Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture
Abstract
In recent years, a phenomenal increase in the development of Unmanned Aerial Vehicles (UAVs) has been observed in a broad range of applications in various fields of study. Precision agriculture has emerged as a major field of interest, integrating unmanned monitoring of crop health into general agricultural practices for researchers are utilizing UAV to collect data for post-analysis. This paper describes a modular and generic system that is able to control the UAV using computer vision. A configuration approach similar to the Observation, Orientation, Decision and Action (OODA) loop has been implemented to allow the system to perform on-board decision making. The detection of an object of interest is performed by computer vision functionality. This allows the UAV to change its planned path accordingly and approach the target in order to perform a close inspection, or conduct a manoeuvres such as the application of herbicide or collection of higher resolution agricultural images. The results show the ability of the developed system to dynamically change its current goal and implement an inspection manoeuvre to perform necessary actions after detecting the target. The vision based navigation system and on-board decision making were demonstrated in three types of tests: ArUco Marker detection, colour detection and weed detection. The results are measured based on the sensitivity and the selectivity of the algorithm. The sensitivity is the ability of the algorithm to identify and detect the true positive target while the selectivity is the capability of the algorithm to filter out the false negatives for detection targets. Results indicate that the system is capable of detecting ArUco Markers with 99% sensitivity and 100% selectivity at 5 m above the ground level. The system is also capable of detecting a red target with 96% sensitivity and 99% selectivity at the same height during a test height at 5 metres. This system has potential applicability in the field of precision agriculture such as, crop health monitoring, pest plant detection which causes detrimental financial damage to crop yields if not noticed at an early stage.
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The path planning and self-localization and navigation abilities of UAVs are critical to complete agricultural tasks, which have been studied in the agricultural scenario.
“The path planning [140] and self-localization and navigation [139] abilities of UAVs are critical to complete the above tasks, which have been studied in the agricultural scenario.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [140]
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- Authors
- Alsalam, B. H. Y., Morton, K., Campbell, D., & Gonzalez, F.
- Affiliations
- Queensland University of Technology (AU)
- Venue
- 2017 IEEE Aerospace Conference
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 1-12
- ISSN
- 1095-323X (ISSN-L)
- OpenAlex
- W2623923152
- Cited by
- 178
- References
- 25
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 25 · 2023: 24 · 2022: 35 · 2021: 26 · 2020: 20 · 2019: 13 · 2018: 12 · 2017: 2
- FWCI
- 37.74
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Aerospace Conference (AERO) (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Engineering › Aerospace Engineering › Aerospace Engineering and Control Systems
- Keywords
- autonomous UAV, visual navigation, OODA loop, precision agriculture, remote sensing, color recognition, weed detection, crop health monitoring, path planning, herbicide application, plant pest detection
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