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plant science Archives - Page 19 of 91 - The Global Plant Council

Rafflesia banaoana. Credit: Chris Thorogood

Researchers issue urgent call to save the world’s largest flower -Rafflesia – from extinction

By | Botany, News, Plant Science

An international group of scientists has issued an urgent call for coordinated action to save the iconic genus Rafflesia, which contains the world’s largest flowers. This follows a new study which found that most of the 42 species are severely threatened, yet just one of these is listed in the International Union for Conservation of Nature (IUCN)’s Red List of Threatened Species. Furthermore, over two thirds (67%) of the plants’ habitats are unprotected and at risk of destruction.

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Microscope image of one of the closest algal relatives of land plants, a single-celled alga called Mesotaenium endlicherianum (20 micrometres corresponds to 0.02 millimetres). Credit: Tatyana Darienko

Algae provide clues about 600 million years of plant evolution

By | News, Plant Science

The Earth’s surface is covered by plants. They make up the majority of biomass on land and exhibit a wide range of diversity, from mosses to trees. This astounding biodiversity came into existence due to a fateful evolutionary event that happened just once: plant terrestrialization. This describes the point where one group of algae, whose modern descendants can still be studied in the lab, evolved into plants and invaded land around the world. An international group of researchers generated large scale gene expression data to investigate the molecular networks that operate in one of the closest algal relatives of land plants, a humble single-celled alga called Mesotaenium endlicherianum.

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Image: Rice field image. Credit: Pixabay

Artificial Intelligence Can Now Estimate Rice Yields, According to New Study

By | Agriculture, News, Plant Science

The global demand for rice is projected to rise significantly by 2050, necessitating sustainable intensification of existing croplands. Now, researchers have made significant progress by developing deep-learning algorithms that can rapidly estimate rice yield through the analysis of thousands of photographs. The model exhibited high precision across diverse conditions and cultivars, surpassing previous methods, while effectively detecting yield differences between cultivars and also with different water management practices.

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