Multiscale imaging of plants: current approaches and challenges

Rousseau, D.; Chene, Yann; Belin, Étienne; Semaan, Georges; Trigui, G.; Boudehri, Karima; Franconi, Florence; Chapeau‐Blondeau, François

doi:10.1186/s13007-015-0050-1

Cited by 40 publications

(43 citation statements)

References 68 publications

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“…Phenotyping is addressed by combining novel technologies such as non-invasive imaging, spectroscopy, image analysis, robotics and high-performance computing [1]. Plant measurements have been done on different scales, at the level of cells, organs, root systems, plants, and canopies, where different sensors are used for each scale [5]. Measurement are performed using two-dimensional (2D) images or 3D models.…”

Section: Introductionmentioning

confidence: 99%

Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping

Golbach

Kootstra

Damjanović

et al. 2015

Machine Vision and Applications

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In plant phenotyping, there is a demand for high-throughput, non-destructive systems that can accurately analyse various plant traits by measuring features such as plant volume, leaf area, and stem length. Existing vision-based systems either focus on speed using 2D imaging, which is consequently inaccurate, or on accuracy using time-consuming 3D methods. In this paper, we present a computer-vision system for seedling phenotyping that combines best of both approaches by utilizing a fast three-dimensional (3D) reconstruction method. We developed image processing methods for the identification and segmentation of plant organs (stem and leaf) from the 3D plant model. Various measurements of plant features such as plant volume, leaf area, and stem length are estimated based on these plant segments. We evaluate the accuracy of our system by comparing the measurements of our methods with ground truth measurements obtained destructively by hand. The results indicate that the proposed system is very promising.

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Section: Introductionmentioning

confidence: 99%

Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping

Golbach

Kootstra

Damjanović

et al. 2015

Machine Vision and Applications

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“…In addition to providing two different contrasts, this could also serve to provide a 2.5-dimensional image via the production of a disparity map as if these two cameras were considered as a stereovision system [45]. Another example considering 3D modalities is the association of MRI with X-ray tomography [46]. The MRI technique delivers information concerning water localization, useful to characterize the dynamics of imbibition.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of 3D/2D Imaging and Image Processing Techniques for the Monitoring of Seed Imbibition

et al. 2018

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Seed imbibition is a very important process in plant biology by which, thanks to a simple water income, a dry seed may turn into a developing organism. In natural conditions, this process occurs in the soil, e.g., with difficult access for a direct observation. Monitoring the seed imbibition with non-invasive imaging techniques is therefore an important and possibly challenging task if one tries to perform it in natural conditions. In this report, we describe a set of four different imaging techniques that enable to addressing this task either in 3D or in 2D. For each technique, the following items are proposed. A detailed experimental protocol is provided to acquire images of the imbibition process. With the illustration of real data, the significance of the physical quantities measured in terms of their relation to the income of water in the seed is presented. Complete image analysis pipelines are then proposed to extract dynamic information on the imbibition process from such monitoring experiments. A final discussion compares the advantages and current limitations of each technique in addition to elements concerning the associated throughput and cost. These are criteria especially relevant in the field of plant phenotyping where large populations of plants are imaged to produce quantitatively significative traits after image processing.

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“…In addition, hyperspectral images deliver huge amounts of data, typically some gigabytes per image. This induces challenges on the handling of such big data, especially in the perspective of continuous monitoring or imaging of populations of plants [6]. In a translational research perspective with application to agronomy or high-throughput phenotyping with multiple imaging systems, it is important to promote the use of low-cost setups [7].…”

Section: Introductionmentioning

confidence: 99%

On the value of the Kullback–Leibler divergence for cost-effective spectral imaging of plants by optimal selection of wavebands

Benoit

Belin

et al. 2015

Machine Vision and Applications

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The practical value of a criterion based on statistical information theory is demonstrated for the selection of optimal wavelength and bandwidth of low-cost lighting systems in plant imaging applications. Kullback-Leibler divergence is applied to the problem of spectral band reduction from hyperspectral imaging. The results are illustrated on various plant imaging problems and show similar results to the one obtained with state-of-the-art criteria. A specific interest of the proposed approach is to offer the possibility to integrate technological constraints in the optimization of the spectral bands selected.

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Multiscale imaging of plants: current approaches and challenges

Cited by 40 publications

References 68 publications

Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping

Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping

Evaluation of 3D/2D Imaging and Image Processing Techniques for the Monitoring of Seed Imbibition

On the value of the Kullback–Leibler divergence for cost-effective spectral imaging of plants by optimal selection of wavebands

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