Scientists are turning to AI to advance sustainable agriculture. According to a writeup from the Chinese Academy of Sciences shared by Phys.org, a team of researchers in China has developed a framework for integrating artificial intelligence into biotechnology with the goal of combating global food insecurity.
Researchers from multiple universities, including professor Gao Caixia from the Institute of Genetics and Developmental Biology of the Chinese Academy of Sciences and professor Li Guotian of Huazhong Agricultural University, among other collaborators, teamed up to improve crop breeding. Published in Nature, their findings outline a method for applying AI to genetic modification in crops.
"We suggest that integrating these technologies with agricultural practices will lead to a new revolution in crop improvement, contributing to global food security in a sustainable manner," the study authors wrote.
With the help of AI-driven protein design as well as genome editing technologies, researchers can breed crops that are more drought and disease-resistant.
"The review particularly focuses on the proposal for an integrative 'AI-assisted crop design' model that would use AI to analyze multimodal big data from genomes, phenotypes, environments, and agricultural practices," the writeup explained, per Phys.org.
These crop modifications ultimately make the agricultural industry more resilient to extreme weather and rising global temperatures, helping to protect global food supplies.
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While the researchers offer a potential track for advancing sustainable agriculture, it could come at an environmental cost. AI uses a massive amount of electricity and water to perform functions, generating a significant amount of pollution. What's worse, AI's environmental impact is only projected to grow, as the International Energy Agency estimates that by 2026, AI data centers' electricity usage will be that of a country the size of Japan.
Though AI offers ways to optimize crop management frameworks, it's important to consider the environmental challenges associated with AI-backed solutions. Moving forward, the study's authors recognize there are still obstacles that need to be addressed with their AI-backed framework.
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