Adrian Salazar Gomez

Lincoln University - Pennsylvania, King's College London

Papers

Total Citations

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About

Adrian Salazar Gomez is a pioneering researcher at the intersection of agricultural robotics and human-robot collaboration. His work centers on two key areas: robotic phenotyping for precision agriculture and understanding human-robot dynamics in farming contexts. In his most cited work, "Deep Regression Versus Detection for Counting in Robotic Phenotyping" (2021, 25 citations), Gomez systematically compared computer vision methods for estimating fruit and grain counts in agricultural images, providing a crucial framework for researchers deciding between deep regression and detection approaches. This contribution has become a reference point for advancing automated yield estimation. His subsequent research explores the human dimension of agricultural robotics, investigating how people respond to robot errors during collaborative harvesting tasks and how to design effective human-robot interaction in simulated farming environments. Through papers like "Understanding Human Responses to Errors in a Collaborative Human-Robot Selective Harvesting Task" and "Toward Robot Co-Labourers for Intelligent Farming," Gomez is shaping the future of human-robot teams in agriculture, ensuring that technological advances are grounded in real-world usability and trust.