Time-of-Flight (ToF) camera technology for high throughput holistic phenotyping and canopy volume measurement of horticultural crops

Abstract

• ToF camera was evaluated for 3D phenotyping and canopy volume estimation. • Strong correlation was observed between ToF imagery and manual methods. • ToF technology could accurately estimate canopy volume in horticultural crops. • Study highlights the potential of ToF imagery for smart farm management. Noninvasive and accurate quantification of plant architectural traits remains a persistent challenge in horticultural crop breeding and high-throughput phenotyping (HTPP), largely due to labor constraints and limited availability of scalable 3D sensing technologies. Time-of-Flight (ToF) imaging offers a promising active sensing approach capable of generating high-resolution digital replicas of plant canopies for structural trait analysis. This study evaluated the feasibility of deploying 3D ToF imaging for digital phenotyping of three horticultural and ornamental crops, guava, brinjal, and jasmine with a focus on canopy volume estimation. High-resolution 3D point clouds were acquired for five plants per crop across three growth stages during the summer season of 2024. Structural parameters including plant height, width North–South (W NS ), width East–West (W EW ), and 3D aspect ratio were extracted and used to compute canopy volume via a voxel grid method. To validate accuracy, ToF-derived canopy volumes were compared against manually measured volumes estimated using the prolate spheroid volume (PSV) method. The ToF-based digital phenotyping framework demonstrated strong agreement with manual measurements across all crops. Guava exhibited the highest accuracy at the intermediate growth stage (R² = 1.0; RMSE = 0.0004), while brinjal (R² = 0.908; RMSE = 0.004) and jasmine (R² = 0.935; RMSE = 0.4351) showed robust performance at the full-grown stage. Those regression relationships were statistically significant ( p < 0.05), confirming the reliability of ToF-based canopy reconstruction for structural phenotyping. Overall, the findings demonstrate that ToF imaging provides a robust, noninvasive, and scalable framework for digital canopy phenotyping in horticultural crops, supporting precision breeding and structural trait monitoring applications in field environments.

Plant phenotyping relevance

ToFによる3D画像取得とキャノピー体積・構造形質の抽出を開発・評価し、手動測定との比較検証を行っており、植物表現型取得法が研究の中心である。

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