Optimizing 3D LiDAR Installation Height for High-Fidelity Canopy Phenotyping in Spindle-Shaped Orchards

Abstract

High-fidelity acquisition of canopy phenotypic data is critical for the advancement of orchard Artificial Intelligence (AI). Yet, an improper Light Detection and Ranging (LiDAR) installation height (IH) frequently induces data occlusion and substantial measurement errors. To address this limitation, this study developed an information collection vehicle (ICV) integrated with a 16-channel three-dimensional (3D) LiDAR to determine the optimal LiDAR IH. Three representative LiDAR IHs (1.4 m, 2.0 m, and 2.6 m) were evaluated on spindle-shaped cherry trees under both forward and reverse driving strategies. Subsequently, a novel 12-zone refined evaluation framework was introduced to quantify localized errors that are conventionally obscured by traditional whole-canopy metrics. Results demonstrated a profound nonlinear relationship between IH and measurement accuracy. Specifically, the 2.0 m IH (approximating the canopy’s geometric center) emerged as the optimal setup, maintaining relative errors (REs) below 5% with minimal dispersion. Conversely, the 2.6 m IH caused lower-canopy volume REs to surge beyond 16% owing to restricted downward viewing angles. Additionally, reverse driving at higher IHs exacerbated mechanical vibrations via the “lever arm effect”, thereby significantly degrading point cloud registration accuracy. Ultimately, these findings underscore the critical necessity of aligning sensors with the canopy geometric center, supplying essential theoretical guidelines for the hardware design of future orchard robots.

Plant phenotyping relevance

果樹キャノピー形質の高精度取得を目的に、LiDAR搭載車両、設置高さ、走行条件、局所誤差評価法を開発・比較検証しており、フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe a custom ICV with 3D LiDAR for canopy phenotyping of cherry trees, including point cloud processing in Python/Open3D, but contain no data availability statement, public dataset deposit, or author code repository. No paper-specific public asset is identified.

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