# Adaptive path planning for efficient object search by UAVs in agricultural fields

arXiv: https://arxiv.org/abs/2504.02473 · HTML: https://mlsanity.com/paper/2504.02473/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Online Informative Path Planning for Active Classification Using UAVs](https://mlsanity.com/paper/1609.08446/) (2016)
- [Learning-based Methods for Adaptive Informative Path Planning](https://mlsanity.com/paper/2404.06940/) (2024)
- [Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2203.01642/) (2022)
- [Active learning with MaskAL reduces annotation effort for training Mask R-CNN](https://mlsanity.com/paper/2112.06586/) (2021)
- [Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoring](https://mlsanity.com/paper/2005.10040/) (2020)
- [Fields2Cover: An open-source coverage path planning library for unmanned agricultural vehicles](https://mlsanity.com/paper/2210.07838/) (2022)
- [UAV-based path planning for efficient localization of non-uniformly distributed weeds using prior knowledge: A reinforcement-learning approach](https://mlsanity.com/paper/2412.11717/) (2024)
- [Improved detection of discarded fish species through BoxAL active learning](https://mlsanity.com/paper/2410.04880/) (2024)

## Built on by (most cited first, 1 of 1)

- [Trajectory Planning for UAV-Based Smart Farming Using Imitation-Based Triple Deep Q-Learning](https://mlsanity.com/paper/2512.18604/) (2025)

## Abstract (arXiv)

This paper presents an adaptive path planner for object search in agricultural fields using UAVs. The path planner uses a high-altitude coverage flight path and plans additional low-altitude inspections when the detection network is uncertain. The path planner was evaluated in an offline simulation environment containing real-world images. We trained a YOLOv8 detection network to detect artificial plants placed in grass fields to showcase the potential of our path planner. We evaluated the effect of different detection certainty measures, optimized the path planning parameters, investigated the effects of localization errors, and different numbers of objects in the field. The YOLOv8 detection confidence worked best to differentiate between true and false positive detections and was therefore used in the adaptive planner. The optimal parameters of the path planner depended on the distribution of objects in the field. When the objects were uniformly distributed, more low-altitude inspections were needed compared to a non-uniform distribution of objects, resulting in a longer path length. The adaptive planner proved to be robust against localization uncertainty. When increasing the number of objects, the flight path length increased, especially when the objects were uniformly distributed. When the objects were non-uniformly distributed, the adaptive path planner yielded a shorter path than a low-altitude coverage path, even with a high number of objects. Overall, the presented adaptive path planner allowed finding non-uniformly distributed objects in a field faster than a coverage path planner and resulted in a compatible detection accuracy. The path planner is made available at https://github.com/wur-abe/uav_adaptive_planner.