Artificial intelligence (AI) can help plant scientists collect and analyze unprecedented volumes of data, which would not be possible using conventional methods. Researchers at the University of Zurich (UZH) have now used big data, machine learning and field observations in the university's experimental garden to show how plants respond to changes in the environment.
Climate change is making it increasingly important to know how plants can survive and thrive in a changing environment. Conventional experiments in the lab have shown that plants accumulate pigments in response to environmental factors. To date, such measurements were made by taking samples, which required a part of the plant to be removed and thus damaged.
With the support of UZH's University Research Priority Program "Evolution in Action," a team of researchers has now developed a method that enables scientists to observe plants in nature with great precision. PlantServation is a method that incorporates robust image-acquisition hardware and deep learning-based software to analyze field images, and it works in any kind of weather.
In the current study, the anthocyanin content of the hybrid polyploid species A. kamchatica resembled that of its two ancestors: from fall to winter its anthocyanin content was similar to that of the ancestor species originating from a warm region, and from winter to spring it resembled the other species from a colder region.
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