Shubham Subrot Panigrahi
Post-doctoral Research Associate
Also affiliated: University of Lethbridge (2024–2026); Agriculture and Agri-Food Canada (2025–2026); National Institute of Technology Rourkela (2015–2018); University of South Australia (2019–2025); Lethbridge Research and Development Centre (2026); Savitribai Phule Pune University (2023); Lethbridge College (2022–2026)
Postdoc Researcher
Food Science
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Shubham Subrot Panigrahi's research focuses on applying engineering principles to food safety and the circular bioeconomy. His work utilizes multiphysics and data-driven modeling to investigate these areas. Previously, Panigrahi was involved in high-throughput crop phenotyping at Agriculture and Agri-Food Canada, employing LiDAR, RGB, hyperspectral, and multispectral imaging technologies.
His prior role as a Postdoctoral Research Associate at Lethbridge Polytechnic involved leading industry projects to develop automated, energy-efficient crop storage solutions. In this capacity, he collaborated with various stakeholders, including farmers and technology developers, to create scalable systems for on-farm grain monitoring, IoT integration, and quality control. Panigrahi's doctoral research at the University of South Australia centered on computational modeling, specifically using computational fluid dynamics (CFD) and finite element method (FEM), to enhance aeration strategies in stored grain facilities.
His scholarly output includes 40 publications with 670 citations, and he holds an h-index of 12. Panigrahi has a key collaborator, Kaushik Luthra, at the University of Arkansas at Fayetteville, with whom he shares one publication.
Metrics
- h-index: 12
- Publications: 42
- Citations: 706
Selected Publications
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Comparison on energy efficiency, carbon emissions, and cost implications of cross-flow, mixed-flow, and double-flow grain dryers in evaluating process sustainability (2026)
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Impact of on-farm high-temperature wheat drying parameters on milling quality: Process efficiency vs quality comparison (2026)
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An integrated UAV-based spectral-index fusion framework using machine learning classifiers to model physiological maturity in dry bean (2026)
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Forecasting Spring Wheat Maturity from UAV-Based Multispectral Imagery Using Machine and Deep Learning Models (2026)
Collaboration Network
Top Collaborators
- Forecasting Spring Wheat Maturity from UAV-Based Multispectral Imagery Using Machine and Deep Learning Models
- An integrated UAV-based spectral-index fusion framework using machine learning classifiers to model physiological maturity in dry bean
- Forecasting Spring Wheat Maturity from UAV-Based Multispectral Imagery Using Machine and Deep Learning Models
- Forecasting Spring Wheat Maturity from UAV-Based Multispectral Imagery Using Machine and Deep Learning Models
- An integrated UAV-based spectral-index fusion framework using machine learning classifiers to model physiological maturity in dry bean
- An integrated UAV-based spectral-index fusion framework using machine learning classifiers to model physiological maturity in dry bean
- An integrated UAV-based spectral-index fusion framework using machine learning classifiers to model physiological maturity in dry bean
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