This robot can sort recycling by giving it a squeeze

The robot, called RoCycle, uses pincers to pick through garbage and identify what materials each bit contains. It could help reduce how much waste gets sent to landfill.

Image of recycling robot sorting various items into bins
JASON DORFMAN, MIT CSAIL

Greasy pizza box, takeaway coffee cup, plastic yogurt pot—are they trash or recycling? What can and can’t be recycled is often confusing, not least because the answer depends on the facilities at your local waste processing plant. In many plants, grease-soaked cardboard or cups lined with polyethylene cannot be recycled and thus head for landfill—often taking a batch of other recycling with them.

One US waste processing company has reported that 25% of all recycling it receives is so contaminated it must be sent straight to landfills. Meanwhile, the amount of household waste rejected for recycling in England increased by 84% between 2011-2012 and 2014-2015, according to government figures. And it’s about to get worse. Much of the world’s waste is sold to China for recycling. But last month China introduced stricter standards for the amount of contamination it will accept: anything more that 0.5% impure will go in the ground.

That’s why the way we sort waste needs to get much better. Many large recycling centers already use magnets to pull out metals, and air filters to separate paper from heavier plastics. Even so, most sorting is still done by hand. It’s dirty and dangerous work.

So Lillian Chin and her colleagues at the Computer Science and Artificial Intelligence Lab at MIT have developed a robot arm with soft grippers that picks up objects from a conveyor belt and identifies what they are made from by touch. MORE

Machine learning is making pesto even more delicious

Researchers at MIT have used AI to improve the flavor of basil. It’s part of a trend that is seeing artificial intelligence revolutionize farming.

What makes basil so good? In some cases, it’s AI.

Machine learning has been used to create basil plants that are extra-delicious. While we sadly cannot report firsthand on the herb’s taste, the effort reflects a broader trend that involves using data science and machine learning to improve agriculture.

The researchers behind the AI-optimized basil used machine learning to determine the growing conditions that would maximize the concentration of the volatile compounds responsible for basil’s flavor. The study appears in the journal PLOS One today.

The basil was grown in hydroponic units within modified shipping containers in Middleton, Massachusetts. Temperature, light, humidity, and other environmental factors inside the containers could be controlled automatically. The researchers tested the taste of the plants by looking for certain compounds using gas chromatography and mass spectrometry. And they fed the resulting data into machine-learning algorithms developed at MIT and a company called Cognizant.

The idea of using machine learning to optimize plant yield and properties is rapidly taking off in agriculture. Last year, Wageningen University in the Netherlands organized an “Autonomous Greenhouse” contest, in which different teams competed to develop algorithms that increased the yield of cucumber plants while minimizing the resources required. They worked with greenhouses where a variety of factors are controlled by computer systems.

Similar technology is already being applied in some commercial farms, says Naveen Singla, who leads a data science team focused on crops at Bayer, a German multinational that acquired Monsanto last year. “Flavor is one of the areas where we are heavily using machine learning—to understand the flavor of different vegetables,” he says.  MORE