3-D Color Video Microscopy of Intact Plants: A New Method for Measuring Shape and Growth.

Omasa, Kenji; Kouda, Masaki

doi:10.2525/ecb1963.36.217

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…The popular passive approaches for 3D modeling are structure-from-motion (SfM), stereo vision, and shape from texture/silhouette/focus/shading [ 23 – 28 ]. Stereo vision system is the most widely used technique in the literature for obtaining 3D information.…”

Section: Introductionmentioning

confidence: 99%

Plant trait measurement in 3D for growth monitoring

2022

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Background There is a demand for non-destructive systems in plant phenotyping which could precisely measure plant traits for growth monitoring. In this study, the growth of chilli plants (Capsicum annum L.) was monitored in outdoor conditions. A non-destructive solution is proposed for growth monitoring in 3D using a single mobile phone camera based on a structure from motion algorithm. A method to measure leaf length and leaf width when the leaf is curled is also proposed. Various plant traits such as number of leaves, stem height, leaf length, and leaf width were measured from the reconstructed and segmented 3D models at different plant growth stages. Results The accuracy of the proposed system is measured by comparing the values derived from the 3D plant model with manual measurements. The results demonstrate that the proposed system has potential to non-destructively monitor plant growth in outdoor conditions with high precision, when compared to the state-of-the-art systems. Conclusions In conclusion, this study demonstrated that the methods proposed to calculate plant traits can monitor plant growth in outdoor conditions.

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Section: Introductionmentioning

confidence: 99%

Plant trait measurement in 3D for growth monitoring

2022

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“…In terms of plant structure measurement, 3D imaging technologies have more advantages than 2D imaging technologies because of the complex structure of plants [6,[10][11][12]. Among the 3D imaging technologies, the active and passive methods are the two primary imaging techniques to measure 3D plant structure parameters quantitatively [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction method and optimum range of camera-shooting angle for 3D plant modeling using a multi-camera photography system

Ōno

et al. 2020

Plant Methods

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Background: Measurement of plant structure is useful in monitoring plant conditions and understanding the responses of plants to environmental changes. 3D imaging technologies, especially the passive-SfM (Structure from Motion) algorithm combined with a multi-camera photography (MCP) system has been studied to measure plant structure due to its low-cost, close-range, and rapid image capturing ability. However, reconstruction of 3D plant models with complex structure is a time-consuming process and some systems have failed to reconstruct 3D models properly. Therefore, an MCP based SfM system was developed and an appropriate reconstruction method and optimal range of camera-shooting angles were investigated. Results: An MCP system which utilized 10 cameras and a rotary table for plant was developed. The 3D mesh model of a single leaf reconstruction using a set of images taken at each viewing zenith angle (VZA) from 12° (C2 camera) to 60° (C6 camera) by the MCP based SfM system had less undetected or unstable regions in comparison with other VZAs. The 3D mesh model of a whole plant, which merged 3D dense point cloud models built from a set of images taken at each appropriate VZA (Method 1), had high accuracy. The Method 1 error percentages for leaf area, leaf length, leaf width, stem height, and stem width are in the range of 2.6-4.4%, 0.2-2.2%, 1.0-4.9%, 1.9-2.8%, and 2.6-5.7% respectively. Also, the error of the leaf inclination angle was less than 5°. Conversely, the 3D mesh model of a whole plant built directly from a set of images taken at all appropriate VZAs (Method 2) had lower accuracy than that of Method 1. For Method 2, the error percentages of leaf area, leaf length, and leaf width are in the range of 3.1-13.3%, 0.4-3.3%, and 1.6-8.6%, respectively. It was difficult to obtain the error percentages of stem height and stem width because some information was missing in this model. In addition, the calculation time for Method 2 was 1.97 times longer computational time in comparison to Method 1. Conclusions: In this study, we determined the optimal shooting angles on the MCP based SfM system developed. We found that it is better in terms of computational time and accuracy to merge partial 3D models from images taken at each appropriate VZA, then construct complete 3D model (Method 1), rather than to construct 3D model by using images taken at all appropriate VZAs (Method 2). This is because utilization of incorporation of incomplete images to match feature points could result in reduced accuracy in 3D models and the increase in computational time for 3D model reconstruction.

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“…They may also improve the self-occlusion and correspondence problems. In the passive methods, a common approach of 3D imaging technology, such as stereovision, shape from focus/shading/texture, structure from motion etc is normally applied Omasa and Kouda, 1998;Se et al, 2012;Rose et al, 2015;Nguyen et al, 2016;Teng et al, 2016;Zhang et al, 2016 . The most widespread method of acquiring 3D data is stereovision.…”

Section: Introductionmentioning

confidence: 99%

3D monitoring for plant growth parameters in field with a single camera by multi-view approach

Zhang

Teng

Aono

et al. 2018

J. Agric. Meteorol.

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In recent decades, some photogrammetric methods for 3D monitoring of plant growth and structure parameters have been studied by the automated feature extraction and matching. In this study on growth analysis of sweet potato Ipomoea batatas L. plants at different fertilizer conditions, we proposed a convenient solution of 3D reconstruction by a single camera photography system based on Structure from Motion SfM method. Also, we handled effectively the noise problem by minimizing re-projection errors. The results of 3D models demonstrated that the average percentage error was a constant about 4.8 for plant height or decreased from 13 to 8 leaf area index, LAI = 3.5 for leaf number and from 19 to 12 LAI = 3.5 for leaf area with increasing in LAI, although each percentage error fluctuated, especially at low LAI. In contrast, the average percentage error in 2D image processing was 20 to 45 for leaf number and 60 to 90 for leaf area, and the leaf height was immeasurable. Comparing with the errors of 3D results, the errors of 2D images were much larger because 2D imaging had some problems, such as not being robust against occlusion of plant organs, and the ambiguity between object size and distance from the camera. On the other hand, we examined a method to calibrate the estimates from 3D models using the regression model between the measured value and the value estimated from 3D model. The regression models showed the linear and good estimation for leaf height R 2 = 0.97 and RMSE = 0.71 cm , leaf number R 2 = 0.99 and RMSE = 4.03 and leaf area R 2 = 0.98 and RMSE = 0.12 m 2 , in spite of use of data across a wide range in fertilizer supply and growth stage of sweet potato plants.The results demonstrated that 3D imaging technique in the study has the potential to remotely monitor plant growth status and estimate growth and structure parameters at various environmental factors outdoors.

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3-D Color Video Microscopy of Intact Plants: A New Method for Measuring Shape and Growth.

Cited by 6 publications

References 22 publications

Plant trait measurement in 3D for growth monitoring

Plant trait measurement in 3D for growth monitoring

Reconstruction method and optimum range of camera-shooting angle for 3D plant modeling using a multi-camera photography system

3D monitoring for plant growth parameters in field with a single camera by multi-view approach

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