The Innovation Lifecycle of AI-Driven Agriculture: Causal Dynamics in University-Industry-Research Collaboration

Considering the current prediction from the Food and Agriculture Organization, food production needs an increase of over 70 percent by 2050, agriculture sector requires a boost that is also obtained by integrating novelty technologies and methods of artificial intelligence (AI) field. The current research explores the innovation lifecycle of AI-driven agriculture field through a causal inference analysis within University-Industry-Research ecosystem, extracting four key pillars: scientific research papers, research projects, patents and startups. Using an extensive time-series database, Granger causality analysis is applied for uncovering prospective causal relationships that guide the interest in innovation within AI-driven agriculture. Our results indicate an overall increase in AI applied agriculture domain within all four pillars starting from the 2010 year for start-ups pillar and impacting in Granger point of view all the way to 2020 for patents perspective. The findings suggest a potential sequential innovation pathway where progress in one pillar propels advancements in the next, according this flow: startups, projects, scientific research and patents. The implications of this study are significant, providing insights that could guide strategic planning and investment in AI applications in agriculture. By understanding the potential causality and sequentially in innovation, policymakers, investors, and entrepreneurs can better align their efforts with the most impactful areas. This research not only advances in academic knowledge but also provides practical insights that influence real-world applications and practices in agricultural technology.