Papers
1
Total Citations
3
H-Index
1
About
Kendall Koe is a rising roboticist whose work focuses on precision agricultural automation, with a particular emphasis on manipulation in cluttered, unstructured environments. Her research integrates end-effector design with advanced perception systems to address critical labor shortages in specialty crop industries. Koe’s most cited paper, “Precision Harvesting in Cluttered Environments: Integrating End Effector Design with Dual Camera Perception” (2025, 3 citations), tackles the challenge of robotic harvesting in high tunnel environments—compact, cluttered spaces where traditional systems fail. By combining dual-camera perception with novel gripper designs, she has demonstrated how robots can reliably identify and harvest crops amidst dense foliage and variable lighting. Though early in her career, Koe’s work represents a significant step toward making robotic harvesting viable for small-scale, high-value crops. Her contributions are particularly notable for bridging the gap between laboratory demonstrations and real-world agricultural constraints, offering a scalable solution to an industry under pressure. As automation becomes increasingly critical to global food production, Koe’s research positions her as a key innovator in the next generation of agricultural robotics.
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Top Papers
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