Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/12213
Title: | The Dynamic Aerial Survey Algorithm Architecture and Its Potential Use in Airborne Fertilizer Applications | Contributor(s): | Falzon, Gregory (author) ; Lamb, David (author); Schneider, Derek (author) | Publication Date: | 2012 | DOI: | 10.1109/JSTARS.2011.2179020 | Handle Link: | https://hdl.handle.net/1959.11/12213 | Abstract: | The architecture and general structure of the Dynamic Aerial Survey (DAS) algorithm is presented in this paper. This algorithm is specifically designed for real-time airborne prescription fertilizer applications in the agricultural industry and is designed to batch process the dynamically updated data set after the aircraft completes each successive pass over the field using remote crop monitoring equipment. A key aspect of the DAS algorithm is that it allows a variety of different regression and segmentation modules to be added or deleted to suit user requirements. A specific application is presented concerning an aerial geo-survey of a 110 ha wheat field. The DAS algorithm, using the support-vector regression machine and the uniform-cut segmentation modules, will be demonstrated to allow accurate "on-the-go" estimation, updating and segmentation of the entire field into different management zones as the aircraft completes each pass. The DAS algorithm constitutes a key step in a wider research program designed to develop active-sensor based aerial prescription technology. | Publication Type: | Journal Article | Source of Publication: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(6), p. 1772-1779 | Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | Place of Publication: | United States of America | ISSN: | 2151-1535 1939-1404 |
Fields of Research (FoR) 2008: | 070104 Agricultural Spatial Analysis and Modelling 070101 Agricultural Land Management 090606 Photonics and Electro-Optical Engineering (excl Communications) 080605 Decision Support and Group Support Systems |
Fields of Research (FoR) 2020: | 300206 Agricultural spatial analysis and modelling 400909 Photonic and electro-optical devices, sensors and systems (excl. communications) 460507 Information extraction and fusion |
Socio-Economic Objective (SEO) 2008: | 960504 Ecosystem Assessment and Management of Farmland, Arable Cropland and Permanent Cropland Environments 820507 Wheat 820404 Sorghum |
Socio-Economic Objective (SEO) 2020: | 180601 Assessment and management of terrestrial ecosystems 260312 Wheat 260310 Sorghum |
Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article School of Science and Technology |
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