Miel Hostens
KEYNOTE
How AI can transform the entire agricultural industry, or not
Miel Hostens
Cornell University, Ithaca NY, USA
ABSTRACT
The integration of Artificial Intelligence (AI) into the agricultural industry holds the potential to revolutionize farming practices, enhance sustainability, and improve overall productivity. AI-driven technologies, such as computer vision (CV) algorithms and precision livestock farming techniques, are being deployed to monitor animal welfare, detect health issues, optimize farm management practices ultimately creating a more sustainable agriculture.
However, the full potential of AI in agriculture is yet to be realized due to several challenges. The horizontal scattering of data across different research and business organizations introduces difficulties in performing integrated analytics. Moreover, the lack of comprehensive databases that integrate continuous, real-time data collected from scientific facilities hinders the establishment of benchmarks for other studies. Despite these challenges, the fusion of AI and animal & dairy science presents numerous opportunities, particularly in reducing emissions from livestock and enhancing the accuracy and reliability of generative AI models.
In conclusion, while AI has the potential to transform the agricultural industry significantly, its success depends on overcoming data integration challenges and developing robust, scalable AI models that can operate effectively in diverse farm settings. The ongoing research and development efforts in AI-driven agriculture are promising, but the journey towards full transformation requires continued innovation and collaboration across various sectors.
SPEAKER BIOGRAPHY
Miel Hostens has an MSc in Veterinary Medicine at the Ghent University (Belgium) in 2006 and a PhD in Veterinary Medicine in 2012. He also obtained a professional certificate in Epidemiology and Bioinformatics and to further deepen his data science expertise, he enrolled in a MSc in Computational Statistics in 2016. He has attracted multiple projects on data-driven agriculture and precision dairy farming overseas. The unique combination of data and dairy science in an academic profile was the main reason for attracting him as Associate Professor of Digital Dairy Management and Data Analytics at the Department of Animal Science at Cornell University. His lab focuses on developing a data-driven dairy science eventually contributing to sustainable food production systems from a global perspective.
