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Soil Carbon Effectively Measured by New, Efficient AI Model

By Krishna Ramanujan

A new computer model is one of the first artificial intelligence tools to advance scientific discovery in agriculture and biogeochemistry and is 50 times more efficient than its predecessors, according to a study that offers a proof-of-principle for how AI might be used to shed light on obscure biological processes.

In a paper published in the journal Geoscientific Model Development, the researchers demonstrated the AI on processes behind the issue of soil organic carbon, as Earth's soils hold roughly three-quarters of the world's terrestrial carbon and more carbon than the atmosphere and all the world's plants combined.

Scientists have been exploring ways to use AI for research purposes, but most common AI tools, such as ChatGPT, mainly repurpose existing information. Researchers have also used AI to extract patterns from data. But the new model, called the Biogeochemistry-Informed Neural Network (BINN), goes a step further by predicting biological processes that are not yet well understood and suggesting factors that control them.

"BINN is very easy to use and can be democratized among the scientific community in various disciplines," said Yiqi Luo, the Liberty Hyde Bailey Professor in the School of Integrative Plant Science, Soil and Crop Sciences Section, in the College of Agriculture and Life Sciences (CALS) and a senior author of the study. "This is one of the first tools of this type that can promote scientific research with AI."

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