A Novel Technique Using Planar Area and Ground Shadows Calculated from UAV RGB Imagery to Estimate Pistachio Tree (Pistacia vera L.) Canopy Volume

Sergio Vélez*, Rubén Vacas, Hugo Martín, David Ruano-Rosa, Sara Álvarez

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Interest in pistachios has increased in recent years due to their healthy nutritional profile and high profitability. In pistachio trees, as in other woody crops, the volume of the canopy is a key factor that affects the pistachio crop load, water requirements, and quality. However, canopy/crown monitoring is time-consuming and labor-intensive, as it is traditionally carried out by measuring tree dimensions in the field. Therefore, methods for rapid tree canopy characterization are needed for providing accurate information that can be used for management decisions. The present study focuses on developing a new, fast, and low-cost technique, based on two main steps, for estimating the canopy volume in pistachio trees. The first step is based on adequately planning the UAV (unmanned aerial vehicle) flight according to light conditions and segmenting the RGB (Red, Green, Blue) imagery using machine learning methods. The second step is based on measuring vegetation planar area and ground shadows using two methodological approaches: a pixel-based classification approach and an OBIA (object-based image analysis) approach. The results show statistically significant linear relationships (p < 0.05) between the ground-truth data and the estimated volume of pistachio tree crowns, with R2 > 0.8 (pixel-based classification) and R2 > 0.9 (OBIA). The proposed methodologies show potential benefits for accurately monitoring the vegetation of the trees. Moreover, the method is compatible with other remote sensing techniques, usually performed at solar noon, so UAV operators can plan a flexible working day. Further research is needed to verify whether these results can be extrapolated to other woody crops.

Original languageEnglish
Article number6006
JournalRemote Sensing
Volume14
Issue number23
DOIs
Publication statusPublished - 27 Nov 2022

Keywords

  • aerial
  • drone
  • image analysis
  • leaf area
  • machine learning
  • pistachio
  • precision agriculture
  • random forest
  • spatial variability

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