The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-M-3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 7–12, 2021
https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-7-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 7–12, 2021
https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-7-2021

  10 Aug 2021

10 Aug 2021

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

P. Bupathy1, R. Sivanpillai2, V. V. Sajithvariyar1, and V. Sowmya1 P. Bupathy et al.
  • 1Center for Computational Engineering and Networking, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, TN 641112, India
  • 2Wyoming GIS Center, University of Wyoming, Laramie, WY 82072, USA

Keywords: Precision agriculture, Drones, Aerial images, Image Alignment, Mosaics

Abstract. 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.