By: Association of Equipment Manufacturers

Agriculture is among the last major industries to become digitized. It’s doesn’t come as a major surprise, seeing as how off-road, rural environments are more challenging than roadway systems or manufacturing floors.

However, as the connectivity gap continues to close, there is tremendous opportunity to capture data that can ultimately lead to transformative technologies like artificial intelligence (AI).

“To put it as simply as possible, AI allows computer systems to complete tasks that are normally performed by humans,” said Mark Kuehn, OEM sales manager for North America at Trimble.

Given that definition, AI could mean everything from cognitive tasks like data analytics and forecasting to physical tasks like spraying weeds and picking produce. As presented in AEM’s whitepaper, The Future of Food Production, examples already exist that reinforce the positive impacts AI can have. For instance, robots utilizing machine learning can detect and pick harvestable fruit in a fraction of the time a human can.

One Step at a Time

It’s important to remember, though, that the road to AI and complete machine autonomy is a long one.

“Completely replacing a human is pretty hard to do - today,” said Michael Gomes, vice president of business development for Agriculture at Topcon. Along the journey toward AI, several important steps can be taken that can have a profound effect on the way food is produced.

In the on-road world of automobiles, Gomes said that industry has outlined five levels of autonomy. Each level gains additional elements of autonomous operation until Level 5 where no human interaction is needed.

“In the off-road industry... the first step is mechanization," said Gomes. "Next is some form of automation, which much of the ag equipment industry has already been doing. Then the real opportunity emerges: an agricultural system comprised of smart, connected products."

“In looking at the current state in agriculture, we’re still in the early stages of even understanding what the potential could be,” Kuehn said. “Technologies are still being developed that enable a more complete understanding of what is happening in the field. Data not only has to be captured, but also processed into something digestible, enabling machines to ultimately carry out tasks by themselves.”

Said Gomes, “Smart, connected devices with some amount of machine learning can become as smart as a dog today.” Dogs can understand hand and verbal commands. Dogs can learn to go outside to go to the bathroom. Dogs know when they’ve done something right or wrong.

How Industry Can Prepare for the Journey

Gomes said ag equipment manufacturers must first identify where they currently are in the journey toward AI, and then decide where they ultimately want to go.

Kuehn noted that it’s important to work closely with companies developing these technologies to ensure that equipment will be capable of carrying out the desired tasks.

As for the end-users, American farmers, Kuehn says a small percentage are utilizing the tech tools that are currently available. That will begin to change as rural access to high-speed internet improves and the next generation of farmers begins to establish itself.

“For those who are interested in things like machine learning and AI, it’s important to stay informed about what is being developed,” Kuehn said. “Talk with manufacturers and dealers about the options that are available. Precision agriculture technology providers can also help farmers understand what is available and what is coming.”

Finally, Kuehn and Gomes agree that it’s important for farmers to work with trusted advisors — advisors who are forward-thinkers and can help farmers navigate the fast-moving arena of technology and machine learning.

Telling the story of AI’s potential in agriculture is another key element of enabling the transformation over the next decade.

“In addition to increased collaboration, the industry must continue to encourage the government to assist producers who want to acquire this kind of technology,” Kuehn said. “There are already some government programs that can help, and we can learn from various programs that have been successful in other countries. This is an area where we can continue to improve. We can also continue to improve in the area of factory installation of precision agriculture technologies.”

The Future is Coming into Focus

The story of AI and efficiency optimization in agriculture becoming increasingly clear with time.

“As the population and food demand increase, the agriculture industry must find ways to adapt,” Kuhn said. “The adaptation needs to allow farmers to grow more food with fewer resources, including people. AI is one of the core pieces to grow food more sustainably. Sensors can help identify which parts of a field actually require application. Water use is another huge driver. Moisture sensors in the ground can identify when a plant is reaching a wilting point and actually needs water.”

According to Gomes, technology is a lot like fashion in that it is always changing, and sometimes the change can cause a person to become a bit apprehensive. “Nobody likes change, but most people do like progress,” he continued. “The difference is when change has a purpose.”

The purpose of transformational technologies like connectivity, sensors, machine learning and AI is very clear in the agriculture industry: enable farmers to fulfill higher production needs while reducing their environmental impact.

Sure, a little apprehension might come along with that. But so should a great deal of enthusiasm.

About the Association of Equipment Manufacturers (AEM)

AEM is the North America-based international trade group representing off-road equipment manufacturers and suppliers with more than 1,000 companies and more than 200 product lines in the agriculture and construction-related industry sectors worldwide. The equipment manufacturing industry in the United States supports 2.8 million jobs and contributes roughly $288 billion to the economy every year.

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