Summary
This 2025 AHDB evaluation compares machine learning and automated detection systems for barley yellow dwarf virus surveillance against conventional human field assessment in UK cereal production. The work addresses a methodological gap in disease monitoring by benchmarking algorithmic performance and documenting practical barriers to adoption in operational farm settings. Findings are intended to support the development of decision-support tools and inform evidence-based disease management protocols for UK cereal growers.
Regional applicability
This study was conducted in the United Kingdom and directly addresses UK cereal disease management contexts, AHDB priorities, and practical adoption challenges for UK farmers. Findings should be directly applicable to UK cereal production systems and disease surveillance policy.
Key measures
Detection accuracy, sensitivity and specificity of machine learning algorithms versus human assessment; practical constraints to field adoption of automated systems
Outcomes reported
The study compared machine learning and automated detection systems for barley yellow dwarf virus (BYDV) monitoring against conventional human field assessment in UK cereals. It evaluated algorithmic performance and identified practical constraints to adoption of automated surveillance tools.
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