ArticleSensors (Basel, Switzerland)2019
Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Organ-level 3D phenotyping of saffron using a low-cost dual-camera workflow.Plant methods · 2026Article
- Investigation of salt stress effects on maize seedling phenotypic traits based on the PointCornNet point cloud segmentation model.Frontiers in plant science · 2025Article
- Review of applications of artificial intelligence (AI) methods in crop research.Journal of applied genetics · 2024Review
- Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat.AoB PLANTS · 2024Article
- Detection of maize stem diameter by using RGB-D cameras' depth information under selected field condition.Frontiers in plant science · 2024Article
- Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences.Frontiers in plant science · 2022Article
- Automatic Measurement of Morphological Traits of Typical Leaf Samples.Sensors (Basel, Switzerland) · 2021Article
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6 authors.
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Abstract
A reasonable plant type is an essential factor for improving canopy structure, ensuring a reasonable expansion of the leaf area index and obtaining a high-quality spatial distribution of light. It is of great significance in promoting effective selection of the ecological breeding index and production practices for maize. In this study, a method for calculating the phenotypic traits of the maize canopy in three-dimensional (3D) space was proposed, focusing on the problems existing in traditional measurement methods in maize morphological structure research, such as their complex procedures and relatively large error margins. Specifically, the whole maize plant was first scanned with a FastSCAN hand-held scanner to obtain 3D point cloud data for maize. Subsequently, the raw point clouds were simplified by the grid method, and the effect of noise on the quality of the point clouds in maize canopies was further denoised by bilateral filtering. In the last step, the 3D structure of the maize canopy was reconstructed. In accordance with the 3D reconstruction of the maize canopy, the phenotypic traits of the maize canopy, such as plant height, stem diameter and canopy breadth, were calculated by means of a fitting sphere and a fitting cylinder. Thereafter, multiple regression analysis was carried out, focusing on the calculated data and the actual measured data to verify the accuracy of the calculation method proposed in this study. The corresponding results showed that the calculated values of plant height, stem diameter and plant width based on 3D scanning were highly correlated with the actual measured data, and the determinant coefficients R² were 0.9807, 0.8907 and 0.9562, respectively. In summary, the method proposed in this study can accurately measure the phenotypic traits of maize. Significantly, these research findings provide technical support for further research on the phenotypic traits of other crops and on variety breeding.
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