OPTIMIZING LOW-COST UAV AERIAL IMAGE MOSAICING FOR CROP GROWTH MONITORING

OPTIMIZING LOW-COST UAV AERIAL IMAGE MOSAICING FOR CROP GROWTH MONITORING

High spatial resolution images acquired with drones can provide useful information to farmers for devising suitable management practices and increase crop yield. Data collected as individual frames or images have to be mosaiced using pattern recognition and matching process. Most flight missions col...

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Bibliographic Details
Published in:International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLIV-M-3-2021; pp. 7 - 12
Main Authors: Bupathy, P., Sivanpillai, R., Sajithvariyar, V. V., Sowmya, V.
Format: Journal Article Conference Proceeding
Language:English
Published: Gottingen Copernicus GmbH 2021
Copernicus Publications
Subjects:
Algorithms
Archives & records
Consultancies
Crop fields
Crop growth
Crop yield
Developing countries
Distortion
Drones
Farmers
Frames
Image acquisition
LDCs
Missions
Mosaics
Object recognition
Open source software
Optimization
Pattern recognition
Resources
Sensors
Spatial resolution
Unmanned aerial vehicles
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Summary:High spatial resolution images acquired with drones can provide useful information to farmers for devising suitable management practices and increase crop yield. Data collected as individual frames or images have to be mosaiced using pattern recognition and matching process. Most flight missions collect hundreds of photos with high overlap and side overlap in order to generate mosaic without data gaps or distortion. These frames are aligned using the location information associated with each image. The same features are identified in multiple frames for generating the mosaic. In this process, it is common to use all or most of the images which requires a lot of resources. Uploading and processing hundreds of images could take several hours to days. Many farmers and crop consultants in developing countries may not have the necessary resources to upload hundreds of images. This study assessed the optimal number of images required to generate an image mosaic for a crop field without any data gaps or distortion. Images were collected at two different heights and directions. First, the mosaic was generated using all (100%) frames followed by subsets containing 90%, through 50% of images. Results obtained will assist us to plan the settings in future flight missions for acquiring optimal number of images required for generating image mosaic.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLIV-M-3-2021-7-2021