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Field Robot for High-throughput and High-resolution 3D Plant Phenotyping

arXiv (Cornell University) · 17 Oct 2023 · 10.48550/arxiv.2310.11516

Abstract

With the need to feed a growing world population, the efficiency of crop production is of paramount importance. To support breeding and field management, various characteristics of the plant phenotype need to be measured -- a time-consuming process when performed manually. We present a robotic platform equipped with multiple laser and camera sensors for high-throughput, high-resolution in-field plant scanning. We create digital twins of the plants through 3D reconstruction. This allows the estimation of phenotypic traits such as leaf area, leaf angle, and plant height. We validate our system on a real field, where we reconstruct accurate point clouds and meshes of sugar beet, soybean, and maize.

Plant phenotyping relevance

レーザー・カメラ搭載ロボットによる3D植物スキャンと再構成を開発し、葉面積・葉角度・草丈を推定、圃場で検証しているため、表現型取得手法が中心です。

abstractWe present a robotic platform equipped with multiple laser and camera sensors for high-throughput, high-resolution in-field plant scanning.
abstractWe create digital twins of the plants through 3D reconstruction. This allows the estimation of phenotypic traits such as leaf area, leaf angle, and plant height.
abstractWe validate our system on a real field, where we reconstruct accurate point clouds and meshes of sugar beet, soybean, and maize.

Code and data availability

The paper describes a field phenotyping robot and its datasets (≈2.3 TB images, ≈100 GB point clouds), but no public deposit, availability statement, or authors' URL for these data or code appears in the supplied blocks. All allowed URLs are cited only as third-party tools, platforms, or libraries (TerraSentia, Robotti

No evidence-backed public reproduction asset is currently recorded.

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