Olive Actual "on Year" Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery

Area of Science:

• Agricultural Science
• Remote Sensing
• Agronomy

Background:

• Olive production is vital in the Mediterranean, with profitability influenced by yield, costs, and prices.
• Actual Yield (AY) is a key metric, but predicting it accurately remains a challenge for olive growers.

Purpose of the Study:

• To establish a relationship between olive tree canopy geometry and actual yield (AY).
• To develop a simple and cost-effective method for forecasting AY using remote sensing data.

Main Methods:

• Utilized unmanned aerial systems (UAS) to capture orthoimages for calculating individual tree crown area.
• Developed regression equations linking manual canopy volume and individual crown area to AY.
• Analyzed yield variations between irrigated and rainfed olive orchards.

Main Results:

• Yield levels differed significantly between irrigated (7000-17,000 kg ha⁻¹) and rainfed (4000-7000 kg ha⁻¹) orchards.
• Individual tree crown area derived from UAS orthoimages proved to be a reliable predictor of AY.
• A thematic map was created to visualize spatial AY variability.

Conclusions:

• Forecasting olive AY using individual crown area is a practical and economical approach.
• The developed method offers a valuable tool for farmers, insurance, market analysis, and identifying agronomic issues.
• Remote sensing analysis of canopy geometry enhances olive orchard management and productivity prediction.