This project utilizes machine learning methods to examine how subject-specific differences influence hand function and create subject-specific computer models from easy to obtain clinical data. Completion of this project will critically advance the ability to efficiently create subject-specific models of the hand and understand the biomechanical mechanism underlying hand force production.
Tag: ECE
FACT: FreshID-Machine Learning for the Molecular Evaluation of Fresh Produce Quality
In this effort, Tie Liu and Alina Zare are developed AI-based approaches to detect and predict vegetable “freshness” levels.
SiTS: Hyperspectral Signals in the Soil
In this effort, we will build hyperspectral cameras (which also collect information outside of the visible range collected by standard color cameras) that will be inexpensive, have an automated mechanism to move up and down minirhizotron tubes, and are paired with automated algorithms to process and understand the collected data.
Testing Predictions of Plant-Microbe- Environment Interactions to Optimize Climate Adaptation and Improve Sustainability in Switchgrass Feedstocks
CESU – Hurricane effects on the distribution and management of plant invasions in coastal habitats
Luke Flory, Paul Gader, and Alina Zare are using on-the-ground plant measurements and aerial hyperspectral sensing to evaluate the effects of hurricanes on Brazilian peppertree.
Coordinated Adaptive Phenotyping (CAPs) for Improving Soil Water Acquisition
Our multi-disciplinary team will use peanut as a model legume crop and integrate recent sensor advances, the fundamentals of crop water use, and recently developed modeling approaches to estimate genotypic water stress resilience and heritability.
Satellite estimation of Bahiagrass yield and crude protein
In this effort, satellite remote sensing imagery is being used to estimate parameters of interest in pasture used for cattle ranching.
MRA: Disentangling Cross-scale Influences on Tree Species, Traits and Diversity from Individual Trees to Continental Scales
Trees are essential to ecosystems. They store carbon, reduce erosion, and serve as habitat for other species. The factors influencing trees, and the spatial scales at which they are managed, range from an individual tree to entire continents. Since there are approximately three trillion trees in the world collecting data on every tree over large areas is impossible using traditional methods. Therefore, it is necessary to use new technology to measure and describe individual trees over large geographic areas. This research will address this fundamental challenge by combining high resolution remote sensing data with field data on trees.
FreshID: Fruits and Vegetables Quality Evaluation Using Hyperspectral Imaging System
In this effort, Tie Liu and Alina Zare are developed AI-based approaches to detect and predict vegetable “freshness” levels.
Improved System Assessment of Aflatoxin Risk Utilizing Novel Data and Sensing
Vulnerability of agroecosystems to aflatoxin is a major problem worldwide, and in the U.S., represents an economic threat from high costs associated with testing and lost trade when outbreaks occur – events predicted to become increasingly common with climate variability. Our team includes engineers, agronomists, breeders, physiologists, and data scientists, utilizing an integrated systems approach, employing modeling, new sensing technologies, and data mining for assessing risk at vulnerability points.