Category: Publication

Abstract: Smart grid (SG) systems are designed to leverage digital automation technologies for monitoring, control and analysis. As SG technology is implemented in increasing numbers of power systems, SG data becomes increasingly vulnerable to cyber-attacks. Classic analytic physics-model based bad data detection methods may not detect these attacks. Recently, physics-model and datadriven methods have been […]

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!

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 […]

Congratulations to GatorSense alumna, Xiaoxiao Du!  Her paper, titled “Multi-resolution Multi-modal Sensor Fusion For Remote Sensing Data with Label Uncertainty”, was recently published in IEEE Transactions on Geoscience and Remote Sensing.   Check it out here!

Abstract: In remote sensing, it is often challenging to acquire or collect a large dataset that is accurately labeled. This difficulty is usually due to several issues, including but not limited to the study site’s spatial area and accessibility, errors in the global positioning system (GPS), and mixed pixels caused by an image’s spatial resolution. […]

Machine Learning and Sensing Lab Alumni, Anand Seethepalli, and collaborators recently had a paper accepted to Plant Phenomics.  The article discusses an innovative platform to help collect consistent images of root crowns for phenotyping.  Check it out here! 

Abstract: The spectral signatures of the materials contained in hyperspectral images (HI), also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an HI. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process […]

Abstract: The adoption of remote sensing techniques in plant science enables noninvasive or minimally invasive measurement, which is also time and labor saving when compared to traditional field measurements. In this thesis, a method to distinguish switchgrass genotypes with the analysis of remotely-sensed hyperspectral imagery is proposed. A processing protocol for hyperspectral imagery including preprocessing, […]