Soon you’ll be able to ask an agrirobot: How do you like them apples?

As far as apples go, right now, a robot is simply one more harvester at work. Because even the most advanced robots are bulky and incapable of delicate movement, they’re limited in terms of how far into an orchard they can go and how quickly they can move. Boston Dynamics, the world’s best-known robotics manufacturer, worked 26 years before building one that could open a door. Picking fruit, Steere says, is a much more delicate task: “If you bruise the apple or cut the skin—any type of rough handling ruins the fruit.” So instead of waiting decades for fine motor skills to arrive on the scene, Abundant took a non-traditional approach: The company’s harvester works like a reverse vacuum. It’s a flexible tube on wheels that uses computer vision to analyze an apple, then suck it right up.

A camera on top tells the machine if an apple is ready to be picked (or, in this case, sucked). “The vision has to both recognize fruit and it has to recognize whether it’s ripe or not in a fraction of a second,” says Steere. A Gala, for example, is ripe immediately after its green undertone changes completely to red—a nuance computers must be acutely trained to see. For now, this nuance also limits Abundant to apples. And apples aren’t as simple as they may seem. A ripe Gala, for instance, is red, but a ripe Granny Smith is green. So, each variety needs its own computer recognition process; more than 2,500 are grown in the United States.

Bext360 uses similar technology, but can be more readily cross-applied. The Denver, Colorado-based startup builds sorters that separate good coffee cherries from bad, but can also work on cocoa, nuts, and cardamom. Spokesperson Kim Vu says the company’s even “currently exploring how we can use our technology for the seafood supply chain.”

With coffee cherries, farmers load up to 30 kilograms from one section of the field at a time. (Thirty kilograms of cherries hold approximately 18,000 coffee beans.) CEO Daniel Jones says, “The machine drops it through a visioning system where you can picture a waterfall of cherries falling.” As they drop, the machine takes images of each cherry individually, analyzing color, size, density, defects, and signs of disease. The process takes around three minutes. “We’ll know everything about them in that split second that they fall through,” Jones says.

And that information—essentially a grade—directly affects a farmer’s bottom line. TechCrunch in 2017 covered bext360’s efforts to get small-scale farmers a fair price, and get paid instantly, by using its robot in conjunction with blockchain ledger technology.

“Our technology brings more equity to farmers and more transparency in the supply chain, helping them earn more if they produce higher quality cherries,” Vu adds. “We see the technology only enhancing production and workflow for farmers, instead of replacing these workers.”

As Abundant’s apple harvester won’t replace workers either, both technologies seem to give more to farming than they take, ultimately opening the door to more efficient agricultural production. Steere asks, “Imagine if today people still cut cotton by hand or we still cut wheat by hand. And yet all that fresh fruit you see in the grocery store, most of it’s tended by hand … all these types of agriculture are very likely to be automated in the next few years. We’re at a point in technology where these things are becoming possible.”