We present an autonomous aerial system for safe and efficient through-the-canopy fruit counting. Aerial robot applications in large-scale orchards face significant challenges due to the complexity of fine-tuning flight paths based on orchard layouts, canopy density, and plant variability. Through-the-canopy navigation is crucial for minimizing occlusion by leaves and branches but is more challenging due to the complex and dense environment compared to traditional over-the-canopy flights. Our system addresses these challenges by integrating: i) a high-fidelity simulation framework for optimizing flight trajectories, ii) a low-cost autonomy stack for canopy-level navigation and data collection, and iii) a robust workflow for fruit detection and counting using RGB images. We validate our approach through fruit counting with canopy-level aerial images and by demonstrating the autonomous navigation capabilities of our experimental vehicle.
View on arXiv@article{yang2025_2409.18293,
title={ Towards Safe and Efficient Through-the-Canopy Autonomous Fruit Counting with UAVs },
author={ Teaya Yang and Roman Ibrahimov and Mark W. Mueller },
journal={arXiv preprint arXiv:2409.18293},
year={ 2025 }
}