Combined Structural and Functional 3D Plant Imaging Using Structure from Motion

Abstract

We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the extra green channel. Furthermore, we upsample the images to increase the number of key points. With the same setup, we obtain functional fluorescence information that we map onto the 3D structural plant image, in this way obtaining a combined functional and 3D structural plant image using a single setup.

Plant phenotyping relevance

植物の3D構造と蛍光機能情報を取得・統合する画像計測手法の開発が中心であり、植物病害の非侵襲的フェノタイピングに該当します。

Code and data availability

The paper's Data Availability Statement explicitly deposits the code and datasets for reproducing the SfM 3D plant imaging results in the 4TU repository, with a DOI matching an allowed URL.