Estimating 3D Leaf and Stem Shape of Nursery Paprika Plants by a Novel Multi-Camera Photography System

Abstract

For plant breeding and growth monitoring, accurate measurements of plant structure parameters are very crucial. We have, therefore, developed a high efficiency Multi-Camera Photography (MCP) system combining Multi-View Stereovision (MVS) with the Structure from Motion (SfM) algorithm. In this paper, we measured six variables of nursery paprika plants and investigated the accuracy of 3D models reconstructed from photos taken by four lens types at four different positions. The results demonstrated that error between the estimated and measured values was small, and the root-mean-square errors (RMSE) for leaf width/length and stem height/diameter were 1.65 mm (R² = 0.98) and 0.57 mm (R² = 0.99), respectively. The accuracies of the 3D model reconstruction of leaf and stem by a 28-mm lens at the first and third camera positions were the highest, and the number of reconstructed fine-scale 3D model shape surfaces of leaf and stem is the most. The results confirmed the practicability of our new method for the reconstruction of fine-scale plant model and accurate estimation of the plant parameters. They also displayed that our system is a good system for capturing high-resolution 3D images of nursery plants with high efficiency.

Plant phenotyping relevance

幼苗パプリカの葉・茎形状を推定するマルチカメラ3D計測システムを開発し、実測値との精度検証を行っており、植物表現型取得手法が研究の中心である。

Code and data availability

The supplied article blocks describe an MCP-SfM phenotyping system for nursery paprika plants, but contain no public phenotype dataset, image collection, analysis code, or trained model deposit. Processing was done in commercial software (PhotoScan, Microsoft PhotoSynth), and no data or code availability statement with

No evidence-backed public reproduction asset is currently recorded.