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Multispectral imaging – a new tool in seed quality assessment?

Published online by Cambridge University Press:  27 June 2018

Birte Boelt ,
Santosh Shrestha ,
Zahra Salimi ,
Johannes Ravn Jørgensen ,
Mogens Nicolaisen  and
Jens Michael Carstensen
Birte Boelt*
Affiliation:
Department of Agroecology, Aarhus University, DK-4200 Slagelse, Denmark
Santosh Shrestha
Affiliation:
Department of Agroecology, Aarhus University, DK-4200 Slagelse, Denmark
Zahra Salimi
Affiliation:
Department of Agroecology, Aarhus University, DK-4200 Slagelse, Denmark
Johannes Ravn Jørgensen
Affiliation:
Department of Agroecology, Aarhus University, DK-4200 Slagelse, Denmark
Mogens Nicolaisen
Affiliation:
Department of Agroecology, Aarhus University, DK-4200 Slagelse, Denmark
Jens Michael Carstensen
Affiliation:
Videometer A/S, DK-2970 Hørsholm, Denmark Technical University of Denmark, DK-2800 Lyngby, Denmark
*
Author for correspondence: Birte Boelt, Email: [email protected]
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Abstract

Multispectral imaging is a new technology that is being deployed to assess seed quality parameters. Examples of applications in the detection and identification of fungi on seeds are presented, together with an example of the technology used for maturity determination in sugar beet seed. Results from multispectral imaging are compared with reference methods, and a high correlation is found. Applications of the technique for varietal discrimination and insect damage are also presented. There is a need for non-destructive, reliable and fast techniques, and it is concluded that multispectral imaging has potential for seed quality assessment, in particular for those components associated with surface structure and chemical composition, seed colour, morphology and size.

Keywords

chlorophyllseed developmentseed healthseed quality
Type
Review Paper
Information
Seed Science Research , Volume 28 , Special Issue 3: Seeds as Systems , September 2018 , pp. 222 - 228
Copyright
Copyright © Cambridge University Press 2018 

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References

Agrawal, GK and Rakwal, R (2012) Seed Development: OMICS Technologies Toward Improvement of Seed Quality and Crop Yield. Available at: https://link.springer.com/content/pdf/10.1007%2F978-94-007-4749-4.pdf (accessed 19 December 2017).Google Scholar
Baiano, A, Terracone, C, Peri, G and Romaniello, R (2012) Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes. Computers and Electronics in Agriculture 87, 142–151.Google Scholar
Barton, SA (2016) Cereals: Grain Defects, pp. 68–73 in Wrigley, C.W., Corke, H, Seetharaman, K and Faubion, J (eds), Encyclopedia of Food Grains. New York: Academic Press.Google Scholar
Bauriegel, E, Giebel, A, Geyer, M, Schmidt, U and Herppich, WB (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computers and Electronics in Agriculture 75, 304–312.Google Scholar
Bodevin, S, Larsen, TG, Lok, F, Carstensen, JM, Jørgensen, K and Skadhauge, B (2009) A rapid non-destructive method for quantification of fungal infection on barley and malt. Poster presented at 32nd EBC Congress, Hamburg, Germany, 10–14 May 2009.Google Scholar
Christian, M, Titze, J, Ilberg, V and Jacob, F (2011) Novel perspectives in gushing analysis: a review. Journal of The Institute of Brewing 117, 295–313.Google Scholar
Dammer, K.-H., Möller, B, Rodemann, B and Heppner, D (2011) Detection of head blight (Fusarium ssp.) in winter wheat by color and multispectral image analyses. Crop Protection 30, 420–428.Google Scholar
Deleuran, LC, Olesen, MH and Boelt, B (2013) Spinach seed quality: potential for combining seed size grading and chlorophyll fluorescence sorting. Seed Science Research 23, 271–278.Google Scholar
Dell'Aquila, A (2007) Towards new computer imaging techniques applied to seed quality testing and sorting. Seed Science and Technology 35, 519–538.Google Scholar
Dell'Aquila, A (2009) Development of novel techniques in conditioning, testing and sorting seed physiological quality. Seed Science and Technology 37, 608–624.Google Scholar
Esquerre, C, Gowen, AA, Downey, G and O'Donnell, CP (2012) Wavelength selection for development of a near infrared imaging system for early detection of bruise damage in mushrooms (Agaricus bisporus). Journal of Near Infrared Spectroscopy 20, 537–546.Google Scholar
Gagliardi, B and Marcos-Filho, J (2011) Relationship between germination and bell pepper seed structure assessed by the X-ray test. Scientia Agricola 68, 411–416.Google Scholar
Gomes-Junior, FG, Yagushi, JT, Belini, UL, Cicero, SM and Tomazello-Filho, M (2012) X-ray densitometry to assess internal seed morphology and quality. Seed Science and Technology 40, 102–107.Google Scholar
Gustafson, FG (1942) Parthenocarpy: natural and artificial. The Botanical Review 8, 599–654.Google Scholar
Hahn, F (2002) Multi-spectral prediction of unripe tomatoes. Biosystems Engineering 81, 147–155.Google Scholar
Hansen, MAE, Hay, FR, Carstensen, JM (2015) A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions. Plant Genetic Resources: Characterization and Utilization 14(3), 238–241.Google Scholar
Hills, OA (1963) Insects Affecting Sugar Beets Grown for Seed. Washington, DC: Agricultural Research Service, US Department of Agriculture.Google Scholar
Huang, M, Wang, QG, Zhu, QB, Qin, JW and Huang, G (2015) Review of seed quality and safety tests using optical sensing technologies. Seed Science and Technology 43, 337–366.Google Scholar
Jalink, H, Frandas, A, van der Schoor, R and Bino, JB (1998) Chlorophyll fluorescence of the testa of Brassica oleracea seeds as an indicator of seed maturity and seed quality. Scientia Agricola 55, 88–93.Google Scholar
Jaillais, B, Roumet, P, Pinson-Gadais, L and Bertrand, D (2015) Detection of Fusarium head blight contamination in wheat kernels by multivariate imaging. Food Control 54, 250–258.Google Scholar
Kenanoglu, BB, Demir, I and Jalink, H (2013) Chlorophyll fluorescence sorting method to improve quality of Capsicum pepper seed lots produced from different maturity fruits. HortScience 48, 965–968.Google Scholar
Kimuli, D, Wang, W, Lawrence, KC, Yoon, S.-C., Ni, X and Heitschmidt, GW (2018) Utilisation of visible/near-infrared hyperspectral images to classify aflatoxin B1 contaminated maize kernels. Biosystems Engineering 166, 150–160.Google Scholar
Leplat, J, Mangin, P, Falchetto, L, Heraud, C, Gautheron, E and Steinberg, C (2018) Visual assessment and computer-assisted image analysis of Fusarium head blight in the field to predict mycotoxin accumulation in wheat grains. European Journal of Plant Pathology 150, 1065–1081.Google Scholar
Lievens, B and Thomma, B.P.H.J. (2005) Recent developments in pathogen detection arrays: implications for fungal plant pathogens and use in practice. Phytopathology 95, 1374–1380.Google Scholar
Liu, C, Liu, W, Lu, X, Chen, W, Chen, F, Yang, J and Zheng, L (2016) Non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants using multispectral imaging and chemometric methods. Journal of Agricultural Science 154, 1–12.Google Scholar
Liu, C, Liu, W, Lu, X, Chen, W, Yang, J and Zheng, L (2014) Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods. Food Chemistry 153, 87–93.Google Scholar
Lleó, L, Barreiro, P, Ruiz-Altisent, M and Herrero, A (2009) Multispectral images of peach related to firmness and maturity at harvest. Journal of Food Engineering 93, 229–235.Google Scholar
Ma, F, Wang, J, Liu, C, Lu, X, Chen, W, Chen, C, Yang, J and Zheng, L (2015) Discrimination of kernel quality characteristics for sunflower seeds based on multispectral imaging approach. Food Analytical Methods 8, 1629–1636.Google Scholar
Mathur, SB and Kongsdal, O (2003) Common Laboratory Seed Health Testing Methods for Detecting Fungi. Bassendorf: International Seed Testing Association.Google Scholar
Nicolaisen, M, Justesen, AF, Knorr, K, Wang, J and Pinnschmidt, HO (2014) Fungal communities in wheat grain show significant co-existence patterns among species. Fungal Ecology 11, 145–153.Google Scholar
Olesen, MH, Nikneshan, P, Shrestha, S, Tadayyon, A, Deleuran, LC, Boelt, B and Gislum, R (2015) Viability prediction of Ricinus cummunis L. seeds using multispectral imaging. Sensors 15, 4592–4604.Google Scholar
Olesen, MH, Carstensen, JM and Boelt, B (2011) Multispectral imaging as a potential tool for seed health testing of spinach (Spinacia oleracea L.). Seed Science and Technology 39, 140–150.Google Scholar
Qin, J, Chao, K, Kim, MS, Lu, R and Burks, TF (2013) Hyperspectral and multispectral imaging for evaluating food safety and quality. Journal of Food Engineering 118, 157–171.Google Scholar
Rahman, A and Cho, B.-K. (2016) Assessment of seed quality using non-destructive measurement techniques: a review. Seed Science Research 26, 285–305.Google Scholar
Rajkumar, P, Wang, N, Elmasry, G, Raghavan, GSV and Gariepy, Y (2012) Studies on banana fruit quality and maturity stages using hyperspectral imaging. Journal of Food Engineering 108, 194–200.Google Scholar
Sendin, K, Manley, M and Williams, PJ (2018) Classification of white maize defects with multispectral imaging. Food Chemistry 243, 311–318.Google Scholar
Shetty, N, Min, T.-G., Gislum, R, Olesen, MH and Boelt, B (2011) Optimal sample size for predicting viability of cabbage and radish seeds based on near infrared spectra of single seeds. Journal of Near Infrared Spectroscopy 19, 451–461.Google Scholar
Shrestha, S, Deleuran, LC, Olesen, MH and Gislum, R (2015) Use of multispectral imaging in varietal identification of tomato. Sensors 15, 4496–4512.Google Scholar
Shrestha, S, Knapič, M, Žibrat, U, Deleuran, LC and Gislum, R (2016) Single seed near-infrared hyperspectral imaging in determining tomato (Solanum lycopersicum L.) seed quality in association with multivariate data analysis. Sensors and Actuators B: Chemical 237, 1027–1034.Google Scholar
Shrestha, S, Topbjerg, HB, Ytting, NK, Skovgaard, H and Boelt, B (2018) Detection of live larvae in cocoons of Bathyplectes curculionis (Hymenoptera: Ichneumonidae) using visible/near-infrared multispectral imaging. Pest Management Science, doi: 10.1002/ps.4915Google Scholar
Silva, VN, Cicero, SM and Bennett, M (2013) Associations between X-ray visualised internal tomato seed morphology and germination. Seed Science and Technology 41, 225–234.Google Scholar
Śliwińska, E, Jing, HC, Job, C, Job, D, Bergervoet, JHW, Bino, RJ and Groot, SPC (1999) Effect of harvest time and soaking treatment on cell cycle activity in sugarbeet seeds. Seed Science Research 9, 91–99.Google Scholar
Snyder, FW (1971) Relation of sugarbeet germination to maturity and fruit moisture at harvest. Journal of the American Society of Sugar Beet Technologists 16, 541–551.Google Scholar
TeKrony, DM (1969) Seed development and germination of monogerm sugar beets (Beta vulgaris L.) as affected by maturity. Available at: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/gh93h188s (accessed 24 May 2018).Google Scholar
TeKrony, DM and Hardin, EE (1969) The problem of underdeveloped seeds occurring in monogerm sugarbeets. Journal of the American Society of Sugar Beet Technologists 15, 625–639.Google Scholar
United States Department of Agriculture (USDA) (2017) Record Global Production Spurs Record Consumption Sugar Overview. Available at: apps.fas.usda.gov/psdonline/circulars/sugar.pdf (accessed 24 May 2018).Google Scholar
van der Burg, WJ, Aartse, JW, van Zwol, RA, Jalink, H and Bino, RJ (1994) Predicting tomato seedling morphology by X-ray analysis of seeds. Journal of the American Society of Horticultural Sciences 119, 258–263.Google Scholar
Vrešak, M, Olesen, MH, Gislum, R, Bavec, F and Jørgensen, JR (2016) The use of image-spectroscopy technology as a diagnostic method for seed health testing and variety identification. PLoS ONE 11, e0152011. https://doi.org/10.1371/journal.pone.0152011.Google Scholar
Xing, J, Symons, S, Shahin, M and Hatcher, D (2010) Detection of sprout damage in Canada Western Red Spring wheat with multiple wavebands using visible/nearinfrared hyperspectral imaging. Biosystems Engineering 106, 188–194.Google Scholar