Ramesh Bahadur Bist Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
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Biography and Research Information
OverviewAI-generated summary
Ramesh Bahadur Bist's research focuses on the application of machine vision and deep learning technologies to improve poultry farming practices and animal welfare. His work investigates automated systems for monitoring animal behavior, detecting health issues, and assessing production quality. Bist has published studies on using computer vision to track pecking behaviors and damages in cage-free laying hens, detect hens on litter floors, and monitor piling behavior. He has also developed systems for automatic egg grading and defect detection, as well as for identifying mislaying behavior and tracking floor eggs in hen houses.
His research extends to critical reviews of sustainable poultry farming strategies, with a focus on understanding and mitigating ammonia emissions. Bist's scholarly contributions include 64 publications with over 1,000 citations and an h-index of 18. His work aims to enhance efficiency and welfare in livestock production through technological advancements.
Metrics
- h-index: 18
- Publications: 67
- Citations: 1,109
Selected Publications
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Neural network architecture search enabled wide-deep learning (NAS-WD) for spatially heterogenous property awared chicken woody breast classification and hardness regression (2024)
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Leveraging imitation learning in agricultural robotics: a comprehensive survey and comparative analysis (2024)
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