Techniques for Collecting Shadow-Free Imagery with Unmanned Aerial Vehicles in Precision Agriculture
Abstract/Contents
- Abstract
- Rising populations and anthropogenic climate change continue to augment pressures on the agricultural industry to produce more with less land. Imagery from unmanned aerial vehicles (UAVs) has shown promise in meeting this demand, allowing farms to precisely monitor the status of their crops and target areas for intervention. Variations in cloud cover represent a serious challenge when aerial imagery is processed to generate agricultural insights, as even small variations in lighting may result in inaccurate prescriptions of crop inputs. This work introduces a method for processing wide angle imagery to automatically determine the location of shadows cast by clouds. A communication protocol is also developed for directing UAVs to avoid flying over shadowed regions and retrieving UAV imagery for monitoring both crops and cloud cover. This work comprises a critical foundation of core functionality necessary for end-to-end development of multi-vehicle UAV systems to collect shadowless imagery.
Description
| Type of resource | text |
|---|---|
| Date created | May 15, 2021 |
Creators/Contributors
| Author | Choi, Benjamin | |
|---|---|---|
| Primary advisor | Murmann, Boris | |
| Advisor | Levis, Philip | |
| Degree granting institution | Stanford University, Department of Electrical Engineering |
Subjects
| Subject | Electrical Engineering |
|---|---|
| Subject | NIFA |
| Subject | UAV |
| Subject | unmanned aerial vehicles |
| Subject | precision agriculture |
| Subject | drone |
| Subject | shadow |
| Subject | cloud |
| Subject | aerial imagery |
| Subject | removing shadows |
| Genre | Thesis |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Preferred citation
- Preferred Citation
- Choi, Benjamin. (2021). Techniques for Collecting Shadow-Free Imagery with Unmanned Aerial Vehicles in Precision Agriculture. Stanford Digital Repository. Available at: https://purl.stanford.edu/gq085vt3537
Collection
Undergraduate Theses, School of Engineering
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- Contact
- benchoi@stanford.edu
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