Estimating Canopy Structure Parameters and Leaf Nitrogen in Olive Orchards Using UAV Imagery Across Two Agro-Ecological Zones in Tunisia

Abstract

Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This study monitored tree structural parameters, leaf area index (LAI), and leaf nitrogen content (%N DW) in two Tunisian olive orchards during 2022 and 2023. UAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach. Target variables of the automated approach included tree-wise estimates of height, projected crown area, and crown volume, as well as raster cell counts of the canopy cloud and spectral indices such as the normalized green-red difference index (NGRDI) and green leaf index (GLI). In addition, the estimated parameters per tree were used to model LAI and leaf nitrogen content. Analyses were conducted separately for trees represented by a high and a low number of points in the dense point cloud. Outcomes were compared to reference data collected in the field on dates close to the UAV flights. The findings showed strong relationships for the projected crown area (R2 = 0.82 and 0.91) and tree height (R2 = 0.89 and 0.88) when compared to reference values. Linear regression models for LAI (R2 = 0.73 and 0.68) and crown volume (R2 = 0.85 and 0.91) estimation also show strong relationships. However, leaf nitrogen estimation was not feasible from RGB spectral index values, as it showed a weak relationship (R2 = 0.34). A dataset with multispectral imagery could overcome this limitation but would increase costs, making it less suitable for the low-budget approach required in price-sensitive farming contexts, particularly in low-income regions.

Plant phenotyping relevance

UAV画像から樹体形状・LAIなどの植物形質を自動推定し、圃場基準値との比較検証を行う手法が研究の中心である。

Code and data availability

The supplied blocks describe UAV RGB imagery, point clouds, field reference measurements, and an R analysis script, but contain no public data or code availability statement, no repository deposit, and no authors' public URL. The analysis script is described as an enhancement of a prior version from Hobart et al. [42]—

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