Erratum: Quantitative estimation of wheat phenotyping traits using ground and aerial imagery [Remote Sens., 10, 950, (2018)]doi 10.3390/rs10060950;Quantitative estimation of wheat phenotyping traits using ground and aerial imagery

Remote Sensing

Volumn 10, Issue 6, 2018, Pages

  Khan, Zohaib ^a   Chopin, Joshua ^a   Cai, Jinhai ^a   Eichi, Vahid Rahimi ^b   Haefele, Stephan ^b   Miklavcic, Stanley J ^a  

^a UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

^b UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Canopy imaging; Canopy traits; Field phenotyping; Height; Mobile ground platform; Unmanned aerial vehicle; Vigour; Wheat

Indexed keywords

AERIAL PHOTOGRAPHY; FERTILIZERS; UNMANNED AERIAL VEHICLES (UAV);

CANOPY TRAITS; HEIGHT; MOBILE GROUND PLATFORM; PHENOTYPING; VIGOUR; WHEAT;

ANTENNAS;

EID: 85048965289     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10071081     Document Type: Erratum

References (48)

1. Salisbury, F.B.; Ross, C.W. Plant Physiology, 4th ed.;Wadsworth Publishing: Belmont, CA, USA, 1992
2. Patrick, A.; Li, C. High throughput phenotyping of blueberry bush morphological traits using unmanned aerial systems. Remote Sens. 2017, 9, 1250, doi:10.3390/rs9121250
3. Gnädinger, F.; Schmidhalter, U. Digital counts ofmaize plants by unmanned aerial vehicles (UAVs). Remote Sens. 2017, 9, doi:10.3390/rs9060544
4. Virlet, N.; Sabermanesh, K.; Sadeghi-Tehran, P.; Hawkesford, M.J. Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring. Funct. Plant Biol. 2017, 44, 143-153, doi:10.1071/FP16163
5. Cai, J.; Kumar, P.; Chopin, J.; Miklavcic, S.J. Land-based crop phenotyping by image analysis: Accurate estimation of canopy height distributions using stereo images. PLoS ONE 2018, 13, e0196671, doi:10.1371/journal.pone.0196671
6. Deery, D.; Jimenez-Berni, J.; Jones, H.; Sirault, X.; Furbank, R. Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 2014, 4, 349-379, doi:10.3390/agronomy4030349
7. Schirrmann, M.; Giebel, A.; Gleiniger, F.; Pflanz, M.; Lentschke, J.; Dammer, K.H. Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sens. 2016, 8, 706
8. de Castro, A.I.; Torres-Sánchez, J.; Peña, J.M.; Jiménez-Brenes, F.M.; Csillik, O.; López-Granados, F. An automatic Random Forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens. 2018, 10, 285. doi:10.3390/rs10020285
9. Haghighattalab, A.; Pérez, L.G.; Mondal, S.; Singh, D.; Schinstock, D.; Rutkoski, J.; Ortiz-Monasterio, I.; Singh, R.P.; Goodin, D.; Poland, J. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 2016, 12, 35. doi:10.1186/s13007-016-0134-6
10. Kipp, S.; Mistele, B.; Baresel, P.; Schmidhalter, U. High-throughput phenotyping early plant vigour of winter wheat. Eur. J. Agron. 2014, 52, 271-278, doi:10.1016/j.eja.2013.08.009
11. Di Gennaro, S.F.; Rizza, F.; Badeck, F.W.; Berton, A.; Delbono, S.; Gioli, B.; Toscano, P.; Zaldei, A.; Matese, A. UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices. Int. J. Remote Sens. 2017, doi:10.1080/01431161.2017.1395974
12. Watanabe, K.; Guo, W.; Arai, K.; Takanashi, H.; Kajiya-Kanegae, H.; Kobayashi, M.; Yano, K.; Tokunaga, T.; Fujiwara, T.; Tsutsumi, N.; et al. High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front. Plant. Sci. 2017, 8, 421. doi:10.3389/fpls.2017.00421
13. Chen, D.; Shi, R.; Pape, J.M.; Neumann, K.; Arend, D.; Graner, A.; Chen, M.; Klukas, C. Predicting plant biomass accumulation from image-derived parameters. GigaScience 2018, 7, giy001, doi:10.1093/gigascience/giy001
14. Kirk, K.; Andersen, H.J.; Thomsen, A.G.; Jørgensen, J.R.; Jørgensen, R.N. Estimation of leaf area index in cereal crops using red-green images. Biosyst. Eng. 2009, 104, 308-317, doi:10.1016/j.biosystemseng.2009.07.001
15. Makanza, R.; Zaman-Allah, M.; Cairns, J.E.; Magorokosho, C.; Tarekegne, A.; Olsen, M.; Prasanna, B.M. High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging. Remote Sens. 2018, 10, 330, doi:10.3390/rs10020330
16. Holman, F.H.; Riche, A.B.; Michalski, A.; Castle, M.; Wooster, M.J.; Hawkesford, M.J. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens. 2016, 8, 1031, doi:10.3390/rs8121031
17. Jimenez-Berni, J.A.; Deery, D.M.; Rozas-Larraondo, P.; Condon, A.T.G.; Rebetzke, G.J.; James, R.A.; Bovill, W.D.; Furbank, R.T.; Sirault, X.R. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front. Plant. Sci. 2018, 9, 237, doi:10.3389/fpls.2018.00237
18. Benedetti, R.; Rossini, P. On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sens. Environ. 1993, 45, 311-326, doi:10.1016/0034-4257(93)90113-C
19. Panda, S.S.; Ames, D.P.; Panigrahi, S. Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sens. 2010, 2, 673-696, doi:10.3390/rs2030673
20. Moriondo, M.; Maselli, F.; Bindi, M. A simple model of regional wheat yield based on NDVI data. Eur. J. Agron. 2007, 26, 266-274, doi:10.1016/j.eja.2006.10.007
21. 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. 2001, 93, 131-138, doi:10.2134/agronj2001.931131x
22. 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. 2016, 217, 46-60, doi:10.1016/ j.agrformet.2015.11.009
23. Hamuda, E.; Glavin, M.; Jones, E. A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 2016, 125, 184-199, doi:10.1016/j.compag.2016.04.024
24. Meyer, G.E.; Neto, J.C. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 2008, 63, 282-293, doi:10.1016/j.compag.2008.03.009
25. Mao, W.;Wang, Y.;Wang, Y. Real-time detection of between-row weeds using machine vision. In Proceedings of the ASAE Annual Meeting. American Society of Agricultural and Biological Engineers, Las Vegas, NV, USA, 27-30 July 2003
26. Motohka, T.; Nasahara, K.N.; Oguma, H.; Tsuchida, S. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens. 2010, 2, 2369-2387, doi:10.3390/rs2102369
27. 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. 2016, 74, 75-92, doi:10.1016/j.eja.2015.11.026
28. Hunt, E.R.; Cavigelli, M.; Daughtry, C.S.; Mcmurtrey, J.E.;Walthall, C.L. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis. Agric. 2005, 6, 359-378, doi:10.1007/s11119-005-2324-5
29. Hoffmann, H.; Jensen, R.; Thomsen, A.; Nieto, H.; Rasmussen, J.; Friborg, T. Crop water stress maps for an entire growing season from visible and thermal UAV imagery. Biogeosciences 2016, 13, 6545, doi:10.5194/bg-13-6545-2016
30. Montes, J.M.; Technow, F.; Dhillon, B.S.; Mauch, F.; Melchinger, A.E. High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res. 2011, 121, 268-273, doi:10.1016/j.fcr.2010.12.017
31. Mistele, B.; Schmidhalter, U. Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic. Field Crops Res. 2008, 106, 94-103, doi:10.1016/j.fcr.2007.11.002
32. Mistele, B.; Schmidhalter, U. Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat. Agron. J. 2010, 102, 499-506, doi:10.2134/agronj2009.0282
33. Andrade-Sanchez, P.; Gore, M.A.; Heun, J.T.; Thorp, K.R.; Carmo-Silva, A.E.; French, A.N.; Salvucci, M.E.; White, J.W. Development and evaluation of a field-based high-throughput phenotyping platform. Funct. Plant Biol. 2014, 41, 68-79, doi:10.1071/FP13126
34. Freeman, K.W.; Girma, K.; Arnall, D.B.; Mullen, R.W.; Martin, K.L.; Teal, R.K.; Raun, W.R. By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agron. J. 2007, 99, 530-536, doi:10.2134/agronj2006.0135
35. White, J.W.; Conley, M.M. A flexible, low-cost cart for proximal sensing. Crop Sci. 2013, 53, 1646-1649, doi:10.2135/cropsci2013.01.0054
36. Candiago, S.; Remondino, F.; De Giglio, M.; Dubbini, M.; Gattelli, M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 2015, 7, 4026-4047, doi:10.3390/rs70404026
37. Gracia-Romero, A.; Kefauver, S.C.; Vergara-Diaz, O.; Zaman-Allah, M.A.; Prasanna, B.M.; Cairns, J.E.; Araus, J.L. Comparative performance of ground versus aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization. Front. Plant. Sci. 2017, 8, 2004. doi:10.3389/fpls.2017.02004
38. Vergara-Díaz, O.; Zaman-Allah, M.A.; Masuka, B.; Hornero, A.; Zarco-Tejada, P.; Prasanna, B.M.; Cairns, J.E.; Araus, J.L. A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Front. Plant Sci. 2016, 7, doi:10.3389/fpls.2016.00666
39. Liebisch, F.; Kirchgessner, N.; Schneider, D.; Walter, A.; Hund, A. Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 2015, 11, 9. doi:10.1186/s13007-015-0048-8
40. Madec, S.; Baret, F.; De Solan, B.; Thomas, S.; Dutartre, D.; Jezequel, S.; Hemmerlé, M.; Colombeau, G.; Comar, A. High-throughput phenotyping of plant height: Comparing unmanned aerial vehicles and ground LiDAR estimates. Front. Plant Sci. 2017, 8, 2002:1-2002:14, doi:10.3389/fpls.2017.02002
41. Chopin, J.; Kumar, P.; Miklavcic, S.J. Land-based crop phenotyping by image analysis: Consistent canopy characterization from inconsistent field illumination. Plant Methods 2018, 14, 39. doi:10.1186/s13007-018-0308-5
42. Hirschmueller, H. Stereo processing by semiglobal matching and mutual information. Trans. Pattern Anal. Mach. Intell. 2008, 30, 328-341, doi:10.1109/TPAMI.2007.1166
43. Koenderink, J.J.; Van Doorn, A.J. Affine structure from motion. J. Opt. Soc. Am. A 1991, 8, 377-385, doi:10.1364/JOSAA.8.000377
44. Heikkila, J.; Silven, O. A four-step camera calibration procedure with implicit image correction. In Proceedings of the Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, USA, 17-19 June 1997; pp. 1106-1112
45. Khan, Z.; Rahimi-Eichi, V.; Haefele, S.; Garnett, T.; Miklavcic, S.J. Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging. Plant Methods 2018, 14, 20. doi:10.1186/s13007-018-0287-6
46. Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693-712, doi:10.1007/s11119-012-9274-5
47. Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 2013, 114, 358-371, doi:10.1016/j.biosystemseng.2012.08.009
48. Ni, J.; Yao, L.; Zhang, J.; Cao, W.; Zhu, Y.; Tai, X. Development of an unmanned aerial vehicle-borne crop-growth monitoring system. Sensors 2017, 17, 502, doi:10.3390/s17030502