The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1211–1215, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1211-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1211–1215, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1211-2020

  14 Aug 2020

14 Aug 2020

DETECTING AND COUNTING ORCHARD TREES ON UNMANNED AERIAL VEHICLE (UAV)-BASED IMAGES USING ENTROPY AND NDVI FEATURES

A. Haddadi1,2, B. Leblon1, and G. Patterson2 A. Haddadi et al.
  • 1Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton (NB), Canada
  • 2Paradigm Precision Department, A&L Canada Laboratories Inc., London (ON), Canada

Keywords: image processing, precision agriculture, textural analysis, tree counting, unmanned aerial vehicle, vegetation indices

Abstract. Multispectral images were acquired by a camera on board an Unmanned Aerial Vehicle (UAV) over two apple orchards in Prince Edward Island in summer 2016. A method was developed to automatically detect rows of planted trees and trees in each row using the normalized difference vegetation index (NDVI) image and its related entropy and variance images. The image-based tree position was then compared to the actual tree location measured in the field. We achieved an accuracy of 93% between the estimated and measured number of trees in both orchards.