Abstract
Perennial ryegrass (Lolium perenne) is a perennial crop used in temperate regions as forage. In L. perenne breeding programs, persistency is an important trait. Poor persistency results in sward degradation and associated yield and nutritive value losses. Breeders assess persistency of accessions using visual scoring in field plots during the 2nd or 3rd growing season. This evaluation system is easy and cheap but is not free from human bias. In this study, the correlation between the scoring done by different breeders was only 0.243. As an alternative we have developed a methodology to assess persistency of L. perenne breeding materials based on vegetation indices (VIs) derived from Unmanned Aerial Vehicle (UAV) imagery. The VIs Excess green (ExG2), Green Leaf Index and Normalized green intensity (GCC) were found to provide consistent results for flights carried out under different light conditions and were validated by ground reference information. The correlation between the VIs and the percentage of ground cover extracted from on-ground imagery was 0.885. To test the implementation of the method we compared the ExG2 value based approach to selection with a visual score based selection methodology as applied by two breeders. The breeding decisions of Breeder A agreed well with decisions based on ExG2 values (74.6%), but those of Breeder B displayed a lower agreement (54.0%). In contrast, agreement between decisions based on different flights was very high (91.6%). The methodology was validated for general applicability. In summary, the results demonstrate that basing persistency selection in L. perenne breeding programs on ExG2 values from UAV imagery is likely to be more objective in comparison to the currently-used visual scoring method.
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References
Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19:52–61. https://doi.org/10.1016/j.tplants.2013.09.008
Burgon A, Bondesen OB, Verburgt WH, Hall AG, Bark NS, Robinson M, Timm G, Fairey DT, Hampton JG (1998) The forage seed trade. Forage seed Prod. vol 1 Temp. species, pp 271–286
Camargo Neto J (2004) A combined statistical-soft computing approach for classification and mapping weed species in minimum -tillage systems. ETD Collect. Univ. Nebraska - Lincoln
Casler MD, Casler MD, van Santen E (2010) Breeding objectives in forages. In: Boller B, Posselt UK, Veronesi F (eds) Fodder crops and amenity grasses. Springer, New York, pp 115–136. https://doi.org/10.1007/978-1-4419-0760-8_5
Caturegli L, Corniglia M, Gaetani M, Grossi N, Magni S, Migliazzi M, Angelini L, Mazzoncini M, Silvestri N, Fontanelli M, Raffaelli M, Peruzzi A, Volterrani M (2016) Unmanned aerial vehicle to estimate nitrogen status of turfgrasses. PLoS ONE 11:1–13. https://doi.org/10.1371/journal.pone.0158268
Chaves B, De Vliegher A, Van Waes J, Carlier L, Marynissen B (2009) Change in agronomic performance of Lolium perenne and Lolium multiflorum varieties in the past 40 years based on data from Belgian VCU trials. Plant Breed. 128:680–690. https://doi.org/10.1111/j.1439-0523.2009.01621.x
Gitelson AA, Kaufman YJ, Stark R, Rundquist D (2002) Novel algorithms for remote estimation of vegetation fraction. Rem Sen Envir 80(1):76–87. https://doi.org/10.1016/S0034-4257(01)00289-9
Gobron N, Pinty B, Verstraete MM, Widlowski JL (2000) Advanced vegetation indices optimized for up-coming sensors: design, performance, and applications. IEEE Trans Geosci Remote Sens 38:2489–2505. https://doi.org/10.1109/36.885197
Govaerts YM, Verstraete MM, Pinty B, Gobron N (1999) Designing optimal spectral indices: A feasibility and proof of concept study. Int J Remote Sens 20:1853–1873. https://doi.org/10.1080/014311699212524
Guerrero JM, Pajares G, Montalvo M, Romeo J, Guijarro M (2012) Support vector machines for crop/weeds identification in maize fields. Expert Syst Appl 39:11149–11155. https://doi.org/10.1016/j.eswa.2012.03.040
Guijarro M, Pajares G, Riomoros I, Herrera PJ, Burgos-Artizzu XP, Ribeiro A (2011) Automatic segmentation of relevant textures in agricultural images. Comput Electron Agric 75:75–83. https://doi.org/10.1016/J.COMPAG.2010.09.013
Guijarro M, Riomoros I, Pajares G, Zitinski P (2015) Discrete wavelets transform for improving greenness image segmentation in agricultural images. Comput Electron Agric 118:396–407. https://doi.org/10.1016/j.compag.2015.09.011
Hague T, Tillett ND, Wheeler H (2006) Automated crop and weed monitoring in widely spaced cereals. Precis Agric 7:21–32. https://doi.org/10.1007/s11119-005-6787-1
Hatton TJ, West NE, Johnson PS (1986) Relationships of the error associated with ocular estimation and actual total cover. J Range Manag 39:91–92. https://doi.org/10.2307/3899697
Herrmann D, Boller B, Studer B, Widmer F, Kölliker R (2008) Improving persistence in red clover: Insights from QTL analysis and comparative phenotypic evaluation. Crop Sci 48:269–277. https://doi.org/10.2135/cropsci2007.03.0143
Horst GL, Engelke MC, Meyers W (1984) assessment of visual evaluation techniques. Agron J 76:619–622. https://doi.org/10.2134/agronj1984.00021962007600040027x
Humphreys M, Feuerstein U, Vandewalle M, Baert J (2010) Ryegrasses. In: Boller B, Posselt UK, Veronesi F (eds) Fodder crops and amenity grasses. Springer, New York, pp 211–260. https://doi.org/10.1007/978-1-4419-0760-8_10
Kataoka T, Kaneko T, Okamoto H, Hata S (2003) Crop growth estimation system using machine vision. In: Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003) vol 2, pp 1079–1083. https://doi.org/10.1109/AIM.2003.1225492
Kennedy KA, Addison PA (1987) Some considerations for the use of visual estimates of plant cover in biomonitoring. J Ecol 75:151–157
Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH (1993) The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Environ, Remote Sens. https://doi.org/10.1016/0034-4257(93)90013-N
Liu ZY, Huang JF, Wu XH, Dong YP (2007) Comparison of vegetation indices and red-edge parameters for estimating grassland cover from canopy reflectance data. J Integr Plant Biol 49:299–306. https://doi.org/10.1111/j.1744-7909.2007.00401.x
Liu Y, Mu X, Wang H, Yan G (2012) A novel method for extracting green fractional vegetation cover from digital images. J Veg Sci 23:406–418. https://doi.org/10.1111/j.1654-1103.2011.01373.x
Lootens P, Ruttink T, Rohde A, Combes D, Barre P, Ruiz IR (2016) High—throughput phenotyping of lateral expansion and regrowth of spaced Lolium perenne plants using on—field image analysis. Plant Methods. https://doi.org/10.1186/s13007-016-0132-8
Luscier JD, Thompson WL, Wilson JM, Gorham BE, Dragut LD (2006) Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots. Front Ecol Environ 4:408–413. https://doi.org/10.1890/1540-9295(2006)4[408:UDPAOI]2.0.CO;2
Lynch TMH, Barth S, Dix PJ, Grogan D, Grant J, Grant OM (2015) Ground Cover Assessment of Perennial Ryegrass Using Digital Imaging. Agron J 107:2347. https://doi.org/10.2134/agronj15.0185
Mao W, Wang Y, Wang Y (2003) Real-time detection of between-row weeds using machine vision. In: 2003 ASAE Annu. Meet. 0300, 1
Meyer GE, Neto JC (2008) Verification of color vegetation indices for automated crop imaging applications. Comput Electron Agric 63:282–293. https://doi.org/10.1016/j.compag.2008.03.009
Meyer GE, Hindman TW, Laksmi K (1999) Machine vision detection parameters for plant species identification. In: Meyer GE, DeShazer JA (eds) Precision agriculture and biological quality, Proceedings of SPIE, Bellingham, WA, vol 3543, pp 327–335. Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States. https://doi.org/10.1117/12.336896
Milberg P, Bergstedt J, Fridman J, Odell G, Westerberg L (2008) Observer bias and random variation in vegetation monitoring data. J Veg Sci 19:633–644. https://doi.org/10.3170/2008-8-18423
Popay AJ, Hume DE (2011) Endophytes improve ryegrass persistence by controlling insects. Pasture persistence—Grassl. Res Pract Ser 15:149–156
Rasmussen J, Ntakos G, Nielsen J, Svensgaard J, Poulsen RN, Christensen S (2016) Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? Eur J Agron 74:75–92. https://doi.org/10.1016/j.eja.2015.11.026
Richardson MD, Karcher DE, Purcell LC (2001) Quantifying turfgrass cover using digital image analaysis. Crop Sci 41:1884–1888. https://doi.org/10.2135/cropsci2001.1884
Salamí E, Barrado C, Pastor E (2014) UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sens. 6:11051–11081. https://doi.org/10.3390/rs61111051
Sonnentag O, Hufkens K, Teshera-Sterne C, Young AM, Friedl M, Braswell BH, Milliman T, O’Keefe J, Richardson AD (2012) Digital repeat photography for phenological research in forest ecosystems. Agric For Meteorol 152:159–177. https://doi.org/10.1016/j.agrformet.2011.09.009
Taylor NL, Quesenberry KH (1996) Red clover science. Kluwer Academic Publishers, Dordrecht
Torres-Sánchez J, Peña JM, de Castro AI, López-Granados F (2014) Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput Electron Agric 103:104–113. https://doi.org/10.1016/j.compag.2014.02.009
Walter A, Studer B, Kölliker R (2012) Advanced phenotyping offers opportunities for improved breeding of forage and turf species. Ann Bot 110:1271–1279. https://doi.org/10.1093/aob/mcs026
Wilkins PW, Humphreys MO (2003) Progress in breeding perennial clovers for temperate agriculture. J Agric Sci 140:129–150. https://doi.org/10.1017/S0021859605005101
Woebbecke DM, Meyer GE, Von Bargen K, Mortensen DA (1995) Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 38:259–269. https://doi.org/10.13031/2013.27838
Xiang H, Tian L (2011) Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform. Biosyst Eng 108:104–113. https://doi.org/10.1016/j.biosystemseng.2010.11.003
Acknowledgements
The research reported in this article was carried out at ILVO as part of the ISense project (Isense.farm and www.ilvo.vlaanderen.be) without any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors wish to thank Thomas Vanderstocken and Filip De Brouwer for piloting the UAV flights and Miriam Levenson for English language corrections.
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Borra-Serrano, I., De Swaef, T., Aper, J. et al. Towards an objective evaluation of persistency of Lolium perenne swards using UAV imagery. Euphytica 214, 142 (2018). https://doi.org/10.1007/s10681-018-2208-1
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DOI: https://doi.org/10.1007/s10681-018-2208-1