Kyle Lammers
Papers
15
Total Citations
519
H-Index
10
About
Kyle Lammers is a leading researcher in agricultural robotics, specializing in the automation of fruit harvesting. His work directly addresses the critical labor shortages threatening the global apple industry. Lammers’ major contributions center on the complete pipeline of robotic harvesting, from perception to manipulation. He developed a groundbreaking deep learning system, the suppression mask R-CNN, for robust apple detection (154 citations), and engineered the system design and control for a fully functional apple harvesting robot (131 citations). His research culminated in a landmark field evaluation of an automated apple harvester (81 citations), demonstrating real-world viability. Lammers has further advanced the field with innovations in dual-arm harvesting systems, occluder-occludee relational networks for detection in cluttered environments, and high-precision fruit localization using active laser-camera scanning. His work on end effectors for vacuum-based harvesting and the creation of the MetaFruit dataset, which leverages foundation models for multi-fruit applications, underscores his commitment to practical, scalable solutions. With over 600 total citations, Lammers is a pivotal figure in bringing robotic harvesting from concept to commercial reality.
Research Focus
Key Achievements
Top Papers
- 1Deep learning-based apple detection using a suppression mask R-CNN154 citations · 2021
- 2System design and control of an apple harvesting robot131 citations · 2021
- 3An automated apple harvesting robot—From system design to field evaluation81 citations · 2023
- 4Development and evaluation of a dual-arm robotic apple harvesting system27 citations · 2024
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- 6Algorithm Design and Integration for a Robotic Apple Harvesting System26 citations · 2022
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