Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle

Field Crops Research

Volumn 210, Issue , 2017, Pages 71-80

  Duan, T ^a,b   Chapman, S C ^a,c   Guo, Y ^b   Zheng, B ^a  

^a CSIRO   (Australia)

^b CHINA AGRICULTURAL UNIVERSITY   (China)

^c UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

High throughput; Image processing; Plot segmentation; Unmanned aerial vehicle; Vegetative index

Indexed keywords

AGRONOMY; AIRCRAFT; BIOMASS; CULTIVAR; FLOWERING; GENOTYPE-ENVIRONMENT INTERACTION; GROUND COVER; GROWING SEASON; IMAGE PROCESSING; INDEX METHOD; NDVI; PHENOTYPE; PHYSIOLOGY; SEGMENTATION; WHEAT;

TRITICUM AESTIVUM;

EID: 85020381651     PISSN: 03784290     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fcr.2017.05.025     Document Type: Article

References (64)

1. Aase, J.K., Siddoway, F.H., Assessing winter wheat dry matter production via spectral reflectance measurements. Remote Sens. Environ. 11 (1981), 267–277, 10.1016/0034-4257(81)90025-0.
2. Araus, J.L., Cairns, J.E., Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19 (2014), 52–61, 10.1016/j.tplants.2013.09.008.
3. Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., Bareth, G., Estimating biomass of barley using crop surface models (CSMs) Derived from UAV-based RGB imaging. Remote Sens. 6 (2014), 10395–10412, 10.3390/rs61110395.
4. Brown, M.E., de Beurs, K.M., Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sens. Environ. 112 (2008), 2261–2271, 10.1016/j.rse.2007.10.008.
5. Cabrera-Bosquet, L., NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Res. Commun. 39 (2011), 147–159, 10.1556/CRC.39.2011.1.15.
6. Chapman, S.C., Merz, T., Chan, A., Jackway, P., Hrabar, S., Dreccer, M.F., Holland, E., Zheng, B., Ling, T.J., Jimenez-Berni, J., Pheno-copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy 4 (2014), 279–301, 10.3390/agronomy4020279.
7. Christopher, J.T., Christopher, M.J., Borrell, A.K., Fletcher, S., Chenu, K., Stay-green traits to improve wheat adaptation in well-watered and water-limited environments. J. Exp. Bot. 67 (2016), 5159–5172.
8. Coombes, N., DiGGer: DiGGer Design Generator Under Correlation and Blocking. 2016.
9. Cooper, M., Hammer, G.L., Plant Adaptation and Crop Improvement. 1996, CAB International.
10. Crusiol, L.G.T., Carvalho, J. de F.C., Sibaldelli, R.N.R., Neiverth, W., Rio, A. do, Ferreira, L.C., Procópio, S. de O., Mertz-Henning, L.M., Nepomuceno, A.L., Neumaier, N., Farias, J.R.B., NDVI variation according to the time of measurement, sampling size, positioning of sensor and water regime in different soybean cultivars. Precis. Agric., 2016, 1–21, 10.1007/s11119-016-9465-6.
11. Deery, D., Jimenez-Berni, J., Jones, H., Sirault, X., Furbank, R., Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4 (2014), 349–379, 10.3390/agronomy4030349.
12. Duan, T., Chapman, S.C., Holland, E., Rebetzke, G.J., Guo, Y., Zheng, B., Dynamic quantification of canopy structure to characterize early plant vigour in wheat genotypes. J. Exp. Bot. 67 (2016), 4523–4534, 10.1093/jxb/erw227.
13. Duan, T., Zheng, B., Guo, W., Ninomiya, S., Guo, Y., Chapman, S.C., Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. Funct. Plant Biol. 44 (2017), 169–184, 10.1071/FP16123.
14. Edwards, D., Oldroyd, G., Kyratzis, A., Skarlatos, D., Fotopoulos, V., Vamvakousis, V., Katsiotis, A., Agriculture and climate change – adapting crops to increased uncertainty (AGRI 2015) investigating correlation among NDVI index derived by unmanned aerial vehicle photography and grain yield under late drought stress conditions. Procedia Environ. Sci. 29 (2015), 225–226, 10.1016/j.proenv.2015.07.284.
15. Erdle, K., Mistele, B., Schmidhalter, U., Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res. 124 (2011), 74–84, 10.1016/j.fcr.2011.06.007.
16. Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M.D., Neigh, C.S.R., Reichstein, M., Trend change detection in NDVI time series: effects of inter-annual variability and methodology. Remote Sens. 5 (2013), 2113–2144, 10.3390/rs5052113.
17. Foster, A.J., Kakani, V.G., Mosali, J., Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression. Precis. Agric., 2016, 1–18, 10.1007/s11119-016-9455-8.
18. Guo, W., Rage, U.K., Ninomiya, S., Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model. Comput. Electron. Agric. 96 (2013), 58–66, 10.1016/j.compag.2013.04.010.
19. Guo, L., An, N., Wang, K., Reconciling the discrepancy in ground- and satellite-observed trends in the spring phenology of winter wheat in China from 1993 to 2008. J. Geophys. Res. Atmos. 121 (2016), 1027–1042, 10.1002/2015JD023969.
20. Gutman, G.G., On the use of long-term global data of land reflectances and vegetation indices derived from the advanced very high resolution radiometer. J. Geophys. Res. Atmos. 104 (1999), 6241–6255, 10.1029/1998JD200106.
21. Haun, J.R., Visual quantification of wheat development1. Agron. J. 65 (1973), 116–119, 10.2134/agronj1973.00021962006500010035x.
22. Huang, K., Liu, Z., Yang, L., Evaluation of winter wheat productivity in Huang-Huai-Hai region by multi-year graded MODIS-NDVI. Trans. Chin. Soc. Agric. Eng. 30 (2014), 153–161, 10.3969/j.issn.1002-6819.2014.02.020.
23. Inman, D., Khosla, R., Reich, R., Westfall, D.G., Normalized difference vegetation index and soil color-based management zones in irrigated maize. Agron. J. 100 (2008), 60–66, 10.2134/agrojnl2007.0020.
24. Khot, L.R., Sankaran, S., Carter, A.H., Johnson, D.A., Cummings, T.F., UAS imaging-based decision tools for arid winter wheat and irrigated potato production management. Int. J. Remote Sens. 37 (2016), 125–137, 10.1080/01431161.2015.1117685.
25. Kitchen, N.R., Sudduth, K.A., Drummond, S.T., Scharf, P.C., Palm, H.L., Roberts, D.F., Vories, E.D., Ground-based canopy reflectance sensing for variable-rate nitrogen corn fertilization. Agron. J. 102 (2010), 71–84, 10.2134/agronj2009.0114.
26. Kumar, S., Röder, M.S., Singh, R.P., Kumar, S., Chand, R., Joshi, A.K., Kumar, U., Mapping of spot blotch disease resistance using NDVI as a substitute to visual observation in wheat (TriticumTriticum aestivum L.). Mol. Breed. 36 (2016), 1–11, 10.1007/s11032-016-0515-6.
27. Kyratzis, A., Skarlatos, D., Fotopoulos, V., Vamvakousis, V., Katsiotis, A., Investigating correlation among NDVI index derived by unmanned aerial vehicle photography and grain yield under late drought stress conditions. Procedia Environ. Sci. 29 (2015), 225–226, 10.1016/j.proenv.2015.07.284.
28. Lopes, M.S., Reynolds, M.P., Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology. J. Exp. Bot. 63 (2012), 3789–3798, 10.1093/jxb/ers071.
29. Lopresti, M.F., Di Bella, C.M., Degioanni, A.J., Relationship between MODIS-NDVI data and wheat yield: a case study in northern buenos aires province, argentina. Inf. Process. Agric. 2 (2015), 73–84, 10.1016/j.inpa.2015.06.001.
30. Ma, B.L., Dwyer, L.M., Costa, C., Cober, E.R., Morrison, M.J., Early prediction of soybean yield from canopy reflectance measurements. Agron. J. 93 (2001), 1227–1234, 10.2134/agronj2001.1227.
31. Magney, T.S., Eitel, J.U.H., Huggins, D.R., Vierling, L.A., Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality. Agric. For. Meteorol. 217 (2016), 46–60, 10.1016/j.agrformet.2015.11.009.
32. Magney, T.S., Vierling, L.A., Eitel, J.U.H., Huggins, D.R., Garrity, S.R., Response of high frequency Photochemical Reflectance Index (PRI) measurements to environmental conditions in wheat. Remote Sens. Environ. 173 (2016), 84–97, 10.1016/j.rse.2015.11.013.
33. Marti, J., Bort, J., Slafer, G. a., Araus, J. l., Can wheat yield be assessed by early measurements of Normalized Difference Vegetation Index?. Ann. Appl. Biol. 150 (2007), 253–257, 10.1111/j.1744-7348.2007.00126.x.
34. Mekliche, A., Hanifi-Mekliche, L., Aïdaoui, A., Gate, P.H., Bouthier, A., Monneveux, P.H., Grain yield and its components study and their association with normalized difference vegetation index (NDVI) under terminal water deficit and well-irrigated conditions in wheat (Triticum durum Desf. and Triticum aestivum L.). Afr. J. Biotechnol. 14 (2015), 2142–2148, 10.4314/ajb.v14i26.
35. Merz, T., Chapman, S., Autonomous unmanned helicopter system for remote sensing missions in unknown environments. Proceedings of the International Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g), Zurich, Switzerland, ISPRS, 2011.
36. Mora, M., Avila, F., Carrasco-Benavides, M., Maldonado, G., Olguín-Cáceres, J., Fuentes, S., Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies. Comput. Electron. Agric. 123 (2016), 195–202, 10.1016/j.compag.2016.02.011.
37. Morgounov, A., Gummadov, N., Belen, S., Kaya, Y., Keser, M., Mursalova, J., Association of digital photo parameters and NDVI with winter wheat grain yield in variable environments. Turk. J. Agric. For. 38 (2014), 624–632, 10.3906/tar-1312-90.
38. Mulla, D.J., Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst. Eng. 114 (2013), 358–371, 10.1016/j.biosystemseng.2012.08.009.
39. Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R.L., Mouazen, A.M., Wheat yield prediction using machine learning and advanced sensing techniques. Comput. Electron. Agric. 121 (2016), 57–65, 10.1016/j.compag.2015.11.018.
40. Paproki, A., Sirault, X., Berry, S., Furbank, R., Fripp, J., A novel mesh processing based technique for 3D plant analysis. BMC Plant Biol. 12 (2012), 63–75, 10.1186/1471-2229-12-63.
41. Perry, E.M., Morse-McNabb, E.M., Nuttall, J.G., O'Leary, G.J., Clark, R., Managing wheat from space: linking MODIS NDVI and crop models for predicting australian dryland wheat biomass. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7 (2014), 3724–3731, 10.1109/JSTARS.2014.2323705.
42. Pinto, R.S., Lopes, M.S., Collins, N.C., Reynolds, M.P., Modelling and genetic dissection of staygreen under heat stress. Theor. Appl. Genet., 2016, 1–20, 10.1007/s00122-016-2757-4.
43. Pound, M.P., French, A.P., Murchie, E.H., Pridmore, T.P., Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol. 166 (2014), 1688–1698, 10.1104/pp.114.248971.
44. R Development Core Team, R: A Language and Environment for Statistical Computing. 2012, R Foundation for Statistical Computing, Vienna, Austria.
45. Rasmussen, J., Ntakos, G., Nielsen, J., Svensgaard, J., Poulsen, R.N., Christensen, S., Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?. Eur. J. Agron. 74 (2016), 75–92, 10.1016/j.eja.2015.11.026.
46. Raun, W.R., Solie, J.B., Johnson, G.V., Stone, M.L., Lukina, E.V., Thomason, W.E., Schepers, J.S., In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 93 (2001), 131–138, 10.2134/agronj2001.931131x.
47. Raun, W.R., Solie, J.B., Johnson, G.V., Stone, M.L., Mullen, R.W., Freeman, K.W., Thomason, W.E., Lukina, E.V., Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron. J. 94 (2002), 815–820, 10.2134/agronj2002.8150.
48. Rouse, J.W. Jr., Haas, R.H., Schell, J.A., Deering, D.W., Monitoring vegetation systems in the great plains with Erts. NASA 351 (1974), 309–317.
49. Samborski, S.M., Gozdowski, D., Walsh, O.S., Lamb, David. W., Stępień, M., Gacek, E.S., Drzazga, T., Winter wheat genotype effect on canopy reflectance: implications for using NDVI for in-season nitrogen topdressing recommendations. Agron. J., 107, 2015, 2097, 10.2134/agronj14.0323.
50. Sankaran, S., Khot, L.R., Espinoza, C.Z., Jarolmasjed, S., Sathuvalli, V.R., Vandemark, G.J., Miklas, P.N., Carter, A.H., Pumphrey, M.O., Knowles, N.R., Pavek, M.J., Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur. J. Agron. 70 (2015), 112–123, 10.1016/j.eja.2015.07.004.
51. Schirrmann, M., Hamdorf, A., Garz, A., Ustyuzhanin, A., Dammer, K.-H., Estimating wheat biomass by combining image clustering with crop height. Comput. Electron. Agric. 121 (2016), 374–384, 10.1016/j.compag.2016.01.007.
52. Sharma, B., Ritchie, G.L., Rajan, N., Near-remote green: red perpendicular vegetation index ground cover fraction estimation in cotton. Crop Sci. 55 (2015), 2252–2261, 10.2135/cropsci2014.09.0625.
53. Soudani, K., Hmimina, G., Delpierre, N., Pontailler, J.-Y., Aubinet, M., Bonal, D., Caquet, B., de Grandcourt, A., Burban, B., Flechard, C., Guyon, D., Granier, A., Gross, P., Heinesh, B., Longdoz, B., Loustau, D., Moureaux, C., Ourcival, J.-M., Rambal, S., Saint André, L., Dufrêne, E., Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote Sens. Environ. 123 (2012), 234–245, 10.1016/j.rse.2012.03.012.
54. Tattaris, M., Reynolds, M.P., Chapman, S.C., A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Crop Sci. Hortic. 7 (2016), 1131–1139, 10.3389/fpls.2016.01131.
55. Tester, M., Langridge, P., Breeding technologies to increase crop production in a changing world. Science 327 (2010), 818–822, 10.1126/science.1183700.
56. Torres-Sánchez, J., López-Granados, F., De Castro, A.I., Peña-Barragán, J.M., Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS One 8 (2013), 58210–58224, 10.1371/journal.pone.0058210.
57. Torres-Sánchez, J., López-Granados, F., Serrano, N., Arquero, O., Peña, J.M., High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PLoS One 10 (2015), 1–20, 10.1371/journal.pone.0130479.
58. Tucker, C.J., Holben, B.N., Elgin, J., McMurtrey, I., Relationship of spectral data to grain yield variation. Photogramm. Eng. Remote Sens. 46 (1980), 657–666 (19800051130).
59. Tucker, C.J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8 (1979), 127–150, 10.1016/0034-4257(79)90013-0.
60. Wilson, P.B., Rebetzke, G.J., Condon, A.G., Of growing importance: combining greater early vigour and transpiration efficiency for wheat in variable rainfed environments. Funct. Plant Biol. 12 (2015), 1107–1115, 10.1071/FP15228.
61. Zadoks, J.C., Chang, T.T., Konzak, C.F., A decimal code for the growth stages of cereals. Weed Res. 14 (1974), 415–421, 10.1111/j.1365-3180.1974.tb01084.x.
62. Zarco-Tejada, P.J., Diaz-Varela, R., Angileri, V., Loudjani, P., Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur. J. Agron. 55 (2014), 89–99, 10.1016/j.eja.2014.01.004.
63. Zhang, C., Kovacs, J.M., The application of small unmanned aerial systems for precision agriculture: a review. Precis. Agric. 13 (2012), 693–712, 10.1007/s11119-012-9274-5.
64. Zhang, L., Guo, C.L., Zhao, L.Y., Zhu, Y., Cao, W.X., Tian, Y.C., Cheng, T., Wang, X., Estimating wheat yield by integrating the WheatGrow and PROSAIL models. Field Crops Res. 192 (2016), 55–66, 10.1016/j.fcr.2016.04.014.