Vamsi Krishna Kommineni


My Research Project


Leaf traits are important and often used to understand the plant and functional diversity but the numbers of leaf trait values are still strongly limited in space and time. To overcome the leaf trait data limitations, interdisciplinary research is required, in my PhD research we mainly concentrate on automatic extraction of leaf trait related information for around 15 million Digital Herbarium Specimen (DHS) images using deep neural networks. In the second step, we focus on building an intelligent machine learning system and analyzing intra- and interspecific leaf trait variation in space and time.

Short CV


11/2020 - nowDoctoral researcher at the Functional Biogeography group (Max Planck Institute for Biogeochemistry, Jena)
04/2019 - 09/2020

Master thesis and research assistant: Identifying drivers of intraspecific leaf trait variation in space and time from digitized herbarium specimen using machine learning approaches. (Max Planck Institute for Biogeochemistry, Jena)

04/2018 - 08/2018Master internship: Reconstruction of noisy signals using compressed sensing and Fourier transformation with python. (Fachhochschule Jena)



Kommineni, V. K., J. Kattge, J. Gaikwad, P. Baddam, S. Tautenhahn

(2020): Understanding Intraspecific Trait Variability Using Digital Herbarium Specimen Images. Biodiversity Information Science and Standards
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Max Planck Institute for Biogeochemistry
Hans-Knöll-Straße 10
07745 Jena

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Max-Planck-Institut für Biogeochemie

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