Written by Natalie Burg

When Miku Jha talks to farmers about her company’s value proposition, she keeps quiet about exactly what the product is until the finale of her pitch.

“We ask them very simple things,” says Jha, founder and CEO of AgShift, an agricultural technology company focused on food quality assessment and control. “We ask them, ‘What is the sample size of elements you inspect? 500 grams? In how much time? Fifteen minutes? If I give you something where you can inspect 5,000 grams in one minute, does that improve your business?’”

The product is Hydra, a food quality analyzer that uses artificial intelligence (AI) to conduct real-time product inspection. Hydra’s potential to help deliver higher profit margins to farmers while boosting safety and transparency for consumers points to the far-reaching power of AI to significantly improve the agricultural supply chain.

“I can’t think of a better or more noble application of artificial intelligence than into the food supply chain,” says David Roberts, chief innovation officer of the Indiana Economic Development Corporation (IEDC). “It’s tragic that we had a situation where crops were in the fields, hogs were on farms and folks were going hungry during the pandemic.”

The pandemic-related supply chain crisis demonstrates the need for real-time connections between producers and consumers. AI’s potential to close those gaps in the food supply chain  explains why agriculture’s AI market is projected to reach nearly $2.5 billion by 2026. Actual implementation faces its challenges, however. In fact, Jha sometimes holds back mentioning AI to potential customers due to preconceived ideas that its adoption remains a major obstacle for food producers.

Here, we’ll address those assumptions about implementation and explore how AI-powered solutions can benefit efficiency, availability, agility and the overall safety of our food chains.

AI On The Farm

The power of AI to impact the food supply chain touches everything from soil to profits. AI and machine learning (ML) solutions can monitor and optimize irrigation, detect soil conditions, track crop development, evaluate and harvest traditionally hand-harvested crops, project future growing conditions and more. Taranis, a novel precision agriculture technology company, provides unparalleled leaf-level aerial surveillance—relying on AI to deliver granular insights to improve the effectiveness and efficiency of farm management and prevent crop yield loss.

“This industry has a much higher dependency on manual labor for a lot of tasks that are very repetitive and tedious,” Jha says. “We all understand that commodity prices haven’t kept at par with the rising labor costs, which adds increased margin pressures on many of these supply chains.”

Those are enticing opportunities for farmers on the supply side. And the benefits of AI extend to the demand side, too.

“Consumers want to understand how their strawberries were treated,” says Mitch Frazier, president and CEO of AgriNovus Indiana, the state’s partner dedicated to cultivating the agbioscience community. “That’s a really powerful tool, both from a brand perspective [and] from a product perspective, but also from a quality perspective.”

What’s Holding Producers Back?

Before achieving those advantages, farmers must implement the AI solutions and many consider that an overwhelming, or even problematic, project. Some producers are concerned that AI efficiencies come at the expense of industry jobs if machines ultimately replace the traditional work of laborers, for example. That projection isn’t necessarily the case, however: AI could in fact create new industry opportunities.

“When you take out some of those repetitive tasks, you really then have an opportunity to upskill those individuals and have them exercise a broader suite of talents,” says Roberts.

Data management is another concern for growers. Reaping the benefits of AI requires huge amounts of data, which doesn’t always integrate seamlessly with a farm’s existing systems. When AgriNovus leaders recently asked Indiana producers about their farms’ tech challenges, they heard all about it.

“What we found was it wasn’t a machine problem, an ML or AI problem,” says Frazier. “It was actually a data challenge. You’ve got all these different bastions of information. There are systems to integrate those, but those systems at times are cumbersome, clunky and maybe not as trustworthy as you’d hope.”

Solving for this data integration is key. According to Frazier, the real next chapter of innovation in agricultural technology is interoperability—developing a way to deliver data to producers so that they can make smart, strategic decisions for their farms.

Finally, Jha says trust is also a major obstacle. Though evidence suggests that certain AI-based tools increase quality, safety and efficiency, an industry tasked with the responsibility of feeding people—that’s operated for centuries with farmers’ hands in dirt—might be understandably hesitant to embrace change.

“There’s such a big distrust in terms of, ‘Really, your machine can do this for me?’” says Jha. “When the solution you’re providing is fundamentally touching the core business, their bottom line—as a technology company, how do you establish that trust?”

Fortunately, the proof points of AI are strong enough that companies like Jha’s AgShift, collaborating with organizations like AgriNovus and the IEDC, are making swift headway. And like trust, many of implementation barriers are perceived concerns that could gradually subside as more producers observe the benefits of innovative solutions. Ultimately, adopting AI could ultimately help farmers add efficiency and agility to their businesses, while consumers could enjoy increased inventory, food safety and transparency about the origins, compositions and quality of the food on their plates.

 

Source: Forbes