用边缘设备实时检测农田积水，智能分配计算资源。
Edge-Based Standing-Water Detection via FSM-Guided Tiering and Multi-Model Consensus
- 基于有限状态机动态选择本地或云端推理，适应网络和算力变化。
- 多模型共识与昼夜环境融合提升检测准确率，能耗低于固定上云方案。
- 适合农业自动驾驶、智能农机等低延迟高能效场景使用。
农田积水威胁农机通行与作物健康。本文提出一种部署于树莓派级设备（可选Jetson加速）的边缘架构，融合摄像头输入与温湿度气压传感器数据，通过有限状态机（FSM）作为决策核心，动态选择本地或远程推理层级，在间歇性连接和运动相关算力约束下权衡精度、延迟与能耗。多模型YOLO集成提供图像评分，昼夜基线传感器融合根据环境异常调整预警阈值。所有决策按帧记录，支持比特级硬件在环重放。在相同田间序列的十种配置与传感器变体上，结合自适应分层、多模型共识与昼夜传感器融合的方法，相比静态本地基线显著提升洪水检测性能，能耗低于始终上云的朴素策略，并在真实农业环境中保持有界尾部延迟。
原文摘要 · Abstract (English)
Standing water in agricultural fields threatens vehicle mobility and crop health. This paper presents a deployed edge architecture for standing-water detection using Raspberry-Pi-class devices with optional Jetson acceleration. Camera input and environmental sensors (humidity, pressure, temperature) are combined in a finite-state machine (FSM) that acts as the architectural decision engine. The FSM-guided control plane selects between local and offloaded inference tiers, trading accuracy, latency, and energy under intermittent connectivity and motion-dependent compute budgets. A multi-model YOLO ensemble provides image scores, while diurnal-baseline sensor fusion adjusts caution using environmental anomalies. All decisions are logged per frame, enabling bit-identical hardware-in-the-loop replays. Across ten configurations and sensor variants on identical field sequences with frame-level ground truth, we show that the combination of adaptive tiering, multi-model consensus, and diurnal sensor fusion improves flood-detection performance over static local baselines, uses less energy than a naive always-heavy offload policy, and maintains bounded tail latency in a real agricultural setting.
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