This case study is published as part of the Innovate UK BridgeAI programme, under the Independent Scientific Advisor (ISA) offer delivered by The Alan Turing Institute. The ISA initiative 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 Peng Yue, Founder of SparkSoft, Zhipeng Yuan, University of Sheffield PhD student, and Professor 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 and Dominica D'Arcangelo, Programme Manager 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 explores the development of an AI-driven precision agriculture toolkit by SparkSoft to address the significant impact of plant-parasitic nematodes (PPN) on crop yields. Through the Innovate UK BridgeAI programme, SparkSoft collaborated with academic experts to enhance its platform, PPNAnalyzer, integrating computer vision and predictive modelling to improve pest detection and management. The project involved curating a high-quality dataset of over 5,000 labelled images, resulting in a robust detection system achieving 96% mean average precision. In addition, the platform incorporates spatial risk prediction using environmental data and a user-facing AI assistant powered by a retrieval-augmented generation (RAG) framework. Together, these features enable farmers to identify, forecast, and respond to pest threats more effectively. The case study demonstrates how academic–industry collaboration can accelerate the deployment of scalable AI solutions, supporting more sustainable farming practices, reducing reliance on pesticides, and contributing to long-term food security.