Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks

Ao, Zurui; Wu, Fangfang; Hu, Saihan; Sun, Ying; Su, Yanjun; Xin, Qinchuan

doi:10.1016/j.cj.2021.10.010

Cited by 38 publications

(27 citation statements)

References 56 publications

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“…They finally employed a 3D deep learning network ( Jin et al., 2019 ) to segment the stem and leaf. Ao et al. (2022) first performed stem and leaf semantic recognition through PointCNN ( Li et al., 2018 ) and then used DBSCAN clustering to cluster the stems and leaves into single plants through connection relationships for organ segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…They finally employed a 3D deep learning network (Jin et al, 2019) to segment the stem and leaf. Ao et al (2022) first performed stem and leaf semantic recognition through PointCNN (Li et al, 2018) and then used DBSCAN clustering to cluster the stems and leaves into single plants through connection relationships for organ segmentation. DFSP enables group segmentation and organ segmentation through a unified segmentation process, thereby reducing the complexity of developing a maize point cloud segmentation tool.…”

Section: B C Amentioning

confidence: 99%

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DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot

et al. 2023

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The 3D point cloud data are used to analyze plant morphological structure. Organ segmentation of a single plant can be directly used to determine the accuracy and reliability of organ-level phenotypic estimation in a point-cloud study. However, it is difficult to achieve a high-precision, automatic, and fast plant point cloud segmentation. Besides, a few methods can easily integrate the global structural features and local morphological features of point clouds relatively at a reduced cost. In this paper, a distance field-based segmentation pipeline (DFSP) which could code the global spatial structure and local connection of a plant was developed to realize rapid organ location and segmentation. The terminal point clouds of different plant organs were first extracted via DFSP during the stem-leaf segmentation, followed by the identification of the low-end point cloud of maize stem based on the local geometric features. The regional growth was then combined to obtain a stem point cloud. Finally, the instance segmentation of the leaf point cloud was realized using DFSP. The segmentation method was tested on 420 maize and compared with the manually obtained ground truth. Notably, DFSP had an average processing time of 1.52 s for about 15,000 points of maize plant data. The mean precision, recall, and micro F1 score of the DFSP segmentation algorithm were 0.905, 0.899, and 0.902, respectively. These findings suggest that DFSP can accurately, rapidly, and automatically achieve maize stem-leaf segmentation tasks and could be effective in maize phenotype research. The source code can be found at https://github.com/syau-miao/DFSP.git.

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Section: Discussionmentioning

confidence: 99%

Section: B C Amentioning

confidence: 99%

DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot

et al. 2023

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“…Terrestrial lidar is showing great promise in the acquisition of plant structure at extremely fine scales [52,53]. Additionally, the timing of leaf senescence could be potentially studied using hypertemporal multispectral and RGB imagery.…”

Section: Discussionmentioning

confidence: 99%

Comparison between Field Measured and UAV-Derived Pistachio Tree Crown Characteristics throughout a Growing Season

Jacygrad

Kelly

Hogan

et al. 2022

Drones

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Monitoring individual tree crown characteristics is an important component of smart agriculture and is crucial for orchard management. We focused on understanding how UAV imagery taken across one growing season can help understand and predict the growth and development of pistachio trees grown from rootstock seedlings. Tree crown characteristics (i.e., height, size, shape, and mean normalized difference vegetation index (NDVI)) were derived using an object-based image analysis method with multispectral Uncrewed Aerial Vehicles (UAV) imagery flown seven times over 472 five-year-old pistachio trees in 2018. These imagery-derived metrics were compared with field-collected tree characteristics (tree height, trunk caliper, crown height, width and volume, and leaf development status) collected over two months in 2018. The UAV method captured seasonal development of tree crowns well. UAV-derived tree characteristics were better correlated with the field tree characteristics when recorded between May and November, with high overall correlations in November. The highest correlation (R2 = 0.774) was found between trunk caliper and June UAV crown size. The weakest correlations between UAV and field traits were found in March and December. Spring leaf development stage was most variable, and mean NDVI values were lowest in March, when leaf development starts. Mean NDVI increased orchard-wide by May, and was consistently high through November. This study showcased the benefits of timely, detailed drone imagery for orchard managers.

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“…The LiDAR laser can partially penetrate the vegetation canopy and is not vulnerable to natural sunlight [117], so LiDAR sensors have been mounted on the ground and aerial phenotyping platforms to measure various phenotypic traits [114,118,119]. Although LiDAR can be used to monitor the 3D surfaces of plants from one meter up to thousands of meters [113,119], there remain some disadvantages, such as matching errors caused by illumination and shadowing, incomplete reconstruction data caused by occlusion, and tradeoffs between accuracy and efficiency [113,117]. That is the reason why LiDAR has low accuracy when performing large-scale scanning.…”

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

“…For example, after cutting the point cloud to the region of interest and performing a data cleaning step, non-complex parameters such as height and width can be derived [143]. Machine learning approaches can then be employed for further processing, such as segmenting plant organs such as leaves, stems, and flowers [117,[144][145][146]. For instance, Li [116] used a U-Net architecture, which is a popular CNN architecture for image segmentation tasks, and modified the U-Net mode to take the point cloud data as input, then output a segmentation mask that identifies the different plant organs.…”

Section: Data Processing Of 3d Phenotypingmentioning

confidence: 99%

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping

et al. 2023

Plants

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Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.

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Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks

Cited by 38 publications

References 56 publications

DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot

DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot

Comparison between Field Measured and UAV-Derived Pistachio Tree Crown Characteristics throughout a Growing Season

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping

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