A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform

Plant Science

Volumn 282, Issue , 2019, Pages 95-103

  Hassan, Muhammad Adeel ^a   Yang, Mengjiao ^a,b   Rasheed, Awais ^a   Yang, Guijun ^c   Reynolds, Matthew ^d   Xia, Xianchun ^a   Xiao, Yonggui ^a   He, Zhonghu ^a  

^a INSTITUTE OF PLANT PROTECTION   (China)

^b XINJIANG AGRICULTURAL UNIVERSITY   (China)

^c INSTITUTE OF PLANT AND ENVIRONMENT PROTECTION   (China)

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

Author keywords

High throughput phenotyping; Multi spectral imaging; Normalized difference vegetation index; Unmanned aerial vehicle

Indexed keywords

FOOD GRAIN; GENOTYPE; GROWTH, DEVELOPMENT AND AGING; PLANT LEAF; POACEAE; RADIATION RESPONSE; ULTRAVIOLET RADIATION; WHEAT;

EDIBLE GRAIN; GENOTYPE; PLANT LEAVES; POACEAE; TRITICUM; ULTRAVIOLET RAYS;

EID: 85055981496     PISSN: 01689452     EISSN: 18732259     Source Type: Journal    
DOI: 10.1016/j.plantsci.2018.10.022     Document Type: Article

References (46)

1. Rutkoski, J., Poland, J., Mondal, S., Autrique, E., Pérez, L.G., Crossa, J., Reynolds, M., Singh, R., Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. G3: Genes Genomes Genetics 6 (2016), 2799–2808.
2. Magney, T.S., Eitel, J.U., 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.
3. Foster, A., Kakani, V., Mosali, J., Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression. Precis. Agric. 18 (2017), 192–209.
4. Samborski, S.M., Gozdowski, D., Walsh, O.S., Lamb, D., 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), 2097–2106.
5. Tucker, C.J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8 (1979), 127–150.
6. Duan, T., Chapman, S., Guo, Y., Zheng, B., Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crops Res. 210 (2017), 71–80.
7. 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.
8. 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.
9. 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. Marti, J., Bort, J., Slafer, G., Araus, J., Can wheat yield be assessed by early measurements of Normalized Difference Vegetation Index?. Ann. Appl. Biol. 150 (2007), 253–257.
11. 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 (Triticum aestivum L.). Mol. Breed., 36, 2016, 95.
12. 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.
13. Babar, M., Reynolds, M., Van Ginkel, M., Klatt, A., Raun, W., Stone, M., Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci. 46 (2006), 578–588.
14. Araus, J.L., Cairns, J.E., Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19 (2014), 52–61.
15. 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.
16. Magney, T.S., Vierling, L.A., Eitel, J.U., 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.
17. 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.
18. 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.
19. Tattaris, M., Reynolds, M.P., Chapman, S.C., A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front. Plant Sci., 7, 2016, 1131.
20. Watanabe, K., Guo, W., Arai, K., Takanashi, H., Kajiya-Kanegae, H., Kobayashi, M., Yano, K., Tokunaga, T., Fujiwara, T., Tsutsumi, N., High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front. Plant Sci., 8, 2017, 421.
21. Berger, B., Parent, B., Tester, M., High-throughput shoot imaging to study drought responses. J. Exp. Bot. 61 (2010), 3519–3528.
22. Hassan, M.A., Yang, M.J., Rasheed, A., Jin, X., Xia, X.C., Xiao, Y.G., He, Z.H., Time-series multispectral indices from unmanned aerial vehicle imagery reveal senescence rate in bread wheat. Remote Sens., 10, 2018, 809.
23. 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, 12, 2016, 35.
24. Zhang, J., Yang, C., Song, H., Hoffmann, W.C., Zhang, D., Zhang, G., Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification. Remote Sens., 8, 2016, 257.
25. Fahlgren, N., Gehan, M.A., Baxter, I., Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr. Opin. Plant Biol. 24 (2015), 93–99.
26. Kross, A., McNairn, H., Lapen, D., Sunohara, M., Champagne, C., Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int. J. Appl. Earth Obs. Geoinf. 34 (2015), 235–248.
27. 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.
28. 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), 169–183.
29. 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.
30. Tattaris, M., Reynolds, M.P., Chapman, S.C., A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front. Plant Sci., 7, 2016.
31. Montesinos-López, O.A., Montesinos-López, A., Crossa, J., los Campos, G., Alvarado, G., Suchismita, M., Rutkoski, J., González-Pérez, L., Burgueño, J., Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data. Plant Methods, 13, 2017, 4.
32. Gnyp, M.L., Miao, Y., Yuan, F., Ustin, S.L., Yu, K., Yao, Y., Huang, S., Bareth, G., Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crops Res. 155 (2014), 42–55.
33. Ahamed, T., Tian, L., Zhang, Y., Ting, K.C., A review of remote sensing methods for biomass feedstock production. Biomass Bioenergy 35 (2011), 2455–2469.
34. Potgieter, A.B., George-Jaeggli, B., Chapman, S.C., Laws, K., Suárez Cadavid, L.A., Wixted, J., Watson, J., Eldridge, M., Jordan, D.R., Hammer, G.L., Multi-spectral imaging from an unmanned aerial vehicle enables the assessment of seasonal leaf area dynamics of sorghum breeding lines. Front. Plant Sci., 8, 2017, 1532.
35. Pask, A.J.D., Pietragalla, J., Mullan, D.M., Reynolds, M.P., Physiological breeding II: a field guide to wheat phenotyping. CIMMYT Mexico DF (Mexico), 4, 2012, 132.
36. Holland, J.B., Nyquist, W.E., Cervantes-Martínez, C.T., Estimating and interpreting heritability for plant breeding: an update. Janick, Jules, (eds.) Plant Breeding Reviews, 2010, John Wiley & Sons, Inc., 9–112.
37. Liu, S., Baret, F., Andrieu, B., Burger, P., Hemmerlé, M., Estimation of wheat plant density at early stages using high resolution imagery. Front. Plant Sci., 8, 2017, 739.
38. 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.
39. 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.
40. 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.
41. Crusiol, L.G.T., Carvalho, Jd.F.C., Sibaldelli, R.N.R., Neiverth, W., do Rio, A., Ferreira, L.C., Procópio, Sd.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. 18 (2017), 470–490.
42. 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, e0130479.
43. 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, e58210.
44. Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., Zhang, R., Feng, H., Zhao, X., Li, Z., Li, H., Yang, H., Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Front. Plant Sci., 8, 2017, 1111.
45. Wang, Z., Wang, J., Zhao, C., Zhao, M., Huang, W., Wang, C., Vertical distribution of nitrogen in different layers of leaf and stem and their relationship with grain quality of winter wheat. J. Plant Nutr. 28 (2005), 73–91.
46. Madani, A., Rad, A.S., Pazoki, A., Nourmohammadi, G., Zarghami, R., Wheat (Triticum aestivum L.) grain filling and dry matter partitioning responses to source:sink modifications under postanthesis water and nitrogen deficiency. Acta Sci. Agron. 32 (2010), 145–151.