Ramesh Bist

Bio

Dr. Ramesh Bahadur Bist received his Ph.D. in Poultry Science from the University of Georgia’s
Department of Poultry Science in 2024 under the supervision of Dr. Lilong Chai.

During his Ph.D., he worked extensively with AI tools and computer vision to detect, classify, and segment animal health, behavior, and welfare conditions in poultry. He also used a robotic dog to detect
mortality in hens and locate floor eggs. After his Ph.D., Dr. Bist joined the University of Arkansas’s Department of Biological and Agricultural Engineering in May 2024 as a postdoctoral fellow, working on computer vision and robotics with Dr. Dongyi Wang. His work included developing auto-labeling and text-prompt-based behavior detection models and applying hyperspectral and thermal sensors to detect meat
defects and foreign materials. He contributed to an unmanned ground vehicle for autonomous navigation in poultry plants using SLAM.

At NC State, Dr. Bist leads a research program focused on machine learning, robotics, automation, and sensing for livestock and food systems. His goals include predicting livestock diseases, monitoring animal health, building affordable autonomous vehicles, creating mobile apps, and designing non-destructive food quality tests. He had published his research papers in many highly reputed journals like Computers and Electronics in Agriculture, Poultry Science, and Journal of Environmental Management. Dr. Bist serves as a guest editor for special issues in Applied Sciences and AgriEngineering (MDPI) and reviews for journals including Nature Scientific Reports, Computers and Electronics in Agriculture, Poultry Science, Sensors, and many others. To find details about his lab link and research work link. Find details about his lab AIR Lab and research work .

Publications

• Portable electrochemical impedance biosensing with DRT-enabled machine learning for detecting E. coli O157:H7 in poultry meat , Frontiers in Artificial Intelligence (2026)
• Precision Farming Technologies for Monitoring Livestock and Poultry , AgriEngineering (2026)
• Smartphone-enabled Depth-aware Transillumination Imaging for Detection of Chicken Breast Fillet Myopathies in Chicken Fillets , SSRN Electronic Journal (2026)
• A Semi-Supervised Auto-Labeling Approach to Enhance Artificial Intelligence in Poultry Farming , IPSF/IPPE (2025)
• Advanced Deep Learning Methods for Multiple Behavior Classification of Cage-Free Laying Hens , AgriEngineering (2025)
• Automated Detection of Kinky Back in Broiler Chickens Using Optimized Deep Learning Techniques , AgriEngineering (2025)
• Deep Learning Methods for Automatic Identification of Male and Female Chickens in a Cage-Free Flock , Animals (2025)
• Deep Learning Methods for Tracking Activities of Male Birds in Cage-Free Flock , IPSF/IPPE (2025)
• Deep-learning-enhanced automated coherent-light diffraction system for high-speed, highly accurate strain-specific foodborne bacterial recognition , Journal of Agriculture and Food Research (2025)
• Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach , arXiv (Cornell University) (2025)