Tag: roots

Congratulations to our labmates and collaborators: Guohao Yu, Alina Zare, Weihuang Xu, Roser Matamala, Joel Reyes-Cabrera, Felix B. Fritschi and Thomas E. Juenger!  Their paper, “Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM” was recently accepted to the 16th European Conference on Computer Vision (ECCV) Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP 2020).   Their […]

Abstract: We present a multiple instance learning class activation map (MIL-CAM) approach for pixel-level minirhizotron image segmentation given weak image-level labels. Minirhizotrons are used to image plant roots in situ. Minirhizotron imagery is often composed of soil containing a few long and thin root objects of small diameter. The roots prove to be challenging for […]

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 Jose Ruiz-Munoz and Alina Zare as well as collaborators Jyothier Nimmagadda, Tyler Dowd and James Baciak!  Their paper, titled “Super Resolution for Root Imaging”, was recently published to Applications in Plant Sciences (APPS). If you’re interested in learning about a super-resolution framework for enhancing images of plant roots by using convolutional […]

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 Jose Ruiz-Munoz and Alina Zare as well as collaborators Jyothier Nimmagadda, Tyler Dowd and James Baciak!  Their paper, titled “Super Resolution for Root Imaging”, was recently accepted to Applications in Plant Sciences (APP). If you’re interested in learning about a super-resolution framework for enhancing images of plant roots by using convolutional […]

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!

Abstract: High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes. However, the acquisition of high-resolution (HR) imagery of plant roots is more challenging than above-ground data collection. Thus, an effective super-resolution (SR) algorithm is […]