAI; Anna Bermani, Placement Manager, Dominica D'Arcangelo, Programme Manager; and Kathryn Hockman, Project Coordinator, for their leadership and support throughout this work. We further acknowledge Stuart Gillespie for his role as technical writer for this and other case studies within the ISA offer.

This work is led by Dr Vera Matser, Head of Strategic Capabilities and Principal Investigator for BridgeAI at The Alan Turing Institute.

Abstract

This case study presents Mutus Tech, a company developing AI-powered tools to support farmers in pest management, crop health, and agricultural decision-making. Focusing on the challenge of invasive pests such as fall armyworm in West Africa, Mutus Tech adapted its Pezego application to deliver locally relevant, data-driven advice through a combination of computer vision and retrieval-augmented generation (RAG) techniques.

Through support from the BridgeAI internship pilot, delivered by The Alan Turing Institute, the company collaborated with a doctoral researcher to enhance the technical robustness and scalability of its platform. Key contributions included restructuring the codebase for modular, multi-region deployment, improving documentation, and refining the system’s architecture to separate shared and localised knowledge components. User engagement with farmers and agricultural advisors informed the design of more accessible interfaces and introduced features to support human oversight of AI-generated recommendations.

This work demonstrates how targeted research placements can accelerate AI development in SMEs while ensuring solutions remain user-centred and context-aware. It also highlights the role of collaborative, interdisciplinary approaches in delivering scalable digital tools that support sustainable agriculture and real-world impact.