Point cloud registration method for maize plants based on conical surface fitting—ICP

Zhang, Kai’xing; Chen, He; Wu, Hao; Zhao, Xingbo; Zhou, Chang’an

doi:10.1038/s41598-022-10921-6

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…In this study, the image-based 3D reconstruction method with simple operations was used, where the RGB camera, which was cheap, could be integrated onto the self-established phenotype platform, thereby providing an effective solution to the extraction of crop phenotypes. In addition, studies on 3D reconstruction of maize have been more focused on the ears and grains stage (Ma et al, 2019;Wang et al, 2019;Zhu et al, 2020;Zhang et al, 2022a), while less attention has been paid to 3D reconstruction of seedlings.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences

Liu²,

Zhang³

et al. 2022

Front. Plant Sci.

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As an important method for crop phenotype quantification, three-dimensional (3D) reconstruction is of critical importance for exploring the phenotypic characteristics of crops. In this study, maize seedlings were subjected to 3D reconstruction based on the imaging technology, and their phenotypic characters were analyzed. In the first stage, a multi-view image sequence was acquired via an RGB camera and video frame extraction method, followed by 3D reconstruction of maize based on structure from motion algorithm. Next, the original point cloud data of maize were preprocessed through Euclidean clustering algorithm, color filtering algorithm and point cloud voxel filtering algorithm to obtain a point cloud model of maize. In the second stage, the phenotypic parameters in the development process of maize seedlings were analyzed, and the maize plant height, leaf length, relative leaf area and leaf width measured through point cloud were compared with the corresponding manually measured values, and the two were highly correlated, with the coefficient of determination (R2) of 0.991, 0.989, 0.926 and 0.963, respectively. In addition, the errors generated between the two were also analyzed, and results reflected that the proposed method was capable of rapid, accurate and nondestructive extraction. In the third stage, maize stem leaves were segmented and identified through the region growing segmentation algorithm, and the expected segmentation effect was achieved. In general, the proposed method could accurately construct the 3D morphology of maize plants, segment maize leaves, and nondestructively and accurately extract the phenotypic parameters of maize plants, thus providing a data support for the research on maize phenotypes.

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Section: Discussionmentioning

confidence: 99%

Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences

Liu²,

Zhang³

et al. 2022

Front. Plant Sci.

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“…The point cloud filtering stage utilized the passthrough filter algorithm and statistical outlier removal filtering algorithm to purify the point cloud data. The Iterative Closest Point (ICP) [33] algorithm was used in the point cloud alignment stage to realize the point cloud alignment of the maize plants, while the plant segmentation stage utilizes the supervoxel clustering algorithm to realize the segmentation of the plants. The next step was to measure the height of the maize point cloud plants.…”

Section: Standardization Of Maize Plant Height Measurementmentioning

confidence: 99%

Multitemporal Field-Based Maize Plant Height Information Extraction and Verification Using Solid-State LiDAR

Zhao,

Chen,

Zhou

et al. 2024

Agronomy

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Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize’s original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R2 value attained is 0.99. These metrics collectively attest to the method’s ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland.

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“…Using Kinect for acquiring a 3D point cloud data can be found in several studies including Wang et al [44] on lettuce, González et al [45] on tomato seedling, Zhang et al [46] on pumpkin roots, and Zhang et al [47] on maize plants.…”

Section: Active 3d Imaging Approachesmentioning

confidence: 99%

“…Iterative Closest Point (ICP) Barley [156] Maize (Corn) [34,38,47,129,227] Pepper [155] Rapeseed (Rape) [33,125] Thale cress (Arabidopsis) [156] Tomato [34] Minimizes distances between two point clouds. Often used to obtain a full 3D reconstruction from multiple 3D scans which capture the object from different angles [112,[146][147][148][149].…”

Section: Generalized Voxel Coloringmentioning

confidence: 99%

“…Apple tree/orchard [110,341] Calathea makoyana [111] Cyclamen [40] Epipremnum aureum [111] Hedera nepalensis [111] Hydrangea [40] Lettuce [44] Maize (Corn) [13,38,47] Monstera deliciosa [111] Orchidaceae [40] Paprika [109] Pelargonium [40] Pepper [155] Pumpkin [46] Rapeseed [33] Rosebush [110] Sorghum [39] Soybean [43] Sugar beet [13,36] Sunflower [13] Tomato [45] Wheat [36] Yucca [110] ToF cameras use time between emitted light and reflected light from thousands of points to conduct 3D images.…”

Section: Time Of Flight (Tof) (Including Microsoft Kinect Sensors)mentioning

confidence: 99%

See 1 more Smart Citation

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Harandi

Vandenberghe²,

Vankerschaver

et al. 2023

Plant Methods

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Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.

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Point cloud registration method for maize plants based on conical surface fitting—ICP

Cited by 10 publications

References 27 publications

Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences

Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences

Multitemporal Field-Based Maize Plant Height Information Extraction and Verification Using Solid-State LiDAR

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

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