Computer vision under inactinic light for hypocotyl-radicle separation with a generic gravitropism-based criterion

Computers and Electronics in Agriculture

Volumn 111, Issue , 2015, Pages 12-17

  Benoit, Landry ^a   Belin, Étienne ^a   Dürr, Carolyne ^b   Chapeau Blondeau, François ^a   Demilly, Didier ^c   Ducournau, Sylvie ^c   Rousseau, David ^d  

^a UNIVERSITÉ D'ANGERS   (France)

^b Universite d'Angers   (France)

^c GEVES   (France)

^d UNIVERSITÉ LYON 1   (France)

Author keywords

Computer vision; Seedling elongation; Thermal imaging; Visible imaging

Indexed keywords

AUTOMATION; COST REDUCTION; INFRARED IMAGING; LIGHT EMITTING DIODES; QUALITY CONTROL; SEED; THROUGHPUT;

COMPUTER IMPLEMENTATIONS; GENETIC VARIABILITY; GROWTH PROCESS; HIGH-THROUGHPUT; HIGH-THROUGHPUT PHENOTYPING; NORMAL CONDITION; NUTRIENT ABSORPTION; VISIBLE IMAGING;

COMPUTER VISION;

ABSORPTION; AUTOMATION; COMPUTER VISION; GERMINATION; GROWTH RATE; LIGHT EFFECT; NUTRIENT; PLANT; SEEDLING; SEPARATION; SOIL SURFACE;

EID: 84919884314     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2014.12.001     Document Type: Article

References (26)

1. Belin E., Rousseau D., Rojas-Varela J., Demilly D., Wagner M., Cathala M., Dürr C. Thermography as non invasive functional imaging for monitoring seedling growth. Comput. Electron. Agric. 2011, 79:236-240.
2. Belin E., Rousseau D., Benoit L., Demilly D., Ducournau S., Chapeau-Blondeau F., Dürr C. Thermal imaging for evaluation of seedling growth. Plant Image Analysis: Fundamentals and Applications 2014, CRC Press, chapter 2. S. Dutta Gupta, Y. Ibaraki (Eds.).
3. Boiffin J., Dürr C., Fleury A., Marin-Laflèche A., Maillet I. Analysis of the variability of sugar beet (beta vulgaris l.) growth during the early stages. I-influence of various conditions of crop establishment. Agronomy 1992, 12:515-525.
4. Briggs W.R., Olney M.A. Photoreceptors in plant photomorphogenesis to date. Five phytochromes, two cryptochromes, one phototropin, and one superchrome. Plant Physiol. 2001, 125:85-88.
5. Brunel-Muguet S., Aubertot J.N., Dürr C. Simulating the impact of genetic diversity of Medicago truncatula on germination and emergence using a crop emergence model for ideotype breeding. Ann. Botany 2011, 107:1367-1376.
6. Chan T.F., Vese L.A. Active contours without edges. IEEE Trans. Image Process. 2001, 10:266-277.
7. Ducournau S., Feutry A., Plainchault P., Revollon P., Vigouroux B., Wagner M. An image acquisition system for automated monitoring of the germination rate of sunflower seeds. Comput. Electron. Agric. 2004, 44:189-202.
8. Dürr C., Aubertot J.N., Richard G., Dubrulle P., Duval Y., Boiffin J. Simple: a model for simulation of plant emergence predicting the effects of soil tillage and sowing operations. J. Sci. Soil Soc. Am. 2001, 65:414-423.
9. Fayaud B., Coste F., Corre-Hellou G., Gardarin A., Dürr C. Modelling early growth under different sowing conditions: a tool to predict variations in intercrop early stages. Eur. J. Agron. 2014, 52:180-190.
10. Forcella F., Benech Arnold R.L., Sanchez R., Ghersa C.M. Modeling seedling emergence. Field Crop Res. 2000, 67(2):123-139.
11. French A., Ubeda-Tomás S., Holman T.J., Bennett M.J., Pridmore T. High-throughput quantification of root growth using a novel image-analysis tool. Plant Physiol. 2009, 150:1784-1795.
12. Goshtasby A. Image registration by local approximation methods. Image Vis. Comput. 1988, 6:255-261.
13. Ishikawa H., Evans M.L. Novel software for analysis of root gravitropism: comparative response patterns of Arabidopsis wild-type and axr1 seedlings. Plant Cell Environ. 1997, 20:919-928.
14. Jaffe M.J., Wakefield A.H., Telewski F., Gulley E., Biro R. Computer-assisted image analysis of plant growth thigmomorphogenesis, and gravitropism. Plant Physiol. 1985, 77:722-730.
15. Joosen R.V., Kodde J., Willems L.A., Ligterink W., vander Plas L.H., Hilhorst H.W. germinator: a software package for high-throughput scoring and curve fitting of arabidopsis seed germination. Plant J. 2010, 62:148-159.
16. Kimura K., Yamasaki S. Accurate root length and diameter measurement using nih image: use of pythagorean distance for diameter estimation. Plant Soil 2003, 254:305-315.
17. Kong T.Y., Rosenfeld A. Topological Algorithms For Digital Image Processing 1996, Elsevier, Amsterdam.
18. Saglio P.H., Pradet A. Soluble sugars, respiration, and energy charge during aging of excised maize root tips. Plant Physiol. 1980, 66:516-519.
19. Smith H. Phytochromes and light signal perception by plants-an emerging synthesis. Nature 2000, 407:585-591.
20. Smith H., Whitelam G. Phytochrome, a family of photoreceptors with multiple physiological roles. Plant Cell Environ. 1990, 13:695-707.
21. Smith H., Casal J., Jackson G. Reflection signals and the perception by phytochrome of the proximity of neighbouring vegetation. Plant Cell Environ. 1990, 13:73-78.
22. Subramanian R., Spalding E.P., Frerrier N.J. A high throughput robot system for machine vision base plant phenotype studies. Mach. Vis. Appl. 2013, 24:619-636.
23. Taiz L., Zeiger E. Plant Physiology 2010, Sinauer associates, Los Angeles.
24. Walter A., Spies H., Terjung S., Kusters R., Kirchgebner N., Schurr U. Spatio-temporal dynamics of expansion growth in roots: automatic quantification of diurnal course and temperature response by digital image sequence processing. J. Exp. Biol. 2002, 53:689-698.
25. Wang L., Assadi A.H., Spalding E.P. Tracing branched curvilinear structures with a novel adaptive local PCA algorithm. Plant Physiol. 2009, 53:1632-1637.
26. Wang L., Uilecan I., Assadi A., Kozmik C., Spalding E. Hypotrace image analysis software for measuring hypocotyl growth and shape demonstrated on arabidopsis seedling undergoing photomorphogenesis. Plant Physiol. 2009, 49:1632-1637.