Attribute Analysis of Point Cloud Data With Color Information

Kobayashi, Ichiro; Fujita, Yoichi; Sugihara, Hiromi; Yamamoto, K.

doi:10.2208/jscejcei.67.i_95

Cited by 7 publications

(9 citation statements)

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“…In addition, this method will automatically provide growth monitoring (leaf area, LAI, plant height, and fruit weight) because it will be possible to divide the canopy point cloud data including the greenhouse structural material, and cultivation bench, into plants using color information. In the civil engineering field, several types of objects, including forest and signal, have been separated from point cloud data using color information [ 42 , 43 ]. An editor for point cloud data was recently developed [ 44 ], which is expected to be used in the agricultural field in the future.…”

Section: Discussionmentioning

confidence: 99%

Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner

Ohashi

Ishigami

Gotō

2020

Sensors

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Monitoring the growth of fruit vegetables is essential for the automation of cultivation management, and harvest. The objective of this study is to demonstrate that the current sensor technology can monitor the growth and yield of fruit vegetables such as tomato, cucumber, and paprika. We estimated leaf area, leaf area index (LAI), and plant height using coordinates of polygon vertices from plant and canopy surface models constructed using a three-dimensional (3D) scanner. A significant correlation was observed between the measured and estimated leaf area, LAI, and plant height (R2 > 0.8, except for tomato LAI). The canopy structure of each fruit vegetable was predicted by integrating the estimated leaf area at each height of the canopy surface models. A linear relationship was observed between the measured total leaf area and the total dry weight of each fruit vegetable; thus, the dry weight of the plant can be predicted using the estimated leaf area. The fruit weights of tomato and paprika were estimated using the fruit solid model constructed by the fruit point cloud data extracted using the RGB value. A significant correlation was observed between the measured and estimated fruit weights (tomato: R2 = 0.739, paprika: R2 = 0.888). Therefore, it was possible to estimate the growth parameters (leaf area, plant height, canopy structure, and yield) of different fruit vegetables non-destructively using a 3D scanner.

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Section: Discussionmentioning

confidence: 99%

Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner

Ohashi

Ishigami

Gotō

2020

Sensors

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“…The entity of radiometric information refers to attribute properties of each point within a dataset. Richter [32] notes six values of attribute information that can be further investigated to provide an accurate representation of a point cloud: colour [33], object class information consisting of vegetation [34] and terrain [35], surface normal [36], horizontality [37], and global and local height [38]. However, only a few attributes are practically relevant during architectural design practice.…”

Section: Radiometric Informationmentioning

confidence: 99%

A computational workflow to analyse material properties and solar radiation of existing contexts from attribute information of point cloud data

Alkadri

Turrin

Sarıyıldız

2019

Building and Environment

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This paper investigates a prospective application of point cloud data in supporting the contextual analysis of the built environment during the conceptual design process. Often, the complexity of site information causes architects to neglect several relevant properties that may affect environmental performance analysis, especially when dealing with a complex design case. For example, the current approaches of 3D site modelling lack an understanding of the site characteristics of existing environments with respect to either geometrical or material properties. With the advancement of 3D laser scanning technologies, capturing complex information from real contexts offers great possibilities for architects. From geometric and radiometric information stored within point cloud data, this study specifically proposes a novel approach to contextual analysis that considers material aspects and simulates solar radiation in the real environment. In doing so, three computational stages are developed. First, the correction of a raw dataset is designed to not only minimize errors during the scanning process but to also clean the selected dataset. Second, material exploration and the simulation of solar radiation are respectively used to calculate material properties and solar energy in the existing built environment. Third, an integrated environmental simulation aims at identifying materials found in existing areas within a certain level of insolation. As a form of design decision-making support, the present study ultimately generates a computational workflow for analysing the built environment from which architects may conduct a comprehensive analysis of an existing context before initiating design exploration.

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“…It is characterized by spatial XYZ coordinates and is optionally be assigned by typical attributes such as intensity information, color information, and any additional abstract information. Richter et.al [17] illustrates further investigation of attribute information of point cloud data that contains six values: colour [11], object class information such as vegetation [8] and terrain [3], surface normal [14], horizontality [19], global height and local height [2]. These values can be employed to construct an accurate representation of point data such as size, orientation, and shape.…”

Section: Introductionmentioning

confidence: 99%

“…The attribute of intensity potentially caters various practical tasks [10] such as detection of damaged concrete in the tunnel, identification of pavement lines and stripping, mapping seafloor, reflective detection signs, mapping different types of geologic layers in a cliff and identification of certain damages caused by natural disasters. In addition, the color and position information constructs a significant role in determining the artifact and structure of the image by converting RGB color to HSV values [11]. The potential application of these attributes then offers a great possibility to reveal the invisible information in the existing site.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Surface Materials of the Existing Environment Through Point Cloud Data Attributes

Alkadri¹,

Turrin²,

Sariyildiz³

2018

Proceedings of the 2018 Symposium on Simulation for Architecture and Urban Design (SimAUD 2018)

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Attribute Analysis of Point Cloud Data With Color Information

Cited by 7 publications

References 0 publications

Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner

Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner

A computational workflow to analyse material properties and solar radiation of existing contexts from attribute information of point cloud data

Identifying the Surface Materials of the Existing Environment Through Point Cloud Data Attributes

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