Using Visible and Thermal Images by an Unmanned Aerial Vehicle to Monitor the Plant Water Status, Canopy Growth and Yield of Olive Trees (cvs. Frantoio and Leccino) under Different Irrigation Regimes

Abstract

An efficient management of water relies on the correct estimation of tree water requirements and the accurate monitoring of tree water status and canopy growth. This study aims to test the suitability of visible and thermal images acquired by an unmanned aerial vehicle (UAV) for monitoring tree water status and canopy growth in an irrigation experiment. We used mature olive trees of two cultivars subjected to full irrigation, deficit irrigation (41–44% of full irrigation), or rainfed conditions. Deficit irrigation had limited or no effect on fruit and oil yield. There was a significant relationship between the remotely sensed crop water stress index derived from thermal images and the stem water potential (R2 = 0.83). The RGB images by UAV allowed to estimate tree canopy volume and were able to detect differences in canopy growth across irrigation regimes. A significant relationship between canopy volume and LAI was found for both cultivars (R2 of 0.84 and 0.88 for Frantoio and Leccino, respectively). Our results confirm the positive effects of deficit irrigation strategies to save relevant volumes of water and show that aerial images from UAV can be used to monitor both tree water stress and its effects on canopy growth and yield.

Plant phenotyping relevance

UAVの可視・熱画像を用いてオリーブ樹の水ストレス、樹冠体積、成長を推定し、地上測定やLAIとの関係で検証しており、植物形質取得手法が研究の中心です。

Code and data availability

The supplied article blocks describe UAV visible/thermal image acquisition and processing (Agisoft Metashape, ArcGIS) for olive phenotyping, but contain no data availability statement, no public repository deposit, no author code/model release, and no public URL for images, trait data, or analysis workflows. No paper-­

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