Enhanced electrochemical performance of mesoporous spherical SnS2/porous carbon composite prepared by dual-solvent hydrothermal method for lithium-ion batteries

Hao Luan

doi:10.1007/s11581-022-04762-6

AdaRisk-Agent: LLM-Orchestrated Adaptive Risk Calibration for Cost-Sensitive Active Learning in UAV Weed Detection

2026-08-14

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Ali Güneş

DOI：10.3390/drones10070547

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UAV-based weed detection in precision agriculture is constrained by asymmetric error costs: a missed weed patch causes herbicide under-treatment and yield loss, whereas a false alarm only prompts an unnecessary spot treatment. Cost-sensitive active learning (cAL) addresses this through an asymmetric misclassification penalty r + , but the optimal value is scene-dependent and cannot be determined without domain expertise or advance knowledge of scene difficulty—a fundamental barrier to autonomous UAV monitoring workflows. We propose AdaRisk-Agent, the first LLM-orchestrated framework for adaptive r + calibration in cAL-based UAV weed detection. We validate the framework on four UAV multispectral scenes from two public datasets—WeedsGalore (Germany, five-band maize) and WeedyRice (Vietnam, four-band paddy)—spanning two crop types, two sensor configurations, and weed prevalence from 3.1% to 30.5%. Adaptive calibration reduces the false-negative rate (FNR) by up to 80 % relative to symmetric-cost baselines across all scenes. The deterministic surrogate (AdaRisk-Rule) surpasses the fixed-policy oracle (cAL r + = 7 ) on two of four scenes without advance scene knowledge, achieving a 50 % FNR reduction on the most spectrally challenging scene. A context-feature ablation confirms that budget urgency is the primary calibration signal and that test-set-independent deployment is feasible. Each calibration decision is accompanied by a natural-language justification, enabling auditable deployment in operational precision agriculture workflows. Future work will extend AdaRisk-Agent to multi-class weed species detection and multi-scene meta-learning for compact offline surrogate policies.

Keywords:

active learning

cost-sensitive learning

UAV multispectral imagery

weed detection

large language model

agentic AI

precision agriculture

remote sensing

false negative reduction

AdaRisk-Agent: LLM-Orchestrated Adaptive Risk Calibration for Cost-Sensitive Active Learning in UAV Weed Detection

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