The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 631–635, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-631-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 631–635, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-631-2019

  04 Jun 2019

04 Jun 2019

AUTOMATIC APPLE TREE BLOSSOM ESTIMATION FROM UAV RGB IMAGERY

A. Tubau Comas, J. Valente, and L. Kooistra A. Tubau Comas et al.
  • Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands

Keywords: thinning, apple orchard, flowering intensity, UAV, image segmentation

Abstract. Apple trees often produce high amount of fruits, which results in small, low quality fruits. Thinning in apple orchards is used to improve the quality of the apples by reducing the number of flowers or fruits the tree is producing. The current method used to estimate how much thinning is necessary is to measure flowering intensity, currently done by human visual inspection of trees in the orchard. The use of images of apple trees from ground-level to measure flowering intensity and its spatial variation through orchards has been researched with promising results. This research explores the potential of UAV RGB high-resolution imagery to measure flowering intensity. Image segmentation techniques have been used to segment the white pixels, which correspond to the apple flowers, of the orthophoto and the single photos. Single trees have been cropped from the single photos and from the orthophoto, and correlation has been measured between percentage of white pixels per tree and flowering intensity and between percentage of white pixels per tree and flower clusters. The resulting correlation is low, with a maximum of 0.54 for the correlation between white pixels per tree and flower clusters when using the ortophoto. Those results show the complexity of working with drone images, but there are still alternative approaches that have to investigated before discarding the use of UAV RGB imagery for estimation of flowering intensity.