Applications of 3D Reconstruction Techniques in Crop Canopy Phenotyping: A Review

Abstract

Amid growing challenges to global food security, high-throughput crop phenotyping has become an essential tool, playing a critical role in genetic improvement, biomass estimation, and disease prevention. Unlike controlled laboratory environments, field-based phenotypic data collection is highly vulnerable to unpredictable factors, significantly complicating the data acquisition process. As a result, the choice of appropriate data collection equipment and processing methods has become a central focus of research. Currently, three key technologies for extracting crop phenotypic parameters are Light Detection and Ranging (LiDAR), Multi-View Stereo (MVS), and depth camera systems. LiDAR is valued for its rapid data acquisition and high-quality point cloud output, despite its substantial cost. MVS offers the potential to combine low-cost deployment with high-resolution point cloud generation, though challenges remain in the complexity and efficiency of point cloud processing. Depth cameras strike a favorable balance between processing speed, accuracy, and cost-effectiveness, yet their performance can be influenced by ambient conditions such as lighting. Data processing techniques primarily involve point cloud denoising, registration, segmentation, and reconstruction. This review summarizes advances over the past five years in 3D reconstruction technologies—focusing on both hardware and point cloud processing methods—with the aim of supporting efficient and accurate 3D phenotype acquisition in high-throughput crop research.

Plant phenotyping relevance

作物キャノピーの3D形質取得に用いるLiDAR、MVS、深度カメラと点群処理を中心に扱うレビューであり、植物フェノタイピング手法が主題。

Code and data availability

This is a review article of 3D reconstruction techniques in crop canopy phenotyping. The Data Availability Statement explicitly says 'No new data were created or analyzed in this study.' All cited datasets, platforms, and software belong to prior works, not to this paper; no author code, datasets, images, or models are

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