A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice

Plant Methods

Volumn 7, Issue 1, 2011, Pages

  Duan, Lingfeng ^a,b   Yang, Wanneng ^a,b   Huang, Chenglong ^a,b   Liu, Qian ^a,b  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Fast trait evaluation; Machine vision; Plant phenotyping; Rice; Yield related traits

Indexed keywords

EID: 83155191053     PISSN: None     EISSN: 17464811     Source Type: Journal    
DOI: 10.1186/1746-4811-7-44     Document Type: Article

References (25)

1. Zhang Q. Strategies for developing green super rice. PNAS 2007, 104(42):16402-16409. 10.1073/pnas.0708013104, 2034246, 17923667.
2. Wang E, Wang J, Zhu X, Hao W, Wang L, Li Q, Zhang L, He W, Lu B, Lin H, Ma H, Zhang G, He Z. Control of rice grain-filling and yield by a gene with a potential signature of domestication. Nature Genetics 2008, 40(11):1370-1374. 10.1038/ng.220, 18820698.
3. Xing Y, Zhang Q. Genetic and molecular bases of rice yield. Annual Review of Plant Biology 2010, 61:11.1-11.22.
4. Prasertsak A, Fukai S. Nitrogen availability and water stress interaction on rice growth and yield. Field Crops Research 1997, 52:249-260.
5. Xiao J, Li J, Grandillo S, Ahn SN, Yuan L, Tanksley SD, McCouch SR. Identification of trait-improving quantitative trait loci alleles from a wild rice relative, Oryza rufipogon. Genetics 1998, 150:899-909. 1460369, 9755218.
6. Thomson MJ, Tai TH, McClung AM, Lai XH, Hinga ME, Lobos KB, Xu Y, Martinez CP, McCouch SR. Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson. Theoretical and Applied Genetics 2003, 107:479-493. 10.1007/s00122-003-1270-8, 12736777.
7. Bagge M, Lübberstedt T. Functional markers in wheat: technical and economic aspects. Molocular Breeding 2008, 22:319-328.
8. Kolukisaoglu T, Thurow K. Future and frontiers of automated screening in plant sciences. Plant Science 2010, 178:476-484.
9. Bylesjö M, Segura V, Soolanayakanahally RY, Rae AM, Trygg J, Gustafsson P, Jansson S, Street NR. LAMINA: a tool for rapid quantification of leaf size and shape parameters. BMC Plant Biology 2008, 8:82-90. 10.1186/1471-2229-8-82, 2500018, 18647399.
10. French A, Ubeda-Tomás S, Holman TJ, Bennett MJ, Pridmore T. High-throughput quantification of root growth using a novel image-analysis tool. Plant physiology 2009, 150:1784-1795. 10.1104/pp.109.140558, 2719150, 19515787.
11. Yazdanbakhsh N, Fisahn J. High throughput phenotyping of root growth dynamics, lateral root formation, root architecture and root hair development enabled by PlaRoM. Functional Plant Biology 2009, 36:938-946.
12. Wang L, Uilecan IV, Assadi AH, Kozmik CA, Spalding EP. HYPOTrace: image analysis software for measuring hypocotyl growth and shape demonstrated on Arabidopsis seedlings undergoing photomorphogenesis. Plant physiology 2009, 149:1632-1637. 10.1104/pp.108.134072, 2663732, 19211697.
13. Yang W, Xu X, Duan L, Luo Q, Chen S, Zeng S, Liu Q. High-throughput measurement of rice tillers using a conveyor equipped with X-ray computed tomography. Review of Scientific Instruments 2011, 82(2):025102-025109. 10.1063/1.3531980, 21361628.
14. Golzarian MR, Frick RA, Rajendran K, Berger B, Roy S, Tester M, Lun DS. Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 2011, 7:2. 10.1186/1746-4811-7-2, 3042986, 21284859.
15. Granier C, Aguirrezabal L, Chenu K, Cookson SJ, Dauzat M, Hamard P, Thioux JJ, Rolland G, Bouchier-Combaud S, Lebaudy A, Muller B, Simonneau T, Tardieu F. PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytologist 2006, 169:623-635. 10.1111/j.1469-8137.2005.01609.x, 16411964.
16. Reuzeau C, Pen J, Frankard V, Wolf J, Peerbolte R, Broekaert W, Camp W. TraitMill: a discovery engine for identifying yield-enhancement genes in Cereals. Molecular Plant Breeding 2005, 3(5):753-759.
17. The Plant Accelerator. , http://www.plantaccelerator.org.au/
18. The High Resolution Plan Phenotyping Centre. , http://www.plantphenomics.org.au/HRPPC
19. The Leibniz Institute of Plant Genetics and Crop Plant Research (IPK). , http://www.ipk-gatersleben.de
20. Institute of Biological, Environmental and Rural Sciences (IBERS). , http://www.aber.ac.uk/en/ibers/
21. French National Institute for Agricultural Research (INRA). , http://www.international.inra.fr/
22. Duan L, Yang W, Bi K, Chen S, Luo Q, Liu Q. Fast discrimination and counting of filled/unfilled rice spikelets based on bi-modal imaging. Computers and Electronics in Agriculture 2011, 75:196-203.
23. Igathinathane C, Pordesimo LO, Batchelor WD. Major orthogonal dimensions measurement of food grains by machine vision using ImageJ. Food Research International 2009, 42:76-84.
24. Igathinathane C, Pordesimo LO, Columbus EP, Batchelor WD, Methuku SR. Shape identification and particles size distribution from basic shape parameters using ImageJ. Computers and Electronics In Agriculture 2008, 63:168-182.
25. Finkel E. With 'Phenomics,' plant scientists hope to shift breeding into overdrive. Science 2009, 325:380-381. 10.1126/science.325_380, 19628831.