Photo credit: Lawrence Berkeley National Laboratory
New artificial intelligence technologies promise to ensure food security and sustainability in a warming world.
The tool analyzes roots to track plant growth and biomass, enabling applications related to crop quality and yield, reported Interesting Engineering. The tool was developed by scientists in Lawrence Berkeley National Laboratory's Applied Mathematics and Computational Research Division and Environmental Genomics and Systems Biology Division.
According to a news release about the research published in Scientific Reports, the deep learning backbone and convolutional neural network, called RhizoNet, gets the job done by evaluating small patches of the image.
“This revolutionizes root image analysis, providing precise insights into root behavior under different environmental conditions,” IE reported.
It's a vital tool as scientists around the world work to engineer plants that can withstand the increasingly frequent extreme weather events that are exacerbated primarily by humans' use of polluting energy sources and by temperature spikes caused by the same things.
Bayer, for example, is awaiting full approval of “short corn,” a crop that can withstand the high winds that devastate crops in the central United States and can be planted more densely than conventional corn, increasing yields.
Overseas, researchers in the UK and Vietnam have worked to make rice more salt-tolerant to withstand seawater intrusion.
While new plant techniques could lead to similar breakthroughs, IE reports that RhizoNet is superior to manual methods and is also a step towards developing an automated laboratory.
“By using smaller image patches, the model was able to better capture fine root details, improving accuracy,” the news media said.
According to the statement, the researchers hope that this work will inspire sustainable energy solutions, as well as plant- and microbe-based technologies that capture carbon pollution.
“We have made a significant contribution to reducing the manual labor involved in plant cultivation experiments. [image acquisition system] “EcoBOT, and now RhizoNet, reduces the manual work involved in analyzing the data generated,” Peter Andile, research scientist at EGSB, told IE. “This increases our throughput and brings us closer to our goal of an autonomous driving lab.”
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