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Industrial Photonics Extra: Happier Cows and More Milk

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Hank Hogan, Contributing Editor, [email protected]

Contented cows give more milk. So, how do you make a cow — and a farmer concerned about the bottom line — happier?

One answer is to milk photonics technology for all it’s worth. The smart application of sensors, along with automation and other technology, has solved the tricky problem of making a reliable and rugged robotic milking machine.

An example comes from Lely North America Inc., a Pella, Iowa-based subsidiary of a Dutch company. Lely manufactures a robot that can boost milk production by as much as 12 percent over conventional methods, which typically feature milking on a rigid twice-a-day schedule. The increase in productivity is due, in part, to letting each individual cow decide when to be milked.

To automate this process, you need to locate the cow and its teats within the enclosure. Visual sensors make this possible in a cost-effective way.

Take the problem of finding the cow. According to Ben Smink, manager of farm support at Lely North America, at one time this was done using four weight sensors located underneath the deck on which the animal stood. By measuring the weight distribution, the robot figured out where the animal was.

“That was a costly solution,” Smink said. “Now we use a camera to monitor movement.”

The camera sits atop the enclosure and looks down. Thanks to machine vision, the milking robot can find the animal and quickly determine the location of its business end.

When it comes to attaching something to the teats, run-of-the-mill 2-D machine vision won’t do. What’s needed is data in all three dimensions, and the 3-D point cloud has to be dense enough. Otherwise, the robot won’t have the information needed for a successful teat attachment, leading to misses and longer milking times — and, potentially, unhappy cows.

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Generating the critical 3-D data can be done using a laser scanner or a triangulation sensor. The first uses a laser to provide ranging data while the second uses two cameras that generate depth data stereoscopically, much like human eyes do.

A third photonics-based approach involves a time-of-flight camera, which determines distance by measuring how long it takes light to make the trip to an object and back. Time-of-flight camera maker Mesa Imaging AG of Zurich said offers the ability to capture an array of depth data at once. The result, according to the company, is more robust teat detection, with a decreased rate of missed attachments and increased milking capacity.

Mesa has developed application-specific time-of-flight cameras for teat tracking. The 176 × 144-pixel imager sits inside a housing that is ruggedized to withstand conditions in a cow barn. This allows the camera to operate over a wide temperature range, withstand vibration and tolerate exposure to water, dirt and some corrosive chemicals. What’s more, the teat detection and localization routines run on the camera itself, reducing the processing load for the robot controller.

Tens of thousands of robotic milking machines are used daily by millions of cows worldwide. Photonics sensors are making those cows happier, benefiting them and the rest of us.

Related: Sensors Go Up, Down and Around

Published: April 2015
Glossary
machine vision
Machine vision, also known as computer vision or computer sight, refers to the technology that enables machines, typically computers, to interpret and understand visual information from the world, much like the human visual system. It involves the development and application of algorithms and systems that allow machines to acquire, process, analyze, and make decisions based on visual data. Key aspects of machine vision include: Image acquisition: Machine vision systems use various...
time of flight
(TOF) The length of time needed for a signal to arrive at and be reflected from the target. The basis of an active autoranging/autofocus system.
industrialBusinessAmericasEuropeSwitzerlandagricultureLelyIowaBen Sminkmachine visionImagingLaserstime of flightcamerasMesa Imaging

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