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Scientists announce breakthrough AI technology that could transform how we grow food: 'We've made a lot of progress'

"This increases our throughput and moves us toward the goal of self-driving labs."

"This increases our throughput and moves us toward the goal of self-driving labs."

Photo Credit: Lawrence Berkeley National Laboratory

A new artificial intelligence technology promises to ensure food security and sustainability in our warming world.

The tool analyzes roots to help track plant growth and biomass, allowing for applications related to crop quality and yields, Interesting Engineering reported. It was developed by scientists from Lawrence Berkeley National Laboratory's Applied Mathematics and Computational Research and Environmental Genomics and Systems Biology Divisions.

Called RhizoNet, the deep-learning backbone and convolutional neural network gets the job done by evaluating small patches of images, per a news release about the study, which was published in Scientific Reports.

"It revolutionizes root image analysis, offering precise insights into root behavior under various environmental conditions," IE reported.

It's an essential tool, as scientists the world over work to engineer plants that can survive extreme weather events made worse and more frequent mostly by humans' use of dirty energy sources as well as rapidly rising temperatures with the same cause.

Bayer, for example, is awaiting full approval of "short corn," which can withstand strong winds that wreak havoc on crops in the middle of the U.S. and can be planted more densely than traditional corn, increasing yields.

Overseas, researchers from England and Vietnam worked to make rice more salt-resistant in an effort to combat seawater intrusion.

The new plant technology could result in similar breakthroughs, though it's also a step toward developing automated laboratories, per IE, which reported that RhizoNet outperformed manual methods. 

"By using smaller image patches, the model captured fine root details better and improved its accuracy," the news outlet stated.

The researchers hope the study sparks sustainable energy solutions in addition to plant- and microbe-based technology for capturing carbon pollution, according to the release.

"We've made a lot of progress in reducing the manual work involved in plant cultivation experiments with the [image acquisition system] EcoBOT, and now RhizoNet is reducing the manual work involved in analyzing the data generated," EGSB research scientist Peter Andeer said, per IE. "This increases our throughput and moves us toward the goal of self-driving labs."

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