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The use of RGB cameras in defining crop development in legumes

Published online by Cambridge University Press:  01 June 2017

I. Travlos ,
A. Mikroulis ,
E. Anastasiou ,
S. Fountas ,
D. Bilalis ,
Z. Tsiropoulos  and
A. Balafoutis
I. Travlos*
Affiliation:
Department of Crop Science, Agricultural University of Athens, 75, IeraOdos str., 11855 Athens, Greece
A. Mikroulis
Affiliation:
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75, Iera Odos str., 11855 Athens, Greece
E. Anastasiou
Affiliation:
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75, Iera Odos str., 11855 Athens, Greece
S. Fountas
Affiliation:
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75, Iera Odos str., 11855 Athens, Greece
D. Bilalis
Affiliation:
Department of Crop Science, Agricultural University of Athens, 75, IeraOdos str., 11855 Athens, Greece
Z. Tsiropoulos
Affiliation:
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75, Iera Odos str., 11855 Athens, Greece
A. Balafoutis
Affiliation:
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75, Iera Odos str., 11855 Athens, Greece
*
†E-mail: htravlos@yahoo.gr
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Abstract

The human population is expected to reach 9 billion by 2050 and thus high yield crop varieties need to be developed. Remote sensing can estimate crop parameters non-destructively and quickly. The aim of this study was to compare and evaluate the use of a commercial RGB camera with an expensive canopy sensor in the crop development of two legumes. The RGB camera based vegetation index (NGRDI) was compared with the canopy sensor derived vegetation indices (NDVI and NDRE) for estimating legume crop growth parameters. The results indicated that the use of a simple digital camera RGB can in some cases replace spectral canopy sensors.

Keywords

NGRDINDVINDRERGB cameraLegumes
Type
Crop Sensors and Sensing
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 224 - 228
Copyright
© The Animal Consortium 2017 

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References

Andrade-Sanchez, P, Gore, MA, Heun, JT, Thorp, KR, Carmo-Silva, AE, French, AN, Salvucci, ME and White, JW 2014. Development and evaluation of a field-based high-throughput phenotyping platform. Functional Plant Biology 41, 68–79.Google Scholar
Araus, JL and Cairns, JE 2014. Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science 19 (1), 52–61.Google Scholar
Edgerton, MD 2009. Increasing Crop Productivity to Meet Global Needs for Feed, Food, and Fuel. Plant Physiology 149 (1), 7–13.Google Scholar
Frenda, AS, Ruisi, P, Saia, S, Frangipane, B, Di Miceli, G, Amato, G and Giambalvo, D 2013. The critical period of weed control in faba bean and chickpea in Mediterranean areas. Weed Science 61, 452–459.CrossRefGoogle Scholar
Hosoi, F and Omasa, K 2009. Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging. ISPRS Journal of Photogrammetry and Remote Sensing 64, 151–158.Google Scholar
Huete, AR 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment 25, 295–309.Google Scholar
Kang, Y, Khan, S and Ma, X 2009. Climate change impacts on crop yield, crop water productivity and food security – A review. Progress in Natural Science 119 (12), 1665–1674.Google Scholar
Kelly, D, Vatsa, A, Mayham, W, Ngô, L, Thompson, A and Kazic, T 2016. An opinion on imaging challenges in phenotyping field crops. Machine Vision and Applications 5, 681–694.Google Scholar
Li, L, Zhang, Q and Huang, D 2014. A Review of Imaging Techniques for Plant Phenotyping. Sensors 14, 20078–20111.Google Scholar
Paulus, S, Behmann, J, Mahlein, AK, Plumer, L and Kuhlmann, H 2014. Low-Cost 3D Systems: Suitable Tools for Plant Phenotyping. Sensors 14 (22), 3001–3018.Google Scholar
Rahaman, MM, Chen, D, Gillani, Z, Klukas, C and Chen, M 2015. Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Frontiers in Plant Science 6, 619.CrossRefGoogle Scholar
Rosell, JR, Llorens, J, Sanz, R, Arno, J, Ribes-Dasi, M, Masip, J, Escola, A, Camp, F, Solanelles, F, Gracia, F, Gil, E, Val, L, Planas, S and Palacin, J 2009. Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning. Agricultural and Forest Meteorology 149, 1505–1515.Google Scholar
Travlos, IS 2010. Legumes as cover crops or components of intercropping systems and their effects on weed populations and crop productivity. In Advances in Food Science and Technology (edited by AJ Greco), Nova Science Publishers Inc., Hauppauge, New York, USA. 1, 151–164.Google Scholar
Travlos, IS, Economou, G and Kanatas, PJ 2011. Corn and barnyard grass competition as influenced by relative time of weed emergence and corn hybrid. Agronomy Journal 103, 1–6.Google Scholar
Vollmann, J, Walter, H, Sato, T and Schweiger, P 2011. Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean. Computers and Electronics in Agriculture 75, 190–195.CrossRefGoogle Scholar
Wang, W and Li, C 2014. Size estimation of sweet onions using consumer-grade RGB-depth sensor. Journal of Food Engineering 142, 153–162.Google Scholar
White, JW, Andrade-Sanchez, P, Gore, MA, Bronson, KF, Coffelt, TA, Conley, MM, Feldmann, A, French, AN, Heun, JT, Hunsaker, DJ, Jenks, MA, Kimball, BA, Roth, RL, Strand, RJ, Thorp, KR, Wall, GW and Wang, G 2012. Field-based phenomics for plant genetics research. Field Crops Research 133, 101–112.Google Scholar
Yohannes, H 2016. A review on relationship between climate change and agriculture. Journal of Earth Science and Climatic Change 7, 335.Google Scholar