Photogrammetric and LiDAR Scanning with iPhone 13 Pro: Accuracy, Precision and Field Application on Hazelnut Trees

Abstract

Accurate estimation of tree structural and morphological parameters is essential in precision fruit farming, supporting optimised irrigation management, biomass estimation and carbon stock assessment. While traditional field-based measurements remain widely used, they are often time-consuming and subject to operator-induced errors. In recent years, Terrestrial Laser Scanning (TLS) and UAV-based photogrammetry have been successfully employed to generate high-resolution 3D reconstructions of plants; however, their cost and operational constraints limit their scalability in routine field applications. This study investigates the performances of a low-cost, consumer-grade device-the iPhone 13 Pro equipped with an integrated LiDAR sensor and RGB camera-for 3D scanning of fruit tree structures. Cylindrical targets with known geometric dimensions were scanned using both the LiDAR and photogrammetric (Photo) modes of the Polycam© application, with accuracy and precision assessed by comparing extracted measurements to reference values. Field applicability was also tested on hazelnut trees, assessing height, stem diameter and leaf area: the Photo mode delivered the highest accuracy (systematic error of 0.007 m and R 2 = 0.99) and strong agreement with manual leaf measurements (R 2 = 0.93). These results demonstrate that smartphone-based 3D scanning can provide a practical, low-cost approach for structural characterisation in fruit orchards, supporting more efficient crop monitoring.

Code and data availability

The supplied article blocks describe smartphone LiDAR/photogrammetry phenotyping of hazelnut trees and cylindrical targets, but contain no data availability statement, public dataset deposit, author code repository, or supplement with point clouds/measurements. Only generic tools (Polycam, CloudCompare, ImageJ, R) are,

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