Title :
The Dynamic Aerial Survey Algorithm Architecture and Its Potential Use in Airborne Fertilizer Applications
Author :
Falzon, Greg ; Lamb, DavidW ; Schneider, Derek
Author_Institution :
Centre for 4 Dimensions, Univ. of New England, Armidale, NSW, Australia
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.
Keywords :
agriculture; crops; fertilisers; monitoring; regression analysis; support vector machines; DAS algorithm; aerial prescription technology; agricultural industry; airborne fertilizer applications; dynamic aerial survey algorithm architecture; remote crop monitoring equipment; support vector regression; uniform-cut segmentation modules; user requirements; Agriculture; Aircraft; Algorithm design and analysis; Decision support systems; Fertilizers; Geophysical signal processing; Sensors; Adaptive signal processing; agriculture; aircraft expert systems; algorithms; decision support systems; geophysical signal processing; remote sensing;
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
DOI :
10.1109/JSTARS.2011.2179020