FIELD NOTES: THE FUTURE IS NOW

Deere X9 Combine, photo courtesy of johndeere.com

Emerging Technologies Offer Exciting Possibilities for Agriculture BY DUSTY SONNENBERG, AGCREDIT BOARD MEMBER

It’s been said the only thing constant is change. That’s as true today in the agriculture industry as it has been at any time in history. The climate is changing. Technology is changing. Farming as we know it is changing. Once romantically visualized as a red barn or a golden field of wheat gently waving in the wind, agriculture now includes GPS satellites, drones, machines with artificial intelligence (AI) and training-neutral networks.

As weather patterns continue to change, farming practices are adapting and incorporating new technologies. According to Aaron Wilson, Atmospheric Scientist at The Ohio State University, “Ohio has had an average of five fewer days fit for planting and five fewer days fit for harvest than it did 20 years ago.” DUSTY SONNENBERG

“With narrower windows of opportunity in the spring and fall, and frequently less-than-desirable field conditions when the weather does allow opportunities, figuring out how to run under marginal conditions will be crucial,” said Dr. Scott Shearer, Chair of the Department of Food, Agricultural and Biological Engineering at the Ohio State University.

Agriculture is getting bigger and smaller — at the same time.

Today’s Class 9 combines are the largest machines on the market, and every major manufacturer offers one. For example, the largest John Deere combine is the S790, which features a 543-horsepower engine and a 400-bushel grain tank. It can be equipped with the largest combine header in the world, the MidWest Durus Premium 60-foot header.

In late 2019, John Deere introduced its new X9 twin rotor combine, which is considered to be a Class 10 machine. This machine features the widest frame on the market and an engine with well over 500 horsepower. Its twin rotors can harvest over 100 tons of small grains in an hour with losses of less than 1%. That’s over 3,300 bushels of wheat an hour.

To put this in perspective, the same John Deere combine frame was used for all of the Class 7, 8 and 9 machines. If this trend continues, the new X9 frame could potentially be used on future Class 10, 11 and 12 machines. What would a Class 12 machine be capable of?

“A Class 12 combine would possibly be a machine with a 750-horsepower or higher motor and harvest capacity of 10,000 bushels per hour,” Shearer said. “The question to ask then becomes how to get the grain away from a machine with that much harvest capacity?”

Agriculture is also getting smaller.

“As we move into the future, smaller and lighter equipment will become necessary in some situations,” Shearer said. “Farmers may need to change their picture of what a tractor or piece of farm equipment looks like. It may be more of a metal framework with electric motors to hang technology from. Smaller equipment allows more options. The effective working width of a machine could go back to 10 feet.”

The Australian company Swarm Farm offers a compelling look at how small autonomous agricultural equipment is being deployed. The firm’s SwarmBots are small, lightweight, nimble, autonomous robotic “platforms” that conduct farming practices in groups, or “swarms,” of machines to tackle larger acreages. This technology is now being used for crop protection application and mowing.

Several major farm equipment manufacturers are also creating similar AI technologies. John Deere is developing an autonomous sprayer with electric motors. In 2017, John Deere purchased Blue River Technology, which has developed “see and spray” technology that can detect, identify and make management decisions about every plant in the field. This is an example of a training-neutral network. “Training-neutral networks have become a reality in agriculture as we now have machines with the computer power to perform the data collection and processing,” said Shearer.

Training-neutral networks involve using a series of the data points collected along with modeling to create a mapping of inputs to outputs. The training, or machine learning, process is solved using an optimization algorithm that searches through a set of possible values to determine a more efficient model that will result in the optimized performance. AI incorporates training-neutral networks that combine forecasting, data validation and managing risk. This technology could become another tool in the farmer’s toolbox to manage herbicide resistant weeds.