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Number Seven Thousand Two Hundred and Seventy - 17 April 2023
Iran Daily - Number Seven Thousand Two Hundred and Seventy - 17 April 2023 - Page 7

Secret language of plants: stressed plants speak in ultrasonic clicks

Stressed tomato and tobacco plants emit ultrasonic sounds similar to bubble-wrap popping, which may be detectable by insects, mammals, and other plants. Researchers recorded the sounds and used machine-learning to identify stress types and plant species, offering insights into plant communication and potential agricultural applications.
What does a stressed plant sound like? A bit like bubble-wrap being popped. Researchers report in the journal Cell on March 30 that tomato and tobacco plants that are stressed – from dehydration or having their stems severed – emit sounds that are comparable in volume to normal human conversation. The frequency of these noises is too high for our ears to detect, but they can probably be heard by insects, other mammals, and possibly other plants, according to SciTechDaily.
“Even in a quiet field, there are actually sounds that we don’t hear, and those sounds carry information,” says senior author Lilach Hadany, an evolutionary biologist and theoretician. “There are animals that can hear these sounds, so there is the possibility that a lot of acoustic interaction is occurring.”
Although ultrasonic vibrations have been recorded from plants before, this is the first evidence that they are airborne, a fact that makes them more relevant for other organisms in the environment. “Plants interact with insects and other animals all the time, and many of these organisms use sound for communication, so it would be very suboptimal for plants to not use sound at all,” says Hadany.
The researchers used microphones to record healthy and stressed tomato and tobacco plants, first in a soundproofed acoustic chamber and then in a noisier greenhouse environment. They stressed the plants via two methods: by not watering them for several days and by cutting their stems. After recording the plants, the researchers trained a machine-learning algorithm to differentiate between unstressed plants, thirsty plants, and cut plants.

 

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