Finding New Productivity
John Deere recently cracked open for a group of visitors the doors to its Intelligent Solutions Group (ISG), in Urbandale, Iowa. Deere's ISG has 2,000 employees worldwide--830 employees in Urbandale--80% of them engineers, and a fair number of them farm. ISG is turning out new software products every 60 days.
We spent time with Mark Chaney, a 23-year Deere veteran who leads product development within ISG's Automation Delivery group. He describes his team's and ISG's mission: reduce complexity, uncover hidden productivity, go faster and maintain precision.
FARMING IS SEEING
So much of farming is visual, Chaney says. One shade of green tells a story different from another shade of green. But, each plant tells a story. "We like to say make every seed, every drop and every kernel produced count," he says. "Our teams move the focus from having to worry about machine optimization to [optimization of] production systems." Deere is deploying computer vision and machine learning technologies to optimize production. "We are developing new sensing, computing and user experience[s] that enable our customers to reach new levels of productivity," he says, to create an environment for "micromanagement ultimately to the plant level."
Two examples of Computer Vision and Machine Learning (CV/ML) already are in the field:
> Active Fill Control for Self Propelled Forage Harvesters automates the loading spout to automatically fill trucks eliminating a tedious operator task.
> Combine Advisor with ActiveVision Cameras uses cameras to feed grain-quality data into an algorithm that maintains loss and grain-quality expectations. It's a capability noticeable as field conditions change. This capability can save $5 to $15 per acre.
"Farmers will be armed with information that they can turn into actionable insights to tend crops at scale like a gardener would tend to each plant in his garden," Chaney says. "Our goal is to reduce this complexity such that it will be [so much] easier to manage and improve crop yields."
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