Expertise Areas:
• Precision Livestock and Poultry Farming
• Agriculture Engineering
• Computer visions
• Applied Artificial Intelligence

Education and Training:
• Postdoc, Iowa State University
• Ph.D., Mississippi State University
• M.S., China Agricultural University
• B.S., China Agricultural University

Research Interests:
The lab’s mission is to conduct cutting-edge research on integrated precision management/applications of digital technologies, data analytics, automations, or models to modern poultry production systems. Research topics include poultry smart sensing and environmental control, applied artificial intelligence, robotics, automated animal welfare assessment, digital phenotyping, three-dimensional reconstruction, and automatic behavior monitoring and analytics.

Members:
• American Society of Agricultural and Biological Engineers
• Poultry Science Association

Major awards:
• 2022 Editor’s Choice Article from MDPI Sensors
• 2022 ASABE Outstanding Reviewer Award
• 2021 ASABE Boyd-Scott Graduate Research Paper Competition Award (First Place)
• 2021 ASABE Superior Paper Award
• 2020 AOC Student Paper Competition Award
• 2020 AOC Graduate Scholarly Achievement Award
• 2019 Outstanding Graduate Student Paper and Presentation Award in the International Symposium on Animal Environment and Welfare
• 2018 Student Presentation Award in the ASABE AIM

Selected recent publications:
Li, G., Chesser, D., Purswell, J.L., and Magee, C. 2022. Design and development of a broiler mortality removal robot. Applied Engineering in Agriculture (in press).
Lei, T., Li, G., Luo, C., and Gates, R.S. 2022. Path planning of autonomous broiler mortality localization and removal robots. Intelligence and Robotics, 2(4): 313-32.

Li, G., Erickson, G.E., and Xiong, Y. 2022. Individual beef cattle identification using muzzle images and deep learning techniques. Animals, 12(11), 1453.

Li, G., Hui, X., Zhao, Y., Zhai, W., Purswell, J.L., Porter, Z., Poudel, S., Jia, L., Zhang, B., and Chesser, G.D. 2022. Effects of ground robot manipulation on hen floor egg reduction, production performance, stress response, bone quality, and behavior. PloS One, 17(4): e0267568.

Li, G., Xiong, Y., Du, Q., Shi, Z., and Gates, R.S. 2021. Classifying ingestive behavior of dairy cows via automatic sound recognition. Sensors, 21(15): 5231.

Li, G., Chesser, D., Huang, Y., Zhao, Y., and Purswell, J.L. 2021. Development and optimization of a deep-learning-based egg collecting robot. Transactions of the ASABE, 64(5): 1659-1669.

Li, G., Zhao, Y., Porter, Z., and Purswell, J.L. 2021. Automated measurement of broiler stretching behaviors under four stocking densities via faster region-based convolutional neural network. Animal, 15(1): 100059.

Yang, X., Huo, X., Li, G., Purswell, J.L., Tabler, T.G., Chesser Jr, G.D., Magee, C., and Zhao, Y. 2020. Effects of elevated perching platform and robotic vehicle on broiler production, welfare, and housing environment. Transactions of the ASABE, 63(6): 1981-1990.

Li, G., Zhao, Y., Purswell, J.L., and Magee, C. 2020. Effects of feeder space on broiler feeding behaviors. Poultry Science, 100(4): 101016.

Li, G., Zhao, Y., Purswell, J.L., Du, Q., Chesser, G.D., and Lowe, J.W. 2020. Analysis of feeding and drinking behaviors of group-reared broilers via image processing. Computers and Electronics in Agriculture, 175: 105596.

Li, G., Zhao, Y., Purswell, J.L., Chesser, G.D., Lowe, J.W., and Wu, T.-L. 2020. Effects of antibiotic-free diet and stocking density on male broilers reared to 35 days of age. Part 2: feeding and drinking behaviors of broilers. Journal of Applied Poultry Research, 29(2): 391-401.

Li, G., Zhao, Y., Porter, Z., and Purswell, J.L. 2020. Automated measurement of broiler stretching behaviors under four stocking densities via faster region-based convolutional neural network. Animal, 15(1): 100059.

Li, G., Xu, Y., Zhao, Y., Du, Q., and Huang, Y. 2020. Evaluating convolutional neural networks for cage-free floor egg detection. Sensors, 20(2): 332.

Li, G., Ji, B., Li, B., Shi, Z., Zhao, Y., Dou, Y., and Brocato, J. 2020. Assessment of layer pullet drinking behaviors under selectable light colors using convolutional neural network. Computers and Electronics in Agriculture, 172: 105333.

Li, G., Hui, X., Lin, F., and Zhao, Y. 2020. Developing and evaluating poultry preening behavior detectors via mask region-based convolutional neural network. Animals, 10: 1762.

Additional Publications:
ResearchGate | Google Scholar | LinkedIn