Performance of structure-from-motion approach on plant phenotyping using images from smartphone

Abstract

Abstract In recent years, 3D geometry has become increasingly important for plant phenotyping. The purpose of this study is to assess the measuring accuracy of a photogrammetric method based on SfM-MVS using a series of images captured by smartphone. Butterhead lettuce and the rubber plant were the two different plants used in this study. For each single plant, the images were captured from multiple views. A photogrammetry software took image input and converted them into 3D point cloud. Finally, the plant height was computed from the point cloud. Comparing the computed values to the actual values, the RMSE of the plant height was 0.28 and 0.43 for butterhead lettuce and the rubber plant, respectively. A correlation of R 2 ≥ 0.94 to the reference measurement demonstrated that the photogrammetric approach is well suited for evaluation of the plant. The proposed method is simple and cost-effective by using a readily accessible device and software to reconstruct a point cloud model.

Plant phenotyping relevance

スマートフォン画像とSfM-MVSによる3D再構成から植物高を推定する手法を開発・精度検証しており、フェノタイピング手法が研究の中心である。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.