Using remotely controlled platform to acquire low-altitude imagery for grain crop mapping
PhD Thesis
Title | Using remotely controlled platform to acquire low-altitude imagery for grain crop mapping |
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Type | PhD Thesis |
Authors | |
Author | Jensen, Troy |
Supervisor | Apan, Armando |
Institution of Origin | University of Southern Queensland |
Qualification Name | Doctor of Philosophy |
Number of Pages | 311 |
Year | 2008 |
Abstract | [Abstract]Agricultural crops exhibit within-field spatial variation. This variation partly results from relevant bio-physical and environmental factors that influence the While the use of conventional remote sensing systems has found many applications in agriculture, it is constrained by a number of issues and problems related to spatial resolution, repeat cycle, minimum area acquired, timeliness of data, etc. Thus, this research explores the potential of developing and assessing low-cost sensing technologies to overcome these limitations. The specific A low-cost sensor system was developed that incorporated two consumer digital still cameras. One camera captured the colour portion of the spectrum, while the other one (with the addition of a band-pass filter) captured the near Various approaches were taken to determine and evaluate the relationships between imagery and crop attributes. Statistical methods included the use of correlation and discriminant function analysis, along with partial least squares regression. Image analysis techniques included the use of both pixel-based (supervised approach) and object-orientated (multi-resolution segmentation) The results showed that low-cost low-altitude remote sensing systems (incorporating consumer digital cameras with helium balloons or remotely controlled aircraft) have great capacity to quantify variability in cereal grain The same LCLA system has also accurately discriminated (using statistical methods) between: a) different nutrition levels in a wheat crop with 75.6% of the cases correctly classified, and b) between different cereal grain species (with differing nutrition levels) with 86.3% accuracy. These classification accuracies are comparable with, or exceeding other more expensive and/or complicated methods. Attempting to discriminate using image analysis This study concluded that it is feasible to accurately assess selected cereal grain crop attributes using low-cost consumer technologies. The accuracies achieved |
Keywords | remotely controlled platform; imagery; grain crop; mapping |
ANZSRC Field of Research 2020 | 401306. Surveying (incl. hydrographic surveying) |
https://research.usq.edu.au/item/9z54v/using-remotely-controlled-platform-to-acquire-low-altitude-imagery-for-grain-crop-mapping
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