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in preharvest and post-harvest production and processing. This project will focus primarily on safety and quality inspection using spectral imaging techniques such as fluorescence, reflectance, and
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-automated processing pipeline capable of analyzing high-throughput plant phenotyping and soil-sensing data to extract key phenotypic traits. Advancing crop productivity within sustainable cropping systems
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this fellowship, you will participate in research projects involving canine biometric data, looking for novel ways to identify and individuate dogs using neural networks, local feature mapping, image classifiers
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using sensors, data loggers, and thermal images Lab experiments examining how nutrition affects performance and physiological traits Field experiments quantifying behavior of insects Mentor(s): The mentor
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data have the potential to rapidly check animals and serve as a tool for farmers, ranchers, and researchers. Current research is being done by PI to automate imaging and spectral data to develop and
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of incorporating sensors, spectroscopy, imaging, and machine learning techniques into the postharvest processing workflows and/or pre-harvest evaluation of food quality and safety. The participant will have the
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about the application process, please email ORISE.ARS.Southeast@orau.org and include the reference code for this opportunity. Qualifications The qualified candidate should be currently pursuing or have
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and maintain equipment for measuring gas exchange, image and spectral data collection and analysis, and the effect of different turfgrass management options on its visual quality, sustainability and
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Products. A current research project evaluates the role of microbes in texture losses of vegetables during processing and long-term storage. The selected candidate will participate in all aspects