Lamb-succory (arnoseris minima), davall’s sedge (carex davalliana) and red helleborine (cephalanthera rubra) are plants, native to the United Kingdom, that are endangered or already extinct1.
The disappearances of these species might seem inconsequential in the grand scheme of things, but they’re part of a global trend: A decrease in plant (and animal) biodiversity. Biodiversity is a critical component of the survival of any ecosystem. The variety of traits found in each plant (like a resistance to a certain type of insect, or prone to wilting) are critical to resilience of all species against shocks and stresses — whether it be the arrival of invasive species, a natural disaster event or even climate change.
Luckily, the growing availability of data storage and increasingly sophisticated machine learning techniques might be able to help.
“We have the ability to actually store data not just data about the plants, but we have data around planting them, around insect patterns, around pollutants,” says Janet George, Chief Data Officer at Western Digital. “And then we can apply neural networks to observe patterns, and to then predict what will happen if the temperature rises to a certain level, or if we can prevent it rising by two degrees—what would be the impact?”
George says this ability to store, process and analyze plant data is critical not only for scientists to understand how to combat the failing biodiversity in plants, but also to raise awareness around the issue in general — addressing what’s known as “plant blindness2,” or humanity’s inability to appreciate the economic and ecological importance of plant life. Still, collecting plant data is no easy task considering the sheer number and variety of plants. Here’s a closer look at the growth of plant data and how it can be processed and leveraged in machine learning models.
This content is produced by WIRED Brand Lab in collaboration with Western Digital Corporation.
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- Extreme Botany: The Precarious Science of Endangered Rare Plants