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Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling.

F M Jiménez-Brenes¹, F López-Granados¹, A I de Castro¹

¹Institute for Sustainable Agriculture, CSIC, 14004 Córdoba, Spain.

|July 12, 2017

Summary

Unmanned aerial vehicle (UAV) technology combined with object-based image analysis (OBIA) accurately monitors tree pruning impacts. This precision agriculture approach quantifies pruning effects on hundreds of trees, offering economic and environmental benefits.

Keywords:

Crown volume Object-based image analysis Precision agriculture Remote sensing Unmanned aerial vehicle

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Area of Science:

Agricultural Science
Remote Sensing
Forestry

Background:

Tree pruning is crucial for crop yield, pest control, and soil management but involves labor-intensive field measurements.
Traditional methods for assessing pruning impacts are often inconsistent due to tree geometry and require extensive fieldwork.

Purpose of the Study:

To develop and validate an innovative procedure for multi-temporal 3D monitoring of pruned trees using UAV technology and OBIA.
To quantify the impacts of three distinct pruning strategies (traditional, adapted, mechanical) on olive tree dimensions over time.

Main Methods:

Utilized unmanned aerial vehicle (UAV) technology to capture multi-temporal 3D images of olive trees before, after, and one year post-pruning.
Applied advanced object-based image analysis (OBIA) to automatically identify trees and compute 3D dimensions (projected canopy area, tree height, crown volume).

Main Results:

The UAV-OBIA procedure accurately computed 3D tree dimensions for hundreds of trees across three time points.
Adapted pruning was the most aggressive (38.95% area, 42.05% volume reduction), followed by traditional pruning (33.02% area, 35.72% volume reduction).
Mechanical pruning caused the greatest height decrease (12.15%), while annual growth varied based on pruning severity and type.

Conclusions:

Combining UAV imagery and OBIA offers an efficient, accurate method for assessing pruning impacts on numerous trees with minimal field effort.
Pruning type and severity significantly influence foliage loss and subsequent canopy growth, providing insights for tailored management.
This technology generates valuable geospatial data for precision agriculture, enabling site-specific crop management and yielding economic and environmental advantages.