High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning

Abstract

Beyond the use of 2D images, the analysis of 3D images is also necessary for analyzing the phenomics of crop plants. In this study, we configured a system and implemented an algorithm for the 3D image reconstruction of red pepper plant (Capsicum annuum L.), as well as its automatic analysis. A Kinect v2 with a depth sensor and a high-resolution RGB camera were used to obtain more accurate reconstructed 3D images. The reconstructed 3D images were compared with conventional reconstructed images, and the data of the reconstructed images were analyzed with respect to their directly measured features and accuracy, such as leaf number, width, and plant height. Several algorithms for image extraction and segmentation were applied for automatic analysis. The results showed that the proposed method showed an error of about 5 mm or less when reconstructing and analyzing 3D images, and was suitable for phenotypic analysis. The images and analysis algorithms obtained by the 3D reconstruction method are expected to be applied to various image processing studies.

Plant phenotyping relevance

植物の3D画像再構成と自動形質抽出システムの開発が中心であり、葉数・葉幅・草丈を対象に精度評価も行っているため、植物フェノタイピング手法として明確に該当する。

Code and data availability

The paper's red pepper 3D reconstruction images, ground-truth measurements, and analysis algorithms are not publicly deposited; the Data Availability Statement says they are available only on request from the corresponding author. No authors' public code URL is provided; other URLs are generic tools or cited references

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