Steven C. Hespeler

New Mexico State University, Oak Ridge National Laboratory

Papers

Total Citations

H-Index

About

Steven C. Hespeler is a pioneering researcher at the intersection of robotics, deep learning, and non-destructive evaluation, with a primary focus on agricultural automation and industrial inspection. His most influential work, "Non-destructive thermal imaging for object detection via advanced deep learning for robotic inspection and harvesting of chili peppers" (2021, 64 citations), demonstrates a novel application of computer vision and real-time object detection to agricultural robotics, enabling high-quality machine assistance for crop harvesting. This work has become a key reference in precision agriculture, showcasing how deep learning can transform traditional farming practices. More recently, Hespeler has advanced the field of infrastructure maintenance through his 2023 study on "Deep Learning–Based Time-Series Classification for Robotic Inspection of Pipe Condition Using Non-Contact Ultrasonic Testing," which integrates Electromagnetic Acoustic Transducers (EMAT) with time-series classification algorithms for compact robotic platforms. This research addresses critical challenges in pipeline integrity assessment, offering non-contact, automated solutions for detecting structural defects. With a growing citation record, Hespeler’s contributions are shaping the future of intelligent robotics in both agriculture and civil infrastructure, bridging the gap between cutting-edge AI and practical, real-world applications.