Hidetaka Nambo

Papers

Total Citations

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About

Hidetaka Nambo is a researcher whose work spans robotics, artificial intelligence, and agricultural automation, with a focus on practical applications of deep learning. His key contributions include developing a spatial-based deep learning autonomous wheel robot using convolutional neural networks (CNN), which enables robots to make human-like decisions without direct intervention—a foundational step in intelligent robotics. In agriculture, Nambo advanced precision farming by fine-tuning RetinaNet for real-time lettuce detection, addressing the labor-intensive nature of harvesting and supporting efficient crop yield maximization. Earlier in his career, he explored human-robot interaction through an owner distinction method for healing-type pet robots, aiming to reduce stress in therapeutic settings by making robots more responsive to individual users. While his citation counts are modest—with his most cited work reaching 9 citations—his research demonstrates a clear trajectory from human-robot interaction to autonomous systems and agricultural AI. Nambo’s work is notable for bridging theoretical deep learning models with tangible, real-world applications, offering valuable insights for students and researchers interested in robotics, computer vision, and smart farming technologies.