Application of geographically weighted regression to improve grain yield prediction from unmanned aerial system imagery

Crop Science

Volumn 57, Issue 5, 2017, Pages 2478-2489

  Haghighattalab, Atena ^a   Crain, Jared ^a   Mondal, Suchismita ^b   Rutkoski, Jessica ^c   Singh, Ravi Prakash ^b   Poland, Jesse ^a  

^a KANSAS STATE UNIVERSITY   (United States)

^b INTERNATIONAL MAIZE AND WHEAT IMPROVEMENT CENTER CIMMYT   (Mexico)

^c INTERNATIONAL RICE RESEARCH INSTITUTE   (Philippines)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85029591076     PISSN: 0011183X     EISSN: 14350653     Source Type: Journal    
DOI: 10.2135/cropsci2016.12.1016     Document Type: Article

References (66)

1. Agisoft. 2016. Agisoft PhotoScan user manual: Professional edition, version 1.2. Agisoft, St. Petersburg, Russia. http://www.agisoft. com/pdf/photoscan-pro_1_2_en.pdf(accessed 14 Dec. 2016).
2. Araya, S., B. Ostendorf, G. Lyle, and M. Lewis. 2013. Crop phenology based on MODIS satellite imagery as an indicator of plant available water content. In: Proceedings of the 20th International Congress on Modelling and Simulation, Adelaide, Australia. 1–6 Dec. 2013. The Modelling and Simulation Society of Australia and New Zealand. p. 1896–1902.
3. Arnold, J.G., M.A. Weltz, E.E. Alberts, and D.C. Flanagan. 1995. Plant growth component. In: USDA Water Erosion Prediction Project: Tech. Rep. no. 10. USDA, West Lafayette, IN. p. 8.1–8.41.
4. Babar, M.A., M.P. Reynolds, M. Van Ginkel, A.R. Klatt, W.R. Raun, and M.L. Stone. 2006a. Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci. 46:578–588. doi:10.2135/cropsci2005.0059
5. Babar, M.A., M. van Ginkel, R. Klatt, B. Prasad, and M.P. Reynolds. 2006b. The potential of using spectral reflectance indices to estimate yield in wheat grown under reduced irrigation. Euphytica 150:155–172. doi:10.1007/s10681-006-9104-9
6. Bates, D., M. Maechler, B. Bolker, and S. Walker. 2014. lme4: Linear mixed-effects models using lme4. Cornell Univ. Library, Ithaca, NY.
7. Bell, M.A., E. Stations, R.A. Fischer, and W. Program. 1994. Guide to plant and crop sampling: Measurements and observations for agronomic and physiological research in small grain cereals. Wheat Spec. Rep. 32. CIMMYT, Mexico, DF.
8. Braun, H.J., S. Rajaram, and M. Ginkel. 1996. CIMMYT’s approach to breeding for wide adaptation. Euphytica 92:175– 183. doi:10.1007/BF00022843
9. Brunsdon, C., A.S. Fotheringham, and M.E. Charlton. 1996. Geographically weighted regression: A method for exploring spatial nonstationary. Geogr. Anal. 28:281–298. doi:10.1111/j.1538-4632.1996.tb00936.x
10. Cobb, J.N., G. DeClerck, A. Greenberg, R. Clark, and S. McCouch. 2013. Next-generation phenotyping: Requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement. Theor. Appl. Genet. 126:867–887. doi:10.1007/s00122-013-2066-0
11. Crain, J.L., M.P. Reynolds, and J.A. Poland. 2016. Utilizing high-throughput phenotypic data for improved phenotypic selection of stress adaptive traits in wheat. Crop Sci. 57:648–659. doi:10.2135/cropsci2016.02.0135
12. Deery, D., J. Jimenez-Berni, H. Jones, X. Sirault, and R. Furbank. 2014. Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4:349–379. doi:10.3390/agronomy4030349
13. De La Fuente, G.N., U.K. Frei, and T. Lübberstedt. 2013. Accelerating plant breeding. Trends Plant Sci. 18:667–672. doi:10.1016/j.tplants.2013.09.001
14. Elshire, R.J., J.C. Glaubitz, Q. Sun, J.A. Poland, K. Kawamoto, E.S. Buckler, and S.E. Mitchell. 2011. A robust, simple genotyping-by-sequencing (gbs) approach for high diversity species. PLoS One 6:e19379. doi:10.1371/journal.pone.0019379
15. Esfandiary, F., G. Aghaie, and A.D. Mehr. 2009. Wheat yield prediction through agro meteorological indices for ardebil district. Int. J. Biol. Biomol. Agric. Food Biotechnol. Eng. 3:18–21.
16. Falconer, D.S., and T.F. Mackay. 1996. Introduction to quantitative genetics. 4th ed. Longman, Essex, UK.
17. Feng, Q., J. Liu, and J. Gong. 2015. Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier: A case of Yuyao, China. Water 7:1437–1455. doi:10.3390/w7041437
18. Foley, J.A., N. Ramankutty, K.A. Brauman, E.S. Cassidy, J.S. Gerber, M. Johnston et al. 2011. Solutions for a cultivated planet. Nature 478:337–342. doi:10.1038/nature10452
19. Fotheringham, A.S., C. Brunsdon, and M. Charlton. 2002. Geographically weighted regression: The analysis of spatially varying relationships. Wiley, New York.
20. Gilmour, A.R., B.R. Cullis, and A.P. Verbyla. 1997. Accounting for natural and extraneous variation in the analysis of field experiments. J. Agric. Biol. Environ. Stat. 2:269–273. doi:10.2307/1400446
21. Goggin, F.L., A. Lorence, and C.N. Topp. 2015. Applying high-throughput phenotyping to plant–insect interactions: Picturing more resistant crops. Curr. Opin. Insect Sci. 9:69–76. doi:10.1016/j.cois.2015.03.002
22. Haghighattalab, A. 2014. Orthomosaic generation. Zenodo, Genève, Switzerland. doi:10.5281/zenodo.46734
23. Haghighattalab, A. 2015. Plot boundary extraction. Zenodo, Genève, Switzerland. doi:10.5281/zenodo.46732
24. Haghighattalab, A., L. González Pérez, S. Mondal, D. Singh, D. Schinstock, J. Rutkoski et al. 2016. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 12:35. doi:10.1186/s13007-016-0134-6
25. Holland, J., W. Nyquist, and C. Cervantes-Martinez. 2003. Estimating and interpreting heritability for plant breeding: An update. Plant Breed. Rev. 22:9–112. doi:10.1002/9780470650202.ch2
26. Hruska, R., J. Mitchell, M. Anderson, and N.F. Glenn. 2012. Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle. Remote Sens. 4:2736–2752. doi:10.3390/rs4092736
27. Kupfer, J.A., and C.A. Farris. 2007. Incorporating spatial non-sta-tionarity of regression coefficients into predictive vegetation models. Landscape Ecol. 22:837–852. doi:10.1007/s10980-006-9058-2
28. Liebisch, F., N. Kirchgessner, D. Schneider, A. Walter, and A. Hund. 2015. Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 11:9. doi:10.1186/s13007-015-0048-8
29. Lopes, M.S., and M.P. Reynolds. 2012. Stay-green in spring wheat can be determined by spectral reflectance measurements (nor-malized difference vegetation index) independently from phenology. J. Exp. Bot. 63:3789–3798. doi:10.1093/jxb/ers071
30. Lu, B., M. Charlton, P. Harris, and A. Stewart. 2014a. Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data. Int. J. Geogr. Inf. Sci. 28:660–681. doi:10.1080/13658816.2013.865739
31. Lu, B., P. Harris, M. Charlton, and C. Brunsdon. 2014b. The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geospatial Inf. Sci. 17:85–101.
32. Manjunath, K.R., M.B. Potdar, and N.L. Purohit. 2002. Large area operational wheat yield model development and validation based on spectral and meteorological data. Int. J. Remote Sensing 23:3023–3038. doi:10.1080/01431160110104692
33. Mennis, J. 2006. Mapping the results of geographically weighted regression. Cartogr. J. 43:171–179. doi:10.1179/000870406X114658
34. Mevik, B.H., and R. Wehrens. 2007. The pls package: Principal component and partial least squares regression in R. J. Stat. Softw. 18:1–23. doi:10.18637/jss.v018.i02
35. Mevik, B.H., R. Wehrens, and K.H. Liland. 2013. pls: Partial least squares and principal component regression. R Foundation for Statistical Computing, Vienna, Austria.
36. Mitchell, A. 2005. The ESRI guide to GIS analysis. Vol. 2: Spatial measurements and statistics. ESRI Press, Redlands, CA.
37. Mkhabela, M.S., P. Bullock, S. Raj, S. Wang, and Y. Yang. 2011. Crop yield forecasting on the Canadian prairies using MODIS NDVI data. Agric. For. Meteorol. 151:385–393. doi:10.1016/j. agrformet.2010.11.012
38. Muir, J.F., J. Pretty, S. Robinson, S.M. Thomas, and C. Toulmin. 2010. Food Security: The challenge of feeding 9 billion people. Science 327:812–818. doi:10.1126/science.1185383
39. Nakaya, T. 2016. Windows application for geographically weighted regression modelling.
40. Oliver, M.A., and R. Webster. 2015. Geostatistical prediction: Kriging. In: Basic steps in geostatistics: The variogram and kriging. Springer Int. Publ., Wageningen, the Netherlands. p. 43–69.
41. Piepho, H.-P., and J. Möhring. 2007. Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888. doi:10.1534/genetics.107.074229
42. Poland, J. 2015. Breeding-assisted genomics. Curr. Opin. Plant Biol. 24:119–124. doi:10.1016/j.pbi.2015.02.009
43. Prasad, A.K., L. Chai, R.P. Singh, and M. Kafatos. 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Obs. Geoinf. 8:26–33. doi:10.1016/j.jag.2005.06.002
44. QGIS Development Team. 2015. QGIS Geographic Information System. Open Source Geospatial Foundation Project. https://www.qgis.org/(accessed 14 Dec. 2016).
45. Raun, W.R., J.B. Solie, G.V. Johnson, M.L. Stone, E.V. Lukina, W.E. Thomason, and J.S. Schepers. 2001. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 93:131–138. doi:10.2134/agronj2001.931131x
46. Ray, D.K., N.D. Mueller, P.C. West, and J.A. Foley. 2013. Yield trends are insufficient to double global crop production by 2050. PLoS One 8:e66428. doi:10.1371/journal.pone.0066428
47. Reynolds, M., J. Foulkes, R. Furbank, S. Griffiths, J. King, E. Murchie et al. 2012. Achieving yield gains in wheat. Plant Cell Environ. 35:1799–1823. doi:10.1111/j.1365-3040.2012.02588.x
48. Reynolds, M., and P. Langridge. 2016. Physiological breeding. Curr. Opin. Plant Biol. 31:162–171. doi:10.1016/j. pbi.2016.04.005
49. Reynolds, M., Y. Manes, A. Izanloo, and P. Langridge. 2009. Phenotyping approaches for physiological breeding and gene discovery in wheat. Ann. Appl. Biol. 155:309–320. doi:10.1111/j.1744-7348.2009.00351.x
50. Rischbeck, P., S. Elsayed, B. Mistele, G. Barmeier, and K. Heil. 2016. Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley. Eur. J. Agron. 78:44–59. doi:10.1016/j. eja.2016.04.013
51. Rosnell, T., and E. Honkavaara. 2012. Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera. Sensors (Basel Switzerland) 12:453–480. doi:10.3390/s120100453
52. Rutkoski, J., J. Poland, S. Mondal, E. Autrique, L.G. Párez, J. Crossa et al. 2016a. Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. G3: Genes, Genomes, Genet. 6:2799–2808. doi:10.1534/g3.116.032888
53. Rutkoski, J., J. Poland, S. Mondal, E. Autrique, L. Gonzalez Perez, J. Crossa et al. 2016b. Predictor traits from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. CIMMYT, Mexico, DF.
54. Salazar, L., F. Kogan, and L. Roytman. 2007. Use of remote sensing data for estimation of winter wheat yield in the United States. Int. J. Remote Sens. 28:3795–3811. doi:10.1080/01431160601050395
55. Singh, S.P., O.P. Bishnoi, R. Niwas, and M. Singh. 2001. Relationship of wheat grain yield with spectral indices. J. Indian Soc. Remote Sensing 29:93–96. doi:10.1007/BF02989919
56. Smith, G.M., and E.J. Milton. 1999. The use of the empirical line method to calibrate remotely sensed data to reflectance. Int. J. Remote Sensing 20:2653–2662. doi:10.1080/014311699211994
57. Sultana, S.R., A. Ali, A. Ahmad, M. Mubeen, S. Ahmad, S. Ercisli, and H.Z.E. Jaafar. 2014. Normalized difference vegetation index as a tool for wheat yield estimation: A case study from Faisalabad, Pakistan. Sci. World J. 2014:725326. doi:10.1155/2014/725326
58. Teal, R.K., B. Tubana, K. Girma, K.W. Freeman, D.B. Arnall, O. Walsh, and W.R. Raun. 2006. In-season prediction of corn grain yield potential using normalized difference vegetation index. Agron. J. 98:1488–1494. doi:10.2134/agronj2006.0103
59. Tester, M., and P. Langridge. 2010. Breeding technologies to increase crop production in a changing world. Science 327:818–822. doi:10.1126/science.1183700
60. Tobler, A.W.R. 1970. A computer movie simulating urban growth in the Detroit region. Econ. Geogr. 46:234–240. doi:10.2307/143141
61. USDA. 2016. World agricultural supply and demand estimates. Off. Chief Econ., USDA. http://www.usda.gov/oce/com-modity/wasde/latest.pdf (accessed 14 Dec. 2016).
62. Wang, K., C. Zhang, and W. Li. 2012. Comparison of geographically weighted regression and regression kriging for estimating the spatial distribution of soil organic matter. GIsci. Remote Sens. 49:915–932. doi:10.2747/1548-1603.49.6.915
63. Wang, L., Y. Tian, X. Yao, Y. Zhu, and W. Cao. 2014. Predicting grain yield and protein content in wheat by fusing multi- sensor and multi-temporal remote-sensing images. Fields Crops Res. 164:178–188. doi:10.1016/j.fcr.2014.05.001
64. White, J., P. Andrade-Sanchez, M.A. Gore, K.F. Bronson, T.A. Coffelt, M.M. Conley et al. 2012. Field-based phenomics for plant genetics research. Field Crops Res. 133:101–112. doi:10.1016/j.fcr.2012.04.003
65. You, X., J. Meng, M. Zhang, and T. Dong. 2013. Remote sensing based detection of crop phenology for agricultural zones in china using a new threshold method. Remote Sens. 5:3190– 3211. doi:10.3390/rs5073190
66. Zadoks, J.C., T.T. Chang, and C.F. Konzak. 1974. A decimal code for the growth stages of cereals. Weed Res. 14:415–421. doi:10.1111/j.1365-3180.1974.tb01084.x