V. Oury, F. Tardieu, and O. Turc, Ovary apical abortion under water deficit is caused by changes in sequential development of ovaries and in silk growth rate in maize, Plant Physiol, vol.171, pp.986-96, 2016.

G. Edmeades, J. Bolaños, M. Hernandez, and S. Bello, Causes for Silk Delay in a Lowland Tropical Maize Population, Crop Science, vol.33, issue.5, pp.1029-1064, 1993.
DOI : 10.2135/cropsci1993.0011183X003300050031x

G. Edmeades, J. Bolanos, A. Elings, J. Ribaut, M. Bänziger et al., The role and regulation of the anthesis?silking interval in maize Physiology and modeling Kernel set in maize. CSSA Spec

A. Fuad?hassan, F. Tardieu, and O. Turc, Drought-induced changes in anthesis-silking interval are related to silk expansion: a spatio-temporal growth analysis in maize plants subjected to soil water deficit, Plant, Cell & Environment, vol.91, issue.9, pp.1349-60, 2008.
DOI : 10.2135/cropsci1986.0011183X002600050022x

J. Bolanos, G. Edmeades, and L. Martinez, Eight cycles of selection for drought tolerance in lowland tropical maize. III. Responses in drought-adaptive physiological and morphological traits, Field Crops Research, vol.31, issue.3-4, pp.269-86, 1993.
DOI : 10.1016/0378-4290(93)90066-V

S. Anderson, R. Farrington, D. Goldman, T. Hanselman, N. Hausmann et al., Methods for counting corn silks or other plural elongated strands and use of the count for characterizing the strands or their origin, U.S. Patent Application, vol.545266, issue.12, 2009.

P. Bassetti and M. Wesgate, Floral Asynchrony and Kernel Set in Maize Quantified by Image Analysis, Agronomy Journal, vol.86, issue.4, pp.699-703, 1994.
DOI : 10.2134/agronj1994.00021962008600040021x

J. Carcova, M. Uribelarrea, L. Borrás, M. Otegui, and M. Westgate, Synchronous Pollination within and between Ears Improves Kernel Set in Maize, Crop Science, vol.40, issue.4, pp.1056-61, 2000.
DOI : 10.2135/cropsci2000.4041056x

P. Monneveux, J. Ribaut, and A. Okono, Drought phenotyping in crops: from theory to practice. Frontiers E?books, 2014.

O. Turc, M. Bouteillé, A. Fuad?hassan, C. Welcker, and F. Tardieu, The growth of vegetative and reproductive structures (leaves and silks) respond similarly to hydraulic cues in maize, New Phytologist, vol.289, issue.2, pp.377-88, 2016.
DOI : 10.1007/s00438-014-0867-8

F. Tardieu, L. Cabrera?bosquet, T. Pridmore, and M. Bennett, Plant Phenomics, From Sensors to Knowledge, Current Biology, vol.27, issue.15, pp.770-83, 2017.
DOI : 10.1016/j.cub.2017.05.055
URL : https://hal.archives-ouvertes.fr/hal-01608414

A. Bucksch, A. Atta?boateng, A. Azihou, D. Battogtokh, A. Baumgartner et al., Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences, Frontiers in Plant Science, vol.167, p.900, 2017.
DOI : 10.1104/pp.114.251751
URL : https://hal.archives-ouvertes.fr/hal-01537909

L. Cabrera?bosquet, J. Crossa, J. Von-zitzewitz, M. Serret, and J. Araus, High-throughput Phenotyping and Genomic Selection: The Frontiers of Crop Breeding ConvergeF, Journal of Integrative Plant Biology, vol.9, issue.4, pp.312-332, 2012.
DOI : 10.1016/j.fcr.2011.12.016

R. Boyle, F. Corke, and C. Howarth, Image-based estimation of oat panicle development using local texture patterns, Functional Plant Biology, vol.42, issue.5, pp.433-476, 2015.
DOI : 10.1071/FP14056

W. Tang, Y. Zhang, D. Zhang, Y. W. Li, and M. , Corn tassel detection based on image processing, 2012 International Workshop on Image Processing and Optical Engineering, p.83350, 2011.
DOI : 10.1117/12.917672

J. Gage, N. Miller, E. Spalding, S. Kaeppler, and N. De-leon, TIPS: a system for automated image-based phenotyping of maize tassels, Plant Methods, vol.89, issue.175, p.21, 2017.
DOI : 10.1111/tpj.13320

L. Duan, C. Huang, G. Chen, L. Xiong, Q. Liu et al., Determination of rice panicle numbers during heading by multi-angle imaging, The Crop Journal, vol.3, issue.3, pp.211-220, 2015.
DOI : 10.1016/j.cj.2015.03.002

J. Gibbs, M. Pound, A. French, D. Wells, E. Murchie et al., Approaches to three-dimensional reconstruction of plant shoot topology and geometry, Functional Plant Biology, vol.44, issue.1, p.62, 2017.
DOI : 10.1071/FP16167

T. Duan, S. Chapman, E. Holland, G. Rebetzke, Y. Guo et al., Dynamic quantification of canopy structure to characterize early plant vigour in wheat genotypes, Journal of Experimental Botany, vol.67, issue.15, pp.4523-4557, 2016.
DOI : 10.1093/jxb/erw227

S. Paulus, H. Schumann, H. Kuhlmann, and J. Léon, High-precision laser scanning system for capturing 3D plant architecture and analysing growth of??cereal plants, Biosystems Engineering, vol.121, pp.1-11, 2014.
DOI : 10.1016/j.biosystemseng.2014.01.010

S. Jahnke, J. Roussel, T. Hombach, J. Kochs, A. Fischbach et al., Seeder - A Robot System for Automated Handling and Phenotyping of Individual Seeds, Plant Physiology, vol.172, issue.3, pp.1358-70, 2016.
DOI : 10.1104/pp.16.01122

V. Vadez, J. Kholova, G. Hummel, U. Zhokhavets, S. Gupta et al., LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget, Journal of Experimental Botany, vol.66, issue.18, pp.5581-93, 2015.
DOI : 10.1093/jxb/erv251

L. Cabrera?bosquet, C. Fournier, N. Brichet, C. Welcker, B. Suard et al., High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform, New Phytologist, vol.61, issue.1, pp.269-81, 2016.
DOI : 10.1146/annurev-arplant-042809-112206
URL : https://hal.archives-ouvertes.fr/hal-01576907

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-622, 2002.
DOI : 10.1109/34.1000236
URL : http://nichol.as/papers/Comaniciu/Mean%20Shift:%20A%20Robust%20Approach%20Toward.pdf

C. Fournier, S. Artzet, J. Chopard, M. Mielewczik, N. Brichet et al., Phenomenal: a software framework for model?assisted analysis of high throughput plant phenotyping data. In: IAMPS 2015 (international workshop on image analysis methods for the plant sciences). Louvain?la?Neuve, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01253627

G. Van-rossum and F. Drake, Python language reference manual. Network Theory, p.144, 2003.

S. Sural, G. Qian, and S. Pramanik, Segmentation and histogram generation using the HSV color space for image retrieval, Proceedings. International Conference on Image Processing, p.II?II, 2002.
DOI : 10.1109/ICIP.2002.1040019
URL : http://www.facweb.iitkgp.ernet.in/~shamik/papers/2002_ICIP_1.pdf

L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and regres? sion trees, 1984.

R. Team, R: a language and environment for statistical computing. R 3.0.0 edition. Vienna: R Foundation for Statistical Computing, 2015.

M. Hinich and P. Talwar, A Simple Method for Robust Regression, Journal of the American Statistical Association, vol.39, issue.349, pp.113-122, 1975.
DOI : 10.1086/294632

P. Lejeune and G. Bernier, Effect of environment on the early steps of ear initiation in maize (Zea mays L.), Plant, Cell and Environment, vol.85, issue.2, pp.217-241, 1996.
DOI : 10.1007/BF01403879

S. Van-der-walt, J. Schönberger, J. Nunez?iglesias, F. Boulogne, J. Warner et al., scikit-image: image processing in Python, PeerJ, vol.13, issue.2, p.453, 2014.
DOI : 10.7717/peerj.453/fig-5
URL : https://hal.archives-ouvertes.fr/hal-01071542

E. Dijkstra, Anote on two problems in connection with graphs, Numer Math, vol.1, pp.101-119, 1959.

C. Maurer, R. Qi, and V. Raghavan, A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.2, pp.265-70, 2003.
DOI : 10.1109/TPAMI.2003.1177156

J. Salvi, X. Armangué, and J. Batlle, A comparative review of camera calibrating methods with accuracy evaluation, Pattern Recognition, vol.35, issue.7, pp.1617-1652, 2002.
DOI : 10.1016/S0031-3203(01)00126-1

C. Sommer, C. Straehle, U. Kothe, and F. Hamprecht, Ilastik: interactive learn? ing and segmentation toolkit, 8th IEEE international symposium on biomedical imaging, pp.230-233, 2011.
DOI : 10.1109/isbi.2011.5872394

V. Muggeo and M. Muggeo, Package 'segmented', Biometrika, vol.58, pp.525-559, 2017.

A. Hartmann, T. Czauderna, R. Hoffmann, N. Stein, and F. Schreiber, HTPheno: An image analysis pipeline for high-throughput plant phenotyping, BMC Bioinformatics, vol.12, issue.1, p.148, 2011.
DOI : 10.1016/S0031-3203(00)00149-7
URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-12-148?site=bmcbioinformatics.biomedcentral.com

M. Golzarian, R. Frick, K. Rajendran, B. Berger, S. Roy et al., Accurate inference of shoot biomass from high-throughput images of cereal plants, Plant Methods, vol.7, issue.1, 2011.
DOI : 10.1186/1746-4811-7-2

A. Knecht, M. Campbell, A. Caprez, D. Swanson, and H. Walia, Image Harvest: an open-source platform for high-throughput plant image processing and analysis, Journal of Experimental Botany, vol.67, issue.11, pp.3587-99, 2016.
DOI : 10.1093/jxb/erw176
URL : https://academic.oup.com/jxb/article-pdf/67/11/3587/18073688/erw176.pdf

C. Klukas, D. Chen, and J. Pape, Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping, PLANT PHYSIOLOGY, vol.165, issue.2, pp.506-524, 2014.
DOI : 10.1104/pp.113.233932
URL : http://www.plantphysiol.org/content/plantphysiol/165/2/506.full.pdf

A. Coupel?ledru, E. Lebon, C. A. Gallo, A. Gago, P. Pantin et al., Reduced nighttime transpiration is a relevant breeding target for high water-use efficiency in grapevine, Proceedings of the National Academy of Sciences, vol.114, issue.1, pp.8963-8971, 2016.
DOI : 10.1111/nph.14027

A. Coupel?ledru, É. Lebon, C. A. Doligez, A. Cabrera?bosquet, L. Péchier et al., Genetic variation in a grapevine progeny (Vitis vinifera L. cvs Grenache??Syrah) reveals inconsistencies between maintenance of daytime leaf water potential and response of transpiration rate under drought, Journal of Experimental Botany, vol.65, issue.21, pp.6205-6223, 2014.
DOI : 10.1093/jxb/eru228
URL : https://hal.archives-ouvertes.fr/hal-01054469

A. Coupel?ledru, S. Tyerman, D. Masclef, E. Lebon, C. A. Edwards et al., Abscisic Acid Down-Regulates Hydraulic Conductance of Grapevine Leaves in Isohydric Genotypes Only, Plant Physiology, vol.175, issue.3
DOI : 10.1104/pp.17.00698
URL : https://hal.archives-ouvertes.fr/hal-01604427

G. Lopez, B. Pallas, S. Martinez, P. Lauri, J. Regnard et al., Genetic Variation of Morphological Traits and Transpiration in an Apple Core Collection under Well-Watered Conditions: Towards the Identification of Morphotypes with High Water Use Efficiency, PLOS ONE, vol.10, issue.12, p.145540, 2015.
DOI : 10.1371/journal.pone.0145540.s007
URL : https://hal.archives-ouvertes.fr/hal-01382125

G. Maddonni, M. Otegui, B. Andrieu, M. Chelle, and J. Casal, Maize Leaves Turn Away from Neighbors, PLANT PHYSIOLOGY, vol.130, issue.3, pp.1181-1190, 2002.
DOI : 10.1104/pp.009738
URL : http://www.plantphysiol.org/content/plantphysiol/130/3/1181.full.pdf

P. Girardin and M. Tollenaar, Effects of Intraspecific Interference on Maize Leaf Azimuth, Crop Science, vol.34, issue.1, pp.151-156, 1994.
DOI : 10.2135/cropsci1994.0011183X003400010027x

R. Mccormick, S. Truong, and J. Mullet, 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture, Plant Physiology, vol.172, pp.823-857, 2016.
DOI : 10.1104/pp.16.00948
URL : http://www.plantphysiol.org/content/plantphysiol/172/2/823.full.pdf

S. Paulus, J. Dupuis, A. Mahlein, and H. Kuhlmann, Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping, BMC Bioinformatics, vol.14, issue.1, p.238, 2013.
DOI : 10.1016/j.compag.2010.06.009

A. Burgess, R. Retkute, M. Pound, S. Mayes, and E. Murchie, Image-based 3D canopy reconstruction to determine potential productivity in complex multi-species crop systems, Annals of Botany, vol.119, pp.517-549, 2017.
DOI : 10.1093/aob/mcw242
URL : https://academic.oup.com/aob/article-pdf/119/4/517/18764452/mcw242.pdf

P. Bassetti and M. Westgate, Emergence, Elongation, and Senescence of Maize Silks, Crop Science, vol.33, issue.2, pp.271-276, 1993.
DOI : 10.2135/cropsci1993.0011183X003300020011x

J. Cárcova, B. Andrieu, and M. Otegui, Silk Elongation in Maize, Crop Science, vol.43, issue.3, pp.914-934, 2003.
DOI : 10.2135/cropsci2003.9140

C. Pradal, S. Dufour?kowalski, F. Boudon, C. Fournier, and C. Godin, OpenAlea: a visual programming and component-based software platform for plant modelling, Functional Plant Biology, vol.35, issue.10, pp.751-60, 2008.
DOI : 10.1071/FP08084
URL : http://www.publish.csiro.au/fp/pdf/FP08084

C. Pradal, C. Fournier, P. Valduriez, and S. Cohen?boulakia, OpenAlea, Proceedings of the 27th International Conference on Scientific and Statistical Database Management, SSDBM '15, 2015.
DOI : 10.1007/978-3-642-13818-8_33
URL : https://hal.archives-ouvertes.fr/hal-00831801