AgriFuTech is an AI-driven genomic prediction engine that enables faster, more accurate biological decision-making by learning directly from highly complex and noisy genomic datasets. Unlike existing genomic prediction approaches, which rely on extensive feature engineering and narrowly defined species-specific pipelines, AgriFuTech uses advanced deep-learning methods to identify meaningful biological patterns and deliver robust trait predictions across diverse environments.
Current genomic selection tools are often difficult to scale, require significant manual optimisation and perform poorly when transferred beyond specific crops, traits or datasets. AgriFuTech addresses these limitations through a flexible, domain-general AI framework capable of supporting applications across agriculture, biotechnology and other life-science sectors where multi-omics data present similar analytical challenges.
The technology builds on a functional demonstration platform developed on GitHub, which was recognised among the Top 50 entries in the Enterprise Ireland Student Entrepreneur Awards. Market analysis indicates strong commercial potential within the global plant genomics sector, including the rapidly growing genomic selection market. By accelerating breeding programmes, AgriFuTech has the potential to reduce breeding cycles by 50–70% and lower associated costs by 40–60%, delivering substantial productivity gains for crop developers and biotechnology companies.
This Translational Research Project will advance AgriFuTech from TRL 2–3 to TRL 5–6 by developing scalable deep-learning architectures, improving domain-generalisation capabilities and delivering an integrated prototype for evaluation with early adopters. Commercialisation pathways will include software licensing, strategic co-development partnerships and potential spin-out formation. Through its ability to generalise across species and traits, AgriFuTech offers a new generation of AI-enabled genomic intelligence for accelerating sustainable innovation in agriculture and beyond.
