Autonomous Quadcopter Flight Path Generation via MAVLink and Ground Control Station Architecture for Precision Agricultural Crop Monitoring

Abstract

This paper presents an integrated system design for autonomous quadcopter flight path generation using the MAVLink protocol and a custom Ground Control Station (GCS) for precision agricultural crop monitoring. The system combines three coverage path algorithms (Boustrophedon, Spiral, and Energy-Optimized), a Pixhawk 4 / ArduPilot flight stack, a MicaSense RedEdge-P multispectral payload, and a ROS2-based GCS for mission planning, telemetry, and vegetation-index-based crop health assessment. The 2.8 kg quadcopter (450 mm frame, 4-cell LiPo) achieves 22–25 minutes of flight time. Across five field sizes (0.5–10 ha), the Energy-Optimized path achieved 96.5% coverage efficiency with 4.2% overlap and a 12.4% energy reduction over the Boustrophedon baseline. NDVI-based crop segmentation achieved pixel accuracy of 92.5% (maize), 94.1% (rice), and 90.8% (wheat), and four-class crop-health classification achieved a weighted F1-score of 90.0%. MAVLink 2.0 command latency averaged 15.8 ms with 99.3% packet delivery at ranges up to 800 m. An ablation study showed additional gains of 1.5–3.1% coverage from wind compensation and 2.1–2.8% from terrain-following.

Plant phenotyping relevance

自律ドローン、マルチスペクトル撮像、NDVIセグメンテーションによる作物健康状態推定を統合し、飛行・画像解析性能を定量評価しているため、植物状態の取得・抽出が技術的に実質的な構成要素である。

Code and data availability

The article describes a UAV crop-monitoring system with multispectral imagery, NDVI pipelines, and a U-Net classifier, but contains no data availability statement, repository deposit, or public URL for any dataset, imagery, code, or trained model. No paper-specific public asset is identified.

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