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If your business is making agricultural equipment, you would be forgiven a wry smile at the ongoing frustrations of automotive original equipment manufacturers (OEMs) as they strive to develop autonomous vehicles. After all, semi-autonomous tractors have been commonplace in your industry for more than 15 years thanks to guidance technologies like real-time kinematic (RTK) GPS and camera-based navigation systems. Entire fields can be mapped – and routes planned and navigated – by a few swipes on a tablet.
That’s all well and good of course, but I’d like to play devil’s advocate and ask you this: why is there still an operator in your tractor and what’s stopping broad acre farming from being truly autonomous? The answers to these fundamental questions, I would suggest, can be found in closing the loop – and automating the processes that rely on operator judgment and skill.
Myself and my associates at Cambridge Consultants have spoken at length with large corporations and individual farmers alike. We have attended conferences and viewed reels of YouTube videos to identify the recurring problems faced by farmers, even when operating state-of-the-art equipment. Universally, a potential overarching solution to many of the issues arises… closed loop control.
Closed-Loop Control
A control loop takes a measurement, compares it to a target, and then attempts to minimize the error by varying an output. Control loops are everywhere – cruise control in our cars, voltage control in our smartphone chargers and temperature regulation in our office air conditioners. There is even a control system at play when we sip our way through a coffee, as we don’t tend to miss our mouth despite the changing cup weight. Human brains are running control loops all the time.
Level 5 Autonomy
Why is closed-loop control relevant to farming? Simply put, many of today’s agricultural tasks rely on the skill of the operator to get them just right. The ability to ‘know what good looks like’ and what to adjust to ensure it is achieved is something experienced farmers take for granted. Yet it is this expert knowledge that is inadvertently hindering the move to full, Level 5 Autonomy. So, before the marketing dream of the cab-less tractor can become reality, we have to nail the automation of the processes that rely on operator input.
… why is there still an operator in your tractor and what’s stopping broad acre farming from being truly autonomous?
Autonomous Real-Time Solutions
This particular article focuses on the specific challenges faced during plowing– and how closed-loop control could potentially be used to provide autonomous, real-time solutions.
Let us start with furrow width. The operator must ensure the top link is aligned and horizontal to the tractor. If it is not, after swapping over, one side will plow narrow and the other will plow wide. They must also check that the front furrow is plowing at the same width as the other furrows, and that this is the same as the set width.
Now furrow depth. The operator looks closely for even amounts of soil on the mud boards to ensure furrow uniformity. They must ensure that the skimmers are run deep enough so that the trash is cut and buried under the furrow – and must ensure that the back furrow before a turn is the same as the front furrow after a turn. Once again, the furrow depth should be manually checked against the set point.
Finally, taking the plow in and out. When dropping the plow back in, the operator wants the plow to drop and to touch the floor just before the wheel drops in the trough. When taking it out, they must time it so that the troughs are filled in.
Visual Feedback
All of these steps involve a visual feedback loop. But with appropriate instrumentation – machine vision, force transducers, proximity sensors for example – the desired properties and their spatial variation can be quantified and fed back to the control system of the tractor. This allows for real-time changes in speed, plow depth, angle and so on.
The True Potential of Precision Agriculture
With greater data collection, understanding of cause and effect, and perhaps even elements of deep learning to build on the collective wisdom of thousands of farmers, closed loop systems could correct non-optimal processes before the inconsistencies are even visible. Such systems could enable closed loop control to deliver on the promise of true autonomy on the farm, freeing up precious time for the farmer and delivering greater yield and consistency of outcome across the farm. It holds the keys to the true potential of precision agriculture.
Editor’s Note: This article was republished (with slight alterations) with permission from Cambridge Consultants. The original can be found HERE.
About the Author
Rishi Jobanputra, Senior Fluids and Mechanical Engineer, Cambridge Consultants
Rishi Jobanputra is currently a Senior Fluids and Mechanical Engineer at Cambridge Consultants, a global product development and technology consultancy. Since joining the company in 2017, he has led multiple analytical and experimental workstreams in fluidics, mechanics and materials engineering. He has worked across multiple industries including Consumer Products, Sustainability, Industrial Equipment, Agritech and Cell Therapy. Jobanputra graduated with a Distinction in MEng Engineering from the University of Cambridge.
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