Towards an optimized method of olive tree crown volume measurement

Area of Science:

• Agricultural Engineering
• Forestry Science
• Remote Sensing

Background:

• Accurate characterization of large isolated olive tree crowns is crucial for optimizing spray doses in 3D crop agriculture.
• Laser sensors (LiDAR) offer high accuracy but are time-consuming and require specialized knowledge.
• A simpler, more accessible method for crown characterization is needed.

Purpose of the Study:

• To evaluate and compare the accuracy of three simpler crown characterization methods against LiDAR measurements.
• To determine the suitability of these methods across different olive tree plantation types (intensive, traditional one-trunked, traditional).

Main Methods:

• Comparison of Vertical Crown Projected Area (VCPA), Ellipsoid Volume (VE), and Tree Silhouette Volume (VTS) methods with LiDAR data.
• Field trials conducted on 55 olive trees across three distinct plantation types.
• Statistical analysis including coefficient of determination (R2) and correlation analysis.

Main Results:

• All three evaluated methods (VCPA, VE, VTS) demonstrated high accuracy in estimating olive tree crown volume, with R2 values of 0.783, 0.843, and 0.824, respectively.
• Discrepancies were observed when analyzing different plantation types individually, particularly for traditional trees.
• The Mean Vector calculation within the VCPA method showed the highest correlation with LiDAR volume for traditional trees.

Conclusions:

• VCPA, VE, and VTS are viable alternatives to LiDAR for estimating olive tree crown volume in general.
• The VCPA method, specifically using the Mean Vector, is highly recommended for characterizing traditional olive tree plantations due to its superior correlation with LiDAR.
• Simpler methods can enhance the efficiency of precision agriculture practices in olive cultivation.