Automatic stem-leaf segmentation of maize shoots using three-dimensional point cloud

Teng, Miao; Zhu, Chao; Xu, Tongyu; Yang, Tao; Li, Na; Zhou, Yuncheng; Deng, Hongbo

doi:10.1016/j.compag.2021.106310

Cited by 46 publications

(21 citation statements)

References 22 publications

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“…For example, the segmentation of individual plants from point clouds of corn crops was discussed in (Zermas et al 2018). Stem-leaf segmentation from a point cloud was also developed in Miao et al (2021), Sodhi et al (2017) by solving the classification of stems vs. leaves. The segmentation of the voxel-based reconstruction was considered in das Choudhury et al (2020).…”

Section: Reconstructing Small Plants With Skeletonization and Segment...mentioning

confidence: 99%

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

Okura

2022

Breed. Sci.

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This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants ( modeling ) and 2) creating models from real-world plants ( reconstruction ). This paper also discusses the limitations of current methods and possible future directions.

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Section: Reconstructing Small Plants With Skeletonization and Segment...mentioning

confidence: 99%

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

Okura

2022

Breed. Sci.

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“…Measurement using three-dimensional (3D) technology is an active area of research in agriculture [23]. It can be applied in measurements of leaf area, leaf angle, stem and shoots, fruit, and seeds [24,25]. In addition to conventional 2D traits, such as length, width, and the projected perimeter and area, 3D technology can obtain additional traits, such as volume, surface area, thickness, and other shape traits.…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Legume Seed Phenotyping Using a Handheld 3D Laser Scanner

2022

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High-throughput phenotyping involves many samples and diverse trait types. For the goal of automatic measurement and batch data processing, a novel method for high-throughput legume seed phenotyping is proposed. A pipeline of automatic data acquisition and processing, including point cloud acquisition, single-seed extraction, pose normalization, three-dimensional (3D) reconstruction, and trait estimation, is proposed. First, a handheld laser scanner is used to obtain the legume seed point clouds in batches. Second, a combined segmentation method using the RANSAC method, the Euclidean segmentation method, and the dimensionality of the features is proposed to conduct single-seed extraction. Third, a coordinate rotation method based on PCA and the table normal is proposed to conduct pose normalization. Fourth, a fast symmetry-based 3D reconstruction method is built to reconstruct a 3D model of the single seed, and the Poisson surface reconstruction method is used for surface reconstruction. Finally, 34 traits, including 11 morphological traits, 11 scale factors, and 12 shape factors, are automatically calculated. A total of 2500 samples of five kinds of legume seeds are measured. Experimental results show that the average accuracies of scanning and segmentation are 99.52% and 100%, respectively. The overall average reconstruction error is 0.014 mm. The average morphological trait measurement accuracy is submillimeter, and the average relative percentage error is within 3%. The proposed method provides a feasible method of batch data acquisition and processing, which will facilitate the automation in high-throughput legume seed phenotyping.

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“…Elnashef et al [23] proposed a tensor-based 3D plant model segmentation algorithm to divide the point cloud into leaves and stems for wheat, maize, and cotton seedlings grown in laboratory. Miao et al [24] applied the Laplacian based method to 3D point cloud of the field grown maize in order to obtain the skeleton and to classify the leaves and stems. This method could also be used to segment newly grown leaves.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud

et al. 2022

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Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R2 (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants.

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Automatic stem-leaf segmentation of maize shoots using three-dimensional point cloud

Cited by 46 publications

References 22 publications

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

High-Throughput Legume Seed Phenotyping Using a Handheld 3D Laser Scanner

Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud

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