20.500.12556/RUL-186076 RADLER context-aware slimmable model selection for energy-efficient real-time UAV weed detection Real-time UAV weed detection must operate under tight onboard compute and energy constraints, yet most lightweight segmentation pipelines still commit the system to a single fixed accuracy-efficiency operating point. This article introduces RADLER, a context-aware model selection strategy for slimmable neural networks that predicts, for each input image, the smallest network width that remains close to the best attainable segmentation quality. RADLER combines image-level contextual features, feature scaling with redundancy diagnostics, and threshold-controlled optimal-width labels to expose multiple operating points rather than a single adaptive configuration. We evaluate the method on two public UAV weed detection datasets, compare it against static-width baselines and an oracle reference, and characterize runtime and energy behavior on an NVIDIA Jetson Nano. Across the evaluated settings, RADLER matches full-width performance within uncertainty in one setting and otherwise trades a small, measurable IoU decrease for lower average selected width, enabling energy savings between 25% and 50% depending on the operating point. The results show that context-aware slimmable model selection can make onboard agricultural vision pipelines more transparent in their trade-offs and better suited to battery-constrained UAV platforms. adaptive inference energy-efficient deep learning image segmentation precision agriculture slimmable neural networks unmanned aerial vehicles weed detection prilagodljivo sklepanje energijsko učinkovito globoko učenje segmentacija slik precizno kmetijstvo nevronske mreže s prilagodljivo širino brezpilotni zrakoplovi zaznavanje plevela true false true Angleški jezik Slovenski jezik Članek v reviji 2026-08-26 13:42:15 2026-08-26 13:42:16 2026-08-27 04:36:06 0000-00-00 00:00:00 2026 0 0 Str. 116854-116866 Vol. 14 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.93:004.8:632.51 2169-3536 10.1109/ACCESS.2026.3718374 288258307 RAZ_Machidon_Alina_Luminita_2026.pdf RAZ_Machidon_Alina_Luminita_2026.pdf 1 B166CBE645DA88B43C15D15681E36288 c6f1f67dc6ddb41ad4cf323391e3087285cffcee6b22923530301269b12842a0 0ef5963f-a143-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=245141 https://ieeexplore.ieee.org/document/11630601 1 c732328b-a142-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=245140 Fakulteta za računalništvo in informatiko 0 0 0