Automated morphological traits extraction for sorghum plants via 3D point cloud data analysis

Xiang, Lirong; Bao, Yuhai; Tang, Lie; Ortiz, Diego; Salas‐Fernandez, Maria G.

doi:10.1016/j.compag.2019.05.043

Cited by 59 publications

(36 citation statements)

References 50 publications

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“…The success of correction largely relies on the detection of the ground. The RANSAC algorithm can detect ground and soil plane of pot well [30] in an indoor pot experiment. In our study, the RANSAC algorithm was utilized to detect ground plane (Figure 2d).…”

Section: Processing Of Point Cloud Of Individual Plantmentioning

confidence: 99%

“…The correlations between these phenotypic traits and biomass may be lower than between total leaf area and biomass, but it is easier to measure. In addition, researchers have found that stem volume has more advantages than total leaf area in estimating biomass at the later stage of plant growth [30].…”

Section: Estimation Of Biomass Using Plant Phenotypic Traitsmentioning

confidence: 99%

“…Structure from motion (SFM), featured as lightweight and cheap [17,26], is a commonly used passive technology in phenotypic traits quantification [27,28], biomass estimation [29,30] and yield prediction [31]. A previous study achieved high precision for leaf area and volumetric shape of taproots of sugar beet in the laboratory [18].…”

Section: Introductionmentioning

confidence: 99%

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Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

et al. 2020

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Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.

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Section: Processing Of Point Cloud Of Individual Plantmentioning

confidence: 99%

Section: Estimation Of Biomass Using Plant Phenotypic Traitsmentioning

confidence: 99%

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Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

et al. 2020

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“…The multi-view point clouds were filtered and then registered to a single point cloud ( Figures 7A). To compute the component phenotypes, a point cloud skeletonization method was used to analyze the maize plant architecture ( Figures 7B and 7C) (Bao et al, 2019a;Xiang et al, 2019). A series of morphological traits were automatically extracted.…”

Section: Plantsmentioning

confidence: 99%

Robotic Assay for Drought (RoAD): An Automated Phenotyping System for Brassinosteroid and Drought Response

Xiang

Nolan

Bao

et al. 2020

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Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed on petri plates and/or in a lowthroughput manner. Additionally, the BR pathway has extensive crosstalk with drought responses, but drought experiments are time-consuming and difficult to control. Thus, we developed Robotic Assay for Drought (RoAD) to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction in order to accurately measure traits in 2-dimensional (2D) and 3-dimensional (3D) spaces including plant surface area, leaf length, and leaf width. We then applied machine learning algorithms that utilized the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants, providing insights into the BRmediated control of plant growth and stress responses. Arabidopsis thaliana | Zea mays | brassinosteroid | drought | leaf segmentation | phenotypic traits | plant growth | 2D and 3D imaging

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“…Merging these different inflectional terms into a single term is called stemming or lemmatization [47]. Stemming implements a heuristic-based technique [48] to remove characters at the end, while lemmatization practices employ principled approaches to reduce inflectional forms to a common base form [49] by recursive processing in different layers. Miller [50] extracted words from the WordNet dictionary, but this was limited to the convenience of human readers and a combination of traditional computing with lexicographic information.…”

Section: Lemmatizationmentioning

confidence: 99%

Detection of Sensitive Data to Counter Global Terrorism

et al. 2019

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Global terrorism has created challenges to the criminal justice system due to its abnormal activities, which lead to financial loss, cyberwar, and cyber-crime. Therefore, it is a global challenge to monitor terrorist group activities by mining criminal information accurately from big data for the estimation of potential risk at national and international levels. Many conventional methods of computation have successfully been implemented, but there is little or no literature to be found that solves these issues through the use of big data analytical tools and techniques. To fill this literature gap, this research is aimed at the determination of accurate criminal data from the huge mass of varieties of data using Hadoop clusters to support Social Justice Organizations in combating terrorist activities on a global scale. To achieve this goal, several algorithmic approaches, including parallelization, annotators and annotations, lemmatization, stop word Remover, term frequency and inverse document frequency, and singular value decomposition, were successfully implemented. The success of this work is empirically compared using the same hardware, software, and system configuration. Moreover, the efficacy of the experiment was tested with criminal data with respect to concepts and matching scores. Eventually, the experimental results showed that the proposed approach was able to expose criminal data with 100% accuracy, while matching of multiple criminal terms with documents had 80% accuracy; the performance of this method was also proved in multiple node clusters. Finally, the reported research creates new ways of thinking for security agencies in combating terrorism at global scale.

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Automated morphological traits extraction for sorghum plants via 3D point cloud data analysis

Cited by 59 publications

References 50 publications

Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

Robotic Assay for Drought (RoAD): An Automated Phenotyping System for Brassinosteroid and Drought Response

Detection of Sensitive Data to Counter Global Terrorism

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