aiqbench analysis
UltraLight Luma presents a significant advancement in edge AI by achieving efficient crop-row segmentation with minimal parameters and energy consumption, directly addressing the computational constraints of autonomous agricultural robotics. The study validates that specialized lightweight networks can outperform standard architectures like U-Net and YOLO variants while maintaining detection reliability for visual servoing tasks. This work highlights critical progress in deploying practical machine learning models on resource-constrained hardware within the ag-tech sector.
UltraLight Luma: A Novel Edge-Deployable Perception Network for Crop-Row Segmentation in Agricultural Robotics
arXiv:2610.11764v1 Announce Type: new Abstract: Reliable crop-row perception is essential for autonomous agricultural robots, but low-cost deployment is constrained by computation, memory, power, and inference time. This paper addresses crop-row detection and navigation-line extraction using UltraLight Luma, a compact encoder-decoder segmentation network with a progressive learning strategy and only 13.1k trainable parameters. The predicted crop-row masks are processed using a post-processing algorithm to estimate row alignment for downstream visual servoing. UltraLight Luma improves parameter efficiency over U-Net, YOLOv8, and YOLOv26 while maintaining reliable crop-row detection performance. The model required only 25.23 mJ per inference, demonstrating its suitability for resource-constrained agricultural robots.
Abstract:Reliable crop-row perception is essential for autonomous agricultural robots, but low-cost deployment is constrained by computation, memory, power, and inference time. This paper addresses crop-row detection and navigation-line extraction using UltraLight Luma, a compact encoder-decoder segmentation network with a progressive learning strategy and only 13.1k trainable parameters. The predicted crop-row masks are processed using a post-processing algorithm to estimate row alignment for downstream visual servoing. UltraLight Luma improves parameter efficiency over U-Net, YOLOv8, and YOLOv26 while maintaining reliable crop-row detection performance. The model required only 25.23 mJ per inference, demonstrating its suitability for resource-constrained agricultural robots.
This work has been accepted for publication in IECON 2026. The final published version will be available via IEEE Xplore
Robotics (cs.RO)
arXiv:2610.11764 [cs.RO]
(or arXiv:2610.11764v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.11764 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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