Making Sense of the Data: AI Technology for Strategic Global Food Production
Alex Whitley discusses the use of AI in global food production.
In the global food production world, there is no end to the available data to inform crop health, yield and environmental impact.
But deciphering that data into executable processes can be a different story.
Alex Whitley, Managing Director at Arva Intelligence, believes that AI is still in the early stages of aiding agriculture, and his company’s mission is to find new ways to make agricultural production better for small and large factory farming operations.
“Today, there are so many data points to collect, and every new piece of technology coming out introduces a new data point. It is really hard to have the expertise and computational power to make use of that in these decisions at scale,” Whitley said.
One of the biggest obstacles for using all of these agriculture data points to inform strategies on a scalable level is the sheer volume of data layers generated by the sophisticated farming technology.
“The more data layers you have, the less scalable it becomes until that same level of innovation going into the technology gets applied to the data problem,” he said.
Whitley said he feels agriculture is still in the beginning stages of utilizing AI and machine learning in a scalable way. Most farmers want to be in the business of farming, not analyzing data, and, recognizing that, Arva’s mission is to figure out the efficiencies the right data and analysis can produce, then provide the resulting information and action items back to the farmers.
“In what we do, we do not require any cultural or practice change from the farmer,” Whitley said. “We are just supplying the people who provide the farmers the raw goods with the information and what to supply them. The farmers go through the same process, but now they can purchase the right seed for the right area.”
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