Digging into Big Data to Speed up Food Production
One of the United Nations Sustainable Development Goals (SDGs) aims to end hunger, achieve food security and improved nutrition and promote sustainable agriculture by the year 2030. There are in total 17 SDGs that are directly or indirectly connected to sustainable and healthy food, according to the Stockholm Resilience Center.
Food production is becoming a pressing problem with the food shortage looming, exacerbated by the current devastating war, requiring to dig into big data and speed up food production. Big data and AI are powerful tools and are currently being used across sectors but are less used in production and global food security.
In an article on “Big Data’s Big Role in Agriculture” by Stephen DeAngelis, Dr. Glen Plastow, a professor at the University of Alberta, stated:
“Making sense of the data takes a lot of analytical and computing power. Converting this data into information, and understanding what it really means, we will be able to make faster genetic improvements to improve food production [and] reduce the time to market.”
Despite its expansion, the impact of AI on production operations is very limited. This paradox has also been noted by MIT, which reports that these new processing technologies are ubiquitous, except in production statistics. The problem affects almost all countries, including many large emerging economies.
We have used big data in combination with ML/AI that helped to speed up the search for adaptive traits in crops, leading the identification of several traits that are used in the development of portfolios of new crop varieties with adaptive traits within a short period of time.
AI has the potential to help speed up food production and catch with the looming food shortage.
