Artificial intelligence (AI) can help plant scientists collect and analyze unprecedented volumes of data, which would not be possible using conventional methods. Researchers 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.
Artificial intelligence 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 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."This labor-intensive method isn't viable when thousands or millions of samples are needed.
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.
PlantServation also enabled the scientists to experimentally replicate what happens after the natural speciation of a hybrid polyploid species. These species develop from a duplication of the entire genome of their ancestors, a common type of species diversification in plants. Many wild and cultivated plants such as wheat and coffee originated in this way.
The project is an interdisciplinary collaboration with LPIXEL, a company that specializes in AI image analysis, and Japanese research institutes at Kyoto University and the University of Tokyo, among others, under the Global Strategy and Partnerships Funding Scheme of UZH Global Affairs and the International Leading Research grant program of the Japan Society for the Promotion of Science . The project also received funding from the Swiss National Science Foundation .
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