A. Bucksch, J. Burridge, L. M. York, A. Das, E. Nord et al., Image-based high-throughput field phenotyping of crop roots, Plant Physiol, vol.166, pp.470-486, 2014.

S. Liu, L. M. Acosta-gamboa, X. Huang, and A. Lorence, Novel Low Cost 3D Surface Model Reconstruction System for Plant Phenotyping, J. Imaging, vol.3, p.39, 2017.

A. M. Mutka and R. S. Bart, Image-based phenotyping of plant disease symptoms, Front. Plant Sci, vol.5, 2015.

R. Subramanian, E. P. Spalding, and N. J. Ferrier, A high throughput robot system for machine vision based plant phenotype studies, Mach. Vis. Appl, vol.11, pp.619-636, 2013.

J. F. Humplík, D. Lazár, A. Husi?ková, and L. Spíchal, Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses-A review, Plant Methods, vol.11, 2015.

S. Dhondt, N. Wuyts, and D. Inzé, Cell to whole-plant phenotyping: The best is yet to come, Trends Plant Sci, vol.18, pp.428-439, 2013.

S. Paulus, J. Behmann, A. K. Mahlein, L. Plümer, and H. Kuhlmann, Low-cost 3D systems: Suitable tools for plant phenotyping, Sensors, vol.14, pp.3001-3018, 2014.

M. Minervini, H. Scharr, and S. A. Tsaftaris, Image analysis: The new bottleneck in plant phenotyping, IEEE Signal Process. Mag, vol.32, pp.126-131, 2015.

S. A. Tsaftaris, M. Minervini, and H. Scharr, Machine learning for plant phenotyping needs image processing, Trends Plant Sci, vol.21, pp.989-991, 2016.

A. Dell'aquila, Towards new computer imaging techniques applied to seed quality testing and sorting, Seed Sci. Technol, vol.35, pp.519-538, 2007.

A. Dell'aquila, Development of novel techniques in conditioning, testing and sorting seed physiological quality, Seed Sci. Technol, vol.37, pp.608-624, 2009.

J. D. Bewley and M. Black, Seeds: Physiology of Development and Germination, 1994.

L. W. Woodstock, Seed imbibition: A critical perido for successful germination, 14. Bewley, J.D. Seed germination and dormancy. Plant Cell, vol.12, pp.1-15, 1055.

L. N. Pietrzak, J. Fregeau-reid, B. Chatson, and B. Blackwell, Observations on water distribution in soybean seed during hydration processes using nuclear magnetic resonance imaging, Can. J. Plant Sci, vol.82, pp.513-519, 2002.

B. Manz, K. Müller, B. Kucera, F. Volke, and G. Leubner-metzger, Water uptake and distribution in germinating tobacco seeds investigated in vivo by nuclear magnetic resonance imaging, Plant Physiol, vol.138, pp.1538-1551, 2005.

A. Dell'aquila, Pepper seed germination assessed by combined X-radiography and computer-aided imaging analysis, Biol. Plant, vol.51, pp.777-781, 2007.

L. E. Romans, Computed Tomography for Technologists: A Comprehensive Text

W. Williams and &. Wilkins, , 2010.

L. Foucat, A. Chavagnat, and J. P. Renou, Nuclear magnetic resonance micro-imaging and X-radiography as possible techniques to study seed germination, Sci. Hortic, vol.55, pp.323-331, 1993.

N. Otsu, A threshold selection method from gray-level histograms, Automatica, vol.11, pp.23-27, 1975.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2008.

D. Rousseau, T. Widiez, S. Tommaso, H. Rositi, J. Adrien et al., Fast virtual histology using X-ray in-line phase tomography: Application to the 3D anatomy of maize developing seeds, Plant Methods, vol.11, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01538205

Y. M. Staedler, D. Masson, and J. Schönenberger, Plant tissues in 3D via X-ray tomography: Simple contrasting methods allow high resolution imaging, PLoS ONE, vol.8, 2013.

L. Borisjuk, H. Rolletschek, and T. Neuberger, Surveying the plant's world by magnetic resonance imaging, Plant J, vol.70, pp.129-146, 2012.

H. Nonogaki, G. W. Bassel, and J. D. Bewley, Germination-Still a mystery, Plant Sci, vol.179, pp.574-581, 2010.

L. Chaerle, I. Leinonen, H. G. Jones, and D. V. Der-straeten, Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging, J. Exp. Bot, vol.58, pp.773-784, 2007.

E. Belin, D. Rousseau, T. Boureau, and V. Caffier, Thermography versus chlorophyll fluorescence imaging for detection and quantification of apple scab, Comput. Electron. Agric, vol.90, pp.159-163, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01209914

Y. Chéné, E. Belin, F. Chapeau-blondeau, T. Boureau, V. Caffier et al., Anatomo-functional bimodality imaging for plant phenotyping: An insight through depth imaging coupled to thermal imaging, Plant Image Analysis: Fundamentals and Applications

D. Gupta, S. Ibaraki, and Y. , , 2015.

X. Z. Wang, W. P. Yang, A. Wheaton, N. Cooley, and B. Moran, Automated canopy temperature estimation via infrared thermography: A first step towards automated plant water stress monitoring, Comput. Electron. Agric, vol.73, pp.74-83, 2010.

P. Baranowski, W. Mazurek, and R. T. Walczak, The use of thermography for pre-sowing evaluation of seed germination capacity, Proceedings of the International Conference on Quality Chains-An Integrated View on Fruit and Vegetable, vol.1, 2003.

I. Kranner, G. Kastberger, M. Hartbauer, and H. W. Pritchard, Non-invasive diagnosis of seed viability using infrared thermography, Proc. Natl. Acad. Sci, vol.107, pp.3912-3917, 2010.

E. Belin, D. Rousseau, J. Rojas-varela, D. Demilly, M. H. Wagner et al., Thermography as a non-invasive functional imaging for monitoring seedling growth, Comput. Electron. Agric, vol.79, pp.236-240, 2011.

E. Belin, D. Rousseau, L. Benoit, D. Demilly, S. Ducournau et al., Thermal imaging for evaluation of seedling growth, Plant Image Analysis: Fundamentals and Applications
URL : https://hal.archives-ouvertes.fr/hal-02536764

D. Gupta, S. Ibaraki, and Y. , , 2015.

S. Budzan and R. Wy?golik, Noise reduction in thermal images, International Conference on Computer Vision and Graphics, pp.116-123, 2014.

A. Rogalski, Infrared Detectors, 2011.

J. Fehrenbach, P. Weiss, and C. Lorenzo, Variational algorithms to remove stationary noise: Applications to microscopy imaging, IEEE Trans. Image Process, vol.21, pp.4420-4430, 2012.

P. M. Pieczywek, A. Kurenda, A. Zdunek, and A. Adamiak, The biospeckle method for the investigation of agricultural crops: A review, Opt. Lasers Eng, vol.52, pp.156-158, 2014.

R. A. Braga-júnior, When noise became information: State-of-the-art in biospeckle laser, vol.41, pp.359-366, 2017.

E. E. Ramirez-miquet, J. G. Darias, I. Otero, D. Rodriguez, S. Murialdo et al., Biospeckle technique for monitoring bacterial colony growth with minimal photo-exposure time associated, Proceedings of the VI Latin American Congress on Biomedical Engineering CLAIB 2014, pp.313-316, 2014.

E. E. Ramirez-miquet, H. Cabrera, H. C. Grassi, E. D. Andrades, I. Otero et al., Digital imaging information technology for biospeckle activity assessment relative to bacteria and parasites, Lasers Med. Sci, vol.32, pp.1375-1386, 2017.

G. F. Rabelo, A. M. Enes, R. A. Braga-júnior, and I. M. Fabbro, Frequency response of biospeckle laser images of bean seeds contaminated by fungi, Biosyst. Eng, vol.110, pp.297-301, 2011.

D. Rousseau, C. Caredda, Y. Morille, E. Belin, F. Chapeau-blondeau et al., Low-cost biospeckle imaging applied to the monitoring of seed germination, Proceedings of the 3rd International Workshop on Image Analysis Methods for the Plant Sciences (IAMPS), pp.15-16, 2014.

R. A. Braga, I. M. Fabbro, F. M. Borem, G. Rabelo, R. Arizaga et al., Assessment of seed viability by laser speckle techniques, Biosyst. Eng, vol.86, pp.287-294, 2003.

P. Platform and . Available-online, , 2018.

R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2003.

D. Rousseau, Y. Chéné, E. Belin, G. Semaan, G. Trigui et al., Multiscale imaging of plants: Current approaches and challenges, Plant Methods, vol.11, issue.6, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01392050