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Geolocation of Diseased Leaves in Strawberry Orchards for a Custom-Designed Octorotor
| Title: | Geolocation of Diseased Leaves in Strawberry Orchards for a Custom-Designed Octorotor. |
 30 views 14 downloads |
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| Name(s): |
Garcia, Christian, Author Xu, Yunjun, Committee Chair Lin, Kuo-Chi, Committee Member Kauffman, Jeffrey, Committee Member University of Central Florida, Degree Grantor |
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| Type of Resource: | text | |
| Date Issued: | 2016 | |
| Publisher: | University of Central Florida | |
| Language(s): | English | |
| Abstract/Description: | In recent years, technological advances have shown a strive for more automated processes in agriculture, as seem with the use of unmanned aerial vehicles (UAVs) with onboard sensors in many applications, including disease detection and yield prediction. In this thesis, an octorotor UAV is presented that was designed, built, and flight tested, with features that are custom-designed for strawberry orchard disease detection. To further automate the disease scouting operation, geolocation, or the process of determining global position coordinates of identified diseased regions based on images taken, is investigated. A Kalman filter is designed, based on a linear measurement model derived from an orthographic projection method, to estimate the target position. Simulation, as well as an ad-hoc experiment using flight data, is performed to compare this filter to the extended Kalman filter (EKF), which is based on the commonly used perspective projection method. The filter is embedded onto a CPU board for real-time use aboard the octorotor UAV, and the algorithm structure for this process is presented. In the later part of the thesis, a probabilistic data association method is used, jointly with a proposed logic-based measurement-to-target correlation method, to analyze measurements of different target sources and is incorporated into the Kalman filter. A simulation and an ad-hoc experiment, using video and flight data acquired aboard the octorotor UAV with a gimballed camera in hover flight, are performed to demonstrate the effectiveness of the algorithm and UAV platform. | |
| Identifier: | CFE0006305 (IID), ucf:51597 (fedora) | |
| Note(s): |
2016-08-01 M.S.A.E. Engineering and Computer Science, Mechanical and Aerospace Engineering Masters This record was generated from author submitted information. |
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| Subject(s): | UAV -- Geolocation -- Kalman Filtering -- Precision Agriculture -- Data Association -- Multi-Target Tracking -- Disease Detection | |
| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0006305 | |
| Restrictions on Access: | campus 2017-08-15 | |
| Host Institution: | UCF |
