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About

Jikang Yang is a researcher at the forefront of applying advanced computer vision and robotics to modern poultry farming. His work centers on precision livestock farming, specifically addressing the critical challenges of automated monitoring and management in large-scale, stacked-cage hen houses. Yang’s major contributions include developing robust detection and counting systems for densely caged environments. His most-cited paper, "A detection method for dead caged hens based on improved YOLOv7" (2024, 36 citations), tackles the vital task of identifying mortality in crowded conditions, directly improving animal welfare and farm efficiency. He further advanced this with an enhanced YOLOv8-based methodology for accurate chicken counting, overcoming the limitations of manual labor and traditional deep learning models. Beyond detection, Yang has pioneered autonomous navigation for inspection robots, proposing a novel grayscale factor (4B-3R-2G) for rapid road extraction in coops, and developed a learning-from-demonstration method for cleaning robots. His integrated approach—combining object detection, counting, and robotic navigation—positions him as a key innovator in smart agriculture, with his work directly impacting operational efficiency and animal health monitoring in the poultry industry.