Crossing the Artificial Intelligence (AI) Chasm, Albeit Ising Constrained IoT Edges and Tiny ML, for Creating a Sustainable Food Future
Proceedings of Fifth International Congress on Information and Communication Technology ICICT 2020, London, Volume 2
Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
Big data surrounds us. Every minute, our smartphone collects huge amount of data from geolocations to next clickable item on the e-commerce site. Data has become one of the most important commodities for the individuals and companies. Nevertheless, this data revolution has not touched every economic sector, especially rural economies; e.g., small farmers have largely passed over the data revolution, in the developing countries due to infrastructure and constrained compute environments. Not only this is a huge missed opportunity for the big data companies, but it is one of the significant obstacles in the path toward sustainable food and a huge inhibitor closing economic disparities. The purpose of the paper is to develop a framework to deploy artificial intelligence models in constrained compute environments that enable remote rural areas and small farmers to join the data revolution and start contribution to the digital economy and empower the world through the data to create a sustainable food for our collective future. In a nutshell, close the digital gap by crossing the AI chasm to democratize AI for poor and helpless farmers and help ourselves by creating sustainable food future.
Artificial intelligence, Cow Necklace, Dairy cloud, Edge, Hanumayamma, Hardware constrained model, IoT device, Kalman filter, Small-scale farmers, Tiny ML
Chandrasekar Vuppalapati, Anitha Ilapakurti, Sharat Kedari, Raja Vuppalapati, Jaya Vuppalapati, and Santosh Kedari. "Crossing the Artificial Intelligence (AI) Chasm, Albeit Ising Constrained IoT Edges and Tiny ML, for Creating a Sustainable Food Future" Proceedings of Fifth International Congress on Information and Communication Technology ICICT 2020, London, Volume 2 (2021): 540-553. https://doi.org/10.1007/978-981-15-5859-7_54