“Historically, the most important sensor has been the farmer’s eyes themselves as they observe the physical environment,” says Doug Sauder, Director, Digital Product Management & Analytics at John Deere.
“And what we’re doing with camera technology is really augmenting those human eyes with cameras. We’re augmenting and supporting the human brain with computers, and then we’re augmenting the human hand with robotics. And so those things really come together to give farmers additional tools.”
Speaking on Infinia ML’s Machine Meets World, Sauder says “this is no different than the type of innovation that we’ve been doing for 180 years. It’s just different than the steel plow. Now we’re talking about cutting edge technologies like AI, but it’s all about helping farmers be more productive, more profitable, more sustainable.”
More interview highlights:
The Demand for Agricultural Automation
“Sometimes the conversation about automation gets into, oh, are jobs going to be replaced by robots, those type of conversations. In farming . . . there’s a real labor shortage globally, a shortage in skilled labor. Farmers, our customers, are asking us for more automation. They want the ability for a lower skilled operator to be able to operate a piece of equipment that used to require someone with many years of training.”
“And in addition to that, we’re really talking about automation doing for a farmer what they just can’t do without the technology. And so maybe to, I like to say that we’re helping farmers be better micromanagers. Micromanaging is also a bad word in business, but in farming, it’s a great word. . . . “
“Picture that your job as a farmer is to care for millions of [personal] gardens in a given season. You just can’t do that without technology that can automate and give the precise application of nutrients, the precise placement of seeds.”
Doing the Data Dirty Work
“Your AI strategy has to run on data. It’s easy to get focused on the exciting algorithm that’s going to be developed, the predictive model that’s going to be developed, but it’s the unglamorous work of collecting data, of assessing data quality, of building data pipelines and robust structures that allow for data scientists to get at that data.”
“Often you’ll find that data scientists will spend 70% of their time just wrangling the data to get it into a useful form. And so those investments in that foundational data acquisition and transformation pipelines, that’s really where the initial focus of a company should start. Because if you don’t have that data, you’re going to really struggle to create value on top of it.”
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Machine Meets World is Infinia ML’s weekly interview show with AI leaders.