Research Article | Open Access

Volume 2026 |Article ID 100122 | https://doi.org/10.1016/j.plaphe.2025.100122

Three-dimensional reconstruction of densely planted rice seedlings based on MultiView images

Zhigang Zhang,^1,2,3 Liwei Wang,^1,2,3 Weiqi Ren,^1,2 Shoutian Dong,^1,2 Shaowen Liu,^1,2 Haoran Xu,^1,2 Yubo Yang,^1,2 Rui Gao ,^1,2 Zhongbin Su ^1,2

¹Institutions of Electrical and Information, Northeast Agricultural University, Harbin, 150030, China
²Key Laboratory of Northeast Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Heilongjiang Province, Harbin, 150030, China
³These authors contributed equally to this work.

Abstract

Three-dimensional(3D) seedling reconstruction technology can provide critical technical support for monitoring plant growth, phenotyping high-throughput plants, and conducting precision agriculture. However, multiview image-based reconstruction methods, which rely on image registration and feature matching, are susceptible to issues such as similar textures and viewpoint differences, leading to matching errors and the loss of key structural information. This can result in local deficiencies and reduced accuracy in the reconstructed models. Therefore, to attain improved reconstruction accuracy under low-cost constraints, deep learning-based feature extraction and matching methods are employed in this study, the SuperPoint network is utilized to increase the robustness of the feature point detection and description processes, and the LightGlue algorithm is introduced to improve the accuracy and stability of matching. Additionally, to reduce the impact of shooting and platform jitter on image quality, a dedicated plant 3D reconstruction platform is designed and constructed, and a dataset of densely planted rice seedlings under light stress conditions is collected, comprising three factors (light quality, light quantity, and the photoperiod) × three levels, totaling nine groups. Experimental results show that the proposed method achieves optimal performance in terms of its point cloud completeness and reprojection error. The phenotypic parameters (e.g., plant height) extracted from the reconstruction data are strongly correlated with the actual measurements (R2 = 0.989, RMSE = 4.54 mm), validating the potential of the proposed method for applications related to simulating plant growth processes, analyzing the effects of environmental factors (e.g., light), and optimizing crop cultivation schemes.