Tag: plants

Abstract: Purpose: Root system architectures are complex, multidimensional, and challenging to characterize effectively for agronomic and ecological discovery. Methods: We propose a new method, Spatial and Texture Analysis of Root System architecture with Earth mover’s Distance (STARSEED), for comparing root architectures that incorporate spatial information through a novel application of the Earth Mover’s Distance (EMD). […]

Congratulations to our labmate, Xiaolei Guo, for passing her Oral Qualifying Exam and becoming a PhD candidate!  For the remainder of her PhD work, Xiaolei plans to investigate fundamental research questions on “Interactive Segmentation with Deep Metric Learning”. We are excited to see what comes from her work! Great job, Xiaolei!

  Dr. Alina Zare recently presented in the University of Florida Informatics Institute’s virtual seminar on AI Advances and Applications. During her talk, Alina discussed how the Machine Learning and Sensing Lab is using AI methods to advance the understanding of plant root systems.   Check out our Publications page for more info on the exciting […]

In response to the recent AI initiative launched by the University of Florida, the UF Informatics Institute (UFII) is hosting a virtual seminar series, “AI Advances and Applications”.  The online series will feature innovative work being conducted in AI and Machine Learning across the university, and will include a talk by Alina Zare. Sessions will […]

Congratulations to our labmates and collaborators Pamela S. Soltis, Gil Nelson, Alina Zare and Emily K. Meineke!    They wrote the introduction for a special issue of Applicaitons in Plant Sciences.  Their introduction is titled “Plants meet machines: Prospects in machine learning for plant biology”, and it discusses the ways machine learning is being applied […]

Abstract: Machine learning approaches are affecting all aspects of modern society, from autocorrect applications on cell phones to self‐driving cars to facial recognition, personalized medicine, and precision agriculture. Although machine learning has a long history, drastic improvements in these application areas recently have been driven by improvements to computational infrastructure; increased computing power; increased ability […]

Congratulations to our labmates and collaborators Guohao Yu, Alina Zare, Hudanyun Sheng, Roser Matamala, Joel Reyes-Cabrera, Felix Fritschi and Thomas Juenger! Their paper, “Root Identification in Minirhizotron Imagery with Multiple Instance Learning”, was recently published in Machine Vision and Applications! Their paper explores the use of multiple instance learning to segment minirhizotron images of plant […]

Congratulations to our labmates, Weihuang Xu, Guohao Yu and Alina Zare, as well as collaborators Brenden Zurweller, Diane Rowland, Joel Reyes-Cabrera, Felix Fritschi, Roser Matamala and Thomas Juenger!  Their paper, “Overcoming Small Minirhizotron Datasets Using Transfer Learning”, was published in Computers and Electronics in Agriculture.   The document and code can be found here.  Make sure […]

Congratulations to our labmates Guohao Yu, Alina Zare and Hudanyun Sheng, as well as collaborators, Roser Matamala, Joel Reyes-Cabrera, Felix Fritschi and Thomas Juenger! Their paper, “Root Identification in Minirhizotron Imagery with Multiple Instance Learning”, was recently accepted to Machine Vision and Applications! Their paper explores the use of multiple instance learning to segment minirhizotron […]

Congratulations to our labmates, Weihuang Xu, Guohao Yu and Alina Zare, as well as collaborators Brenden Zurweller, Diane Rowland, Joel Reyes-Cabrera, Felix Fritschi, Roser Matamala and Thomas Juenger!  Their paper, titled “Overcoming Small Minirhizotron Datasets Using Transfer Learning”, was recently accepted to Computers and Electronics in Agriculture.  Check it out here!