Leaf Area Measurement Using Stereo Vision

Kanuma, Takahiro; Ganno, Katsunobu; Hayashi, Satoshi; Sakaue, O.

doi:10.1016/s1474-6670(17)42115-x

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…The MAPE of the leaf area estimation with the mesh-based methods ranged from 2.41% to 10.3% [ 16 , 35 , 47 , 48 ] in previous studies, whereas MAPE in the present method was 4.14%, meaning the leaf area estimation accuracy in this method was high enough. In the mesh-based 3D model, a mesh is composed of a target point and the adjacent two points.…”

Section: Discussionmentioning

confidence: 56%

Automatic Leaf Segmentation for Estimating Leaf Area and Leaf Inclination Angle in 3D Plant Images

Itakura

Hosoi

2018

Sensors

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Automatic and efficient plant monitoring offers accurate plant management. Construction of three-dimensional (3D) models of plants and acquisition of their spatial information is an effective method for obtaining plant structural parameters. Here, 3D images of leaves constructed with multiple scenes taken from different positions were segmented automatically for the automatic retrieval of leaf areas and inclination angles. First, for the initial segmentation, leave images were viewed from the top, then leaves in the top-view images were segmented using distance transform and the watershed algorithm. Next, the images of leaves after the initial segmentation were reduced by 90%, and the seed regions for each leaf were produced. The seed region was re-projected onto the 3D images, and each leaf was segmented by expanding the seed region with the 3D information. After leaf segmentation, the leaf area of each leaf and its inclination angle were estimated accurately via a voxel-based calculation. As a result, leaf area and leaf inclination angle were estimated accurately after automatic leaf segmentation. This method for automatic plant structure analysis allows accurate and efficient plant breeding and growth management.

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Section: Discussionmentioning

confidence: 56%

Automatic Leaf Segmentation for Estimating Leaf Area and Leaf Inclination Angle in 3D Plant Images

Itakura

Hosoi

2018

Sensors

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“…Apart from studies of mutagenesis, some plant scientists of agriculture and developmental biology have turned their attention to the 3D quantification of plant morphological parameters. 7,9,10,12 However, as these methods were not intended for large-scale use, these studies did not consider systematic data processing capable of uniformly measuring a large number of individuals. Moreover, these methods were not designed for comprehensive analysis of multiple traits.…”

Section: Introductionmentioning

confidence: 99%

THREE-DIMENSIONAL DEFINITION OF LEAF MORPHOLOGICAL TRAITS OF ARABIDOPSIS IN SILICO PHENOTYPIC ANALYSIS

Kaminuma

Heida

Tsumoto³

et al. 2005

J. Bioinform. Comput. Biol.

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The detection of phenotypic alterations of mutants and variants is one of the bottlenecks that hinder systematic gene functional studies of the model plant Arabidopsis. In an earlier study, we have addressed this problem by proposing a novel methodology for phenome analysis based on in silico analysis of polygon models that are acquired by 3-dimensional (3D) measurement and which precisely reconstruct the actual plant shape. However, 3D quantitative descriptions of morphological traits are rare, whereas conventional 2D descriptions have already been studied but may lack the necessary precision. In this report, we focus on six major leaf morphological traits, which are commonly used in the current manual mutant screens, and propose new 3D quantitative definitions that describe these traits. In experiments to extract the traits, we found significant differences between two variants of Arabidopsis with respect to blade roundness and blade epinasty. Remarkably, the detected difference between variants in the blade roundness trait was undetectable when using conventional 2D descriptions. Thus, the result of the experiment indicates that the proposed definitions with 3D description may lead to new discoveries of phenotypic alteration in gene functional studies that would not be possible using conventional 2D descriptions.

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“…Spatial distribution of roots as well as root length has been quantified using 3D CT data from chestnut trees and maples (Pierret et al ., 1999, 2000). The leaf area of cabbage has been calculated from 3D data obtained by stereo‐photogrammetry, which estimates a 3D model from stereo images (Kanuma et al ., 1998). Developmental parameters of cotton and bean have been extracted from 3D data from a sonic digitizer to visualize developmental processes by animation (Room et al ., 1996).…”

Section: Introductionmentioning

confidence: 99%

Automatic quantification of morphological traits via three‐dimensional measurement of Arabidopsis

et al. 2004

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SummaryMany mutants have been isolated from the model plant Arabidopsis thaliana, and recent important genetic resources, such as T-DNA knockout lines, facilitate the speed of identifying new mutants. However, present phenotypic analysis of mutant screens depends mainly on qualitative descriptions after visual observation of morphological traits. We propose a novel method of phenotypic analysis based on precise three-dimensional (3D) measurement by a laser range ®nder (LRF) and automatic data processing. We measured the 3D surfaces of young plants of two Arabidopsis ecotypes and successfully de®ned two new traits, the direction of the blade surface and epinasty of the blade, quantitatively. The proposed method enables us to obtain quantitative and precise descriptions of plant morphologies compared to conventional 2D measurement.The method will open a way to ®nd new traits from mutant pools or natural ecotypes based on 3D data.

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Leaf Area Measurement Using Stereo Vision

Cited by 7 publications

References 3 publications

Automatic Leaf Segmentation for Estimating Leaf Area and Leaf Inclination Angle in 3D Plant Images

Automatic Leaf Segmentation for Estimating Leaf Area and Leaf Inclination Angle in 3D Plant Images

THREE-DIMENSIONAL DEFINITION OF LEAF MORPHOLOGICAL TRAITS OF ARABIDOPSIS IN SILICO PHENOTYPIC ANALYSIS

Automatic quantification of morphological traits via three‐dimensional measurement of Arabidopsis

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