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Edge-Based Standing-Water Detection via FSM-Guided Tiering and Multi-Model Consensus

Oliver Aleksander Larsen, Mahyar T. Moghaddam

Abstract

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.

Edge-Based Standing-Water Detection via FSM-Guided Tiering and Multi-Model Consensus

Abstract

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.

Paper Structure

This paper contains 25 sections, 2 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Standing-Water Detection and Off-Road Perception
  4. Resource-Aware Edge Architectures and Perception Tactics
  5. Architecture
  6. Logical View
  7. Deployment View
  8. Behavioural View
  9. Reliability and Operability Tactics
  10. Decision Logic and Orchestration
  11. Motion-Aware Finite-State Machine
  12. Multi-Model YOLO Consensus and Image Score
  13. Diurnal Sensor Baselines and Fusion
  14. Unified Scoring and Decision Provenance
  15. Evaluation
  16. ...and 10 more sections

Figures (4)

  • Figure 1: High-level logical view of the three-node flood-detection architecture
  • Figure 2: Deployment view of the system.
  • Figure 3: High Level Motion-aware finite-state machine
  • Figure 4: Adaptive behaviour across hazard severity: grouped bars for energy (left axis) and heavy-tier usage (right axis) by regime for baselines and production models.