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About

Minna Kujala is a pioneering researcher in precision livestock farming, with a particular focus on automated lameness detection and welfare assessment in dairy cattle. Her work sits at the intersection of animal science, veterinary health, and sensor technology, addressing one of the most economically and ethically significant challenges in modern dairy production. Kujala's most influential contribution is the development of force sensor and balance systems integrated into robotic milking units to continuously monitor leg-load distribution in dairy cows. Her 2007 probabilistic neural network model for lameness detection, which analyzed data from nearly 10,000 robotic milkings across 73 cows, demonstrated that automated systems could reliably identify lame animals — a landmark paper that has accumulated 119 citations. Building on this, she refined sensor-based approaches across multiple studies, collectively cited over 200 times, establishing a robust evidence base for non-invasive, real-time lameness monitoring. Her research has also contributed to broader welfare assessment frameworks and informed national hoof health recording systems in Finland. For students and researchers exploring precision animal agriculture, Kujala's body of work represents foundational science in translating automated sensing into actionable animal health insights.