Lydiane Bastos
Life Sciences
A school trip to the Science Forest in Manaus sparked a lifelong passion in young Lydiane. It was there that she discovered her calling as a forest engineer. Today, this Amazonian scientist is a driving force in the field, using artificial intelligence to identify the most viable seeds of native Amazonian forest species for biome restoration. Her dedication to this work has been a constant in her academic career: a degree in forestry engineering from the Federal University of Amazonas, a master’s in tropical forest science from the National Institute for Amazonian Research, and a PhD in forestry from the Federal University of Viçosa, an institution she discovered through her love of music while performing with her band.
Lydiane never gives up the bass. Whether rocking out with her punk band or jamming at home, her bass guitar’s deep, scratchy notes are always playing. This love of music is shared with her daughter, who joins her in impromptu jam sessions. Currently, Lydiane continues her vital research at the Amazon Native Seeds Center.
Projects
The success of any forest restoration project depends on the quality of the seeds used. However, for many Amazonian tree species, we lack critical information on seed viability—the ability of a seed to germinate and grow. While healthy-looking seeds with intact seed coats and high germination rates are generally considered viable, more accurate and efficient methods for assessing seed quality are needed. This project will use advanced imaging techniques, including X-rays and scanners, to analyze the internal structure of seeds from at least 200 native Amazonian tree species of economic and ecological importance. We will identify visual patterns associated with seed viability by comparing these images with germination test results. We will train a model to predict seed viability from these images using machine learning algorithms. This innovative approach will significantly reduce the time required to assess seed quality and streamline the seed production chain.
