F. Faostat, Agriculture Organization of the United Nations. Statistical Database, vol.FAOSTAT, 2016.

R. Gislum, L. C. Deleuran, M. H. Olesen, S. Tveden-nyborg, and B. Boelt, Single seed NIR as a fast method to predict germination ability in Pak Choi, Nir News, vol.23, pp.6-7, 2012.

, International Rules for Seed Testing, 2017.

A. Rahman and B. Cho, Assessment of seed quality using non-destructive measurement techniques: A review, Seed Sci. Res, vol.26, pp.285-305, 2016.

A. Dell'aquila, Towards new computer imaging techniques applied to seed quality testing and sorting, Seed Sci. Technol, vol.35, pp.519-538, 2007.

B. Boelt, S. Shrestha, Z. Salimi, J. R. Jørgensen, M. Nicolaisen et al., Multispectral imaging-A new tool in seed quality assessment?, Seed Sci. Res, vol.28, pp.222-228, 2018.

K. J. Bradford, Population-based models describing seed dormancy behaviour: Implications for experimental design and interpretation, Plant Dormancy: Physiology, pp.313-339, 1996.

Z. Li, C. Li, Y. Gao, W. Ma, Y. Zheng et al., Identification of oil, sugar and crude fiber during tobacco (Nicotiana tabacum L.) seed development based on near infrared spectroscopy, Biomass Bioenergy, vol.111, pp.39-45, 2018.

G. Elmasry, S. Radwan, M. Elamir, and R. Elgamal, Investigating the effect of moisture content on some properties of peanut by aid of digital image analysis, Food Bioprod. Process, vol.87, pp.273-281, 2009.

J. W. Hettinger, M. De-la-pena-mattozzi, W. R. Myers, M. E. Williams, A. Reeves et al., Optical coherence microscopy. A technology for rapid, in vivo, non-destructive visualization of plants and plant cells, Plant Physiol, vol.123, pp.3-16, 2000.

H. Demirba? and I. Dursun, Determination of some physical properties of wheat grains by using image analysis, J. Agric. Sci, vol.13, pp.176-185, 2007.

B. Jaillais, E. Perrin, C. Mangavel, and D. Bertrand, Characterization of the desiccation of wheat kernels by multivariate imaging, Planta, vol.233, pp.1147-1156, 2011.
URL : https://hal.archives-ouvertes.fr/hal-02132376

S. Mahajan, A. Das, and H. K. Sardana, Image acquisition techniques for assessment of legume quality, Trends Food Sci. Technol, vol.42, pp.116-133, 2015.

V. N. Silva and S. M. Cicero, Image seedling analysis to evaluate tomato seed physiological potential, Rev. Ciência Agronômica, vol.45, pp.327-334, 2014.

S. Jia, L. Yang, D. An, Z. Liu, Y. Yan et al., Feasibility of analyzing frost-damaged and non-viable maize kernels based on near infrared spectroscopy and chemometrics, J. Cereal Sci, vol.69, pp.145-150, 2016.

M. Huang, Q. Wang, Q. Zhu, J. Qin, and G. Huang, Review of seed quality and safety tests using optical sensing technologies, Seed Sci. Technol, vol.43, pp.337-366, 2015.

L. E. Agelet, D. D. Ellis, S. Duvick, A. S. Goggi, C. R. Hurburgh et al., Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels, J. Cereal Sci, vol.55, pp.160-165, 2012.

, Sensors, vol.19, pp.1090-1118, 2019.

P. Tsakanikas, D. Pavlidis, and G. Nychas, High throughput multispectral image processing with applications in food science, PLoS ONE, vol.10, 2015.

B. Clergue, B. Amiaud, F. Pervanchon, F. O. Lasserre-joulin, and S. Plantureux, Biodiversity: Function and assessment in agricultural areas. A review, Agron. Sustain. Dev, vol.25, pp.1-15, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00886277

L. Li, Q. Zhang, and D. Huang, A review of imaging techniques for plant phenotyping, Sensors, vol.14, 2014.

E. Belin, C. M. Douarre, N. Gillard, F. Franconi, J. Rojas-varela et al., Evaluation of 3D/2D Imaging and Image Processing Techniques for the Monitoring of Seed Imbibition, J. Imaging, vol.4, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01916798

A. Dell'aquila, Application of a computer-aided image analysis system to evaluate seed germination under different environmental conditions, Ital. J. Agron, vol.8, pp.51-62, 2004.

D. Rousseau, T. Widiez, S. Tommaso, H. Rositi, J. Adrien et al., Fast virtual histology using X-ray in-line phase tomography: Application to the 3D anatomy of maize developing seeds, Plant Methods, vol.11, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01538205

J. Damez and S. Clerjon, Meat quality assessment using biophysical methods related to meat structure, Meat Sci, vol.80, pp.132-149, 2008.

D. Nyström, Colorimetric and Multispectral Image Acquisition Using Model-Based and Empirical Device Characterization, Image Analysis: Lecture Notes in Computer Science, Proceedings of the 15th Scandinavian Conference, SCIA, pp.10-14, 2007.

B. K. Ersbøll and K. S. Pedersen, , vol.4522, pp.798-807, 2007.

L. E. Agelet, C. R. Hurburgh, and . Jr, Limitations and current applications of Near Infrared Spectroscopy for single seed analysis, Talanta, vol.121, pp.288-299, 2014.

B. G. Osborne, T. Fearn, and P. H. Hindle, Practical NIR Spectroscopy with Applications in Food and Beverage Analysis

, Longman Scientific and Technical, 1993.

J. Panford and J. Deman, Determination of oil content of seeds by NIR: Influence of fatty acid composition on wavelength selection, J. Am. Oil Chem. Soc, vol.67, pp.473-482, 1990.

A. Fassio, E. Restaino, and D. Cozzolino, Determination of oil content in whole corn (Zea mays L.) seeds by means of near infrared reflectance spectroscopy, Comput. Electron. Agric, vol.110, pp.171-175, 2015.

L. Velasco and C. Möllers, Nondestructive assessment of protein content in single seeds of rapeseed (Brassica napus L.) by near-infrared reflectance spectroscopy, Euphytica, vol.123, pp.89-93, 2002.

A. Fassio and D. Cozzolino, Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy, Ind. Crop. Prod, vol.20, pp.321-329, 2004.

G. Spielbauer, P. Armstrong, J. W. Baier, W. B. Allen, K. Richardson et al., High-throughput near-infrared reflectance spectroscopy for predicting quantitative and qualitative composition phenotypes of individual maize kernels, Cereal Chem, vol.86, pp.556-564, 2009.

T. Sato, Nondestructive measurements of lipid content and fatty acid composition in rapeseeds (Brassica napus L.) by near infrared spectroscopy, Plant Prod. Sci, vol.11, pp.146-150, 2008.

M. H. Olesen, N. Shetty, R. Gislum, and B. Boelt, Classification of viable and non-viable spinach (Spinacia oleracea L.) seeds by single seed near infrared spectroscopy and extended canonical variates analysis, J. Near Infrared Spectrosc, vol.19, pp.171-180, 2011.

N. Shetty, T. Min, R. Gislum, M. H. Olesen, and B. Boelt, Optimal sample size for predicting viability of cabbage and radish seeds based on near infrared spectra of single seeds, J. Near Infrared Spectrosc, vol.19, pp.451-461, 2011.

S. Shrestha, L. C. Deleuran, and R. Gislum, Separation of viable and non-viable tomato (Solanum lycopersicum L.) seeds using single seed near-infrared spectroscopy, Comput. Electron. Agric, vol.142, pp.348-355, 2017.

K. Wang and C. Wang, The NIR spectra based variety discrimination for single soybean seed, Spectrosc. Spectr. Anal, vol.30, pp.3217-3221, 2010.

J. Tallada, D. Wicklow, T. Pearson, and P. Armstrong, Detection of fungus-infected corn kernels using near-infrared reflectance spectroscopy and color imaging, Trans. ASABE, vol.54, pp.1151-1158, 2011.

G. Elmasry and S. Nakauchi, Image analysis operations applied to hyperspectral images for non-invasive sensing of food quality-A comprehensive review, Biosyst. Eng, vol.142, pp.53-82, 2016.

E. Z. Panagou, O. Papadopoulou, J. M. Carstensen, and G. E. Nychas, Potential of multispectral imaging technology for rapid and non-destructive determination of the microbiological quality of beef filets during aerobic storage, Int. J. Food Microbiol, vol.174, pp.1-11, 2014.

L. M. Kandpal, S. Lohumi, M. S. Kim, J. Kang, and B. Cho, Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds, Sens. Actuators B Chem, vol.229, pp.534-544, 2016.

D. A. Quattrochi and J. C. Luvall, Thermal Remote Sensing in Land Surface Processing, 2004.

K. Sendin, M. Manley, and P. J. Williams, Classification of white maize defects with multispectral imaging, Food Chem, vol.243, pp.311-318, 2018.

A. Dell'aquila, Digital imaging information technology applied to seed germination testing. A review, Agron. Sustain. Dev, vol.29, pp.213-221, 2009.

P. Vithu and J. Moses, Machine vision system for food grain quality evaluation: A review, Trends Food Sci. Technol, vol.56, pp.13-20, 2016.

P. M. Mehl, Y. Chen, M. S. Kim, and D. E. Chan, Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations, J. Food Eng, vol.61, pp.67-81, 2004.

A. Aït-kaddour, S. Jacquot, D. Micol, and A. Listrat, Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis, Int. J. Food Prop, vol.20, pp.1391-1403, 2017.

G. Elmasry, D. F. Barbin, D. W. Sun, and P. Allen, Meat quality evaluation by hyperspectral imaging technique: An overview, Crit. Rev. Food Sci. Nutr, vol.52, pp.689-711, 2012.

G. Elmasry and D. Sun, Meat Quality Assessment Using a Hyperspectral Imaging System, Hyperspectral Imaging for Food Quality Analysis and Control, pp.175-240, 2010.

X. Zhang, F. Liu, Y. He, and X. Li, Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds, Sensors, vol.12, pp.17234-17246, 2012.

L. M. Kandpal, S. Lee, M. S. Kim, H. Bae, and B. Cho, Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels, Food Control, vol.51, pp.171-176, 2015.

E. Bauriegel, A. Giebel, M. Geyer, U. Schmidt, and W. B. Herppich, Early detection of Fusarium infection in wheat using hyper-spectral imaging, Comput. Electron. Agric, vol.75, pp.304-312, 2011.

B. A. Weinstock, J. Janni, L. Hagen, and S. Wright, Prediction of oil and oleic acid concentrations in individual corn (Zea mays L.) kernels using near-infrared reflectance hyperspectral imaging and multivariate analysis, Appl. Spectrosc, vol.60, pp.9-16, 2006.

C. Wakholi, L. M. Kandpal, H. Lee, H. Bae, E. Park et al., Rapid assessment of corn seed viability using short wave infrared line-scan hyperspectral imaging and chemometrics, Sens. Actuators B Chem, vol.255, pp.498-507, 2018.

A. Ambrose, L. M. Kandpal, M. S. Kim, W. Lee, and B. Cho, High speed measurement of corn seed viability using hyperspectral imaging, Infrared Phys. Technol, vol.75, pp.173-179, 2016.

M. N. Islam, G. Nielsen, S. Staerke, A. Kjaer, B. Jørgensen et al., Novel non-destructive quality assessment techniques of onion bulbs: A comparative study, J. Food Sci. Technol, vol.55, pp.3314-3324, 2018.

G. Elmasry, M. Kamruzzaman, D. W. Sun, and P. Allen, Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: A review, Crit. Rev. Food Sci. Nutr, vol.52, pp.999-1023, 2012.

M. Kamruzzaman, G. Elmasry, and S. Nakauchi, On-line screening of meat and poultry product quality and safety using hyperspectral imaging, High Throughput Screening for Food Safety Assessment-Biosensor Technologies, 2015.

G. Elmasry and S. Nakauchi, Prediction of meat spectral patterns based on optical properties and concentrations of the major constituents, Food Sci. Nutr, vol.4, pp.269-283, 2015.

H. Park and K. B. Crozier, Multispectral imaging with vertical silicon nanowires, Sci. Rep, vol.19, 2013.

H. Li, J. Feng, W. Yang, L. Wang, H. Xu et al., Multi-spectral imaging using LED illuminations, Proceedings of the 2012 5th International Congress on Image and Signal Processing, pp.538-542, 2012.

T. Pearson, E. Maghirang, and F. Dowell, A multispectral sorting device for wheat kernels, Am. J. Agric. Sci. Tech, vol.2, pp.45-60, 2013.

F. Møller, R. Larsen, and J. Carstensen, Imaging Food Quality, vol.288, 2012.

M. Parmar, S. Lansel, and J. Farrell, An LED-Based Lighting System for Acquiring Multispectral Scenes; Digital Photography VIII, International Society for Optics and Photonics, p.82990, 2012.

M. A. Hansen, F. R. Hay, and J. M. Carstensen, A virtual seed file: The use of multispectral image analysis in the management of genebank seed accessions, Plant Genet. Resour, vol.14, pp.238-241, 2016.

H. Yao, Z. Hruska, R. Kincaid, R. L. Brown, D. Bhatnagar et al., Detecting maize inoculated with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imagery, Biosyst. Eng, vol.115, pp.125-135, 2013.

P. J. Cumpson, I. W. Fletcher, R. Burnett, N. Sano, A. J. Barlow et al., Multispectral optical imaging combined in situ with XPS or ToFSIMS and principal component analysis, Surf. Interface Anal, vol.48, pp.1370-1378, 2016.

A. I. Ropodi, E. Z. Panagou, and G. E. Nychas, Rapid detection of frozen-then-thawed minced beef using multispectral imaging and Fourier transform infrared spectroscopy, Meat Sci, vol.135, pp.142-147, 2018.

B. S. Dissing, B. Ersbøll, and J. Adler-nissen, New Vision Technology for Multidimensional Quality Monitoring of Food Processes, 2011.

C. Liu, W. Liu, X. Lu, F. Ma, W. Chen et al., Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit, PLoS ONE, vol.9, 2014.

M. S. Andresen, B. Dissing, and H. Løje, Quality assessment of butter cookies applying multispectral imaging, Food Sci. Nutr, vol.1, pp.315-323, 2013.

S. B. Daugaard, J. Adler-nissen, and J. M. Carstensen, New vision technology for multidimensional quality monitoring of continuous frying of meat, Food Control, vol.21, pp.626-632, 2010.

B. Jaillais, D. Bertrand, and J. Abecassis, Identification of the histological origin of durum wheat milling products by multispectral imaging and chemometrics, J. Cereal Sci, vol.55, pp.210-217, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01268051

B. Jaillais, P. Roumet, L. Pinson-gadais, and D. Bertrand, Detection of Fusarium head blight contamination in wheat kernels by multivariate imaging, Food Control, vol.54, pp.250-258, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01268465

D. Wang, M. Ram, and F. Dowell, Classification of damaged soybean seeds using near-infrared spectroscopy, Trans. ASAE, vol.45, pp.1943-1948, 2002.

C. Liu, W. Liu, X. Lu, W. Chen, J. Yang et al., Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods, Food Chem, vol.153, pp.87-93, 2014.

G. N. De-la-fuente, J. M. Carstensen, and M. A. Edberg, Lü bberstedt, T. Discrimination of haploid and diploid maize kernels via multispectral imaging, vol.136, pp.50-60, 2017.

M. H. Olesen, J. M. Carstensen, and B. Boelt, Multispectral imaging as a potential tool for seed health testing of spinach, Spinacia oleracea L.). Seed Sci. Technol, vol.39, pp.140-150, 2011.

S. Shrestha, L. C. Deleuran, and R. Gislum, Classification of different tomato seed cultivars by multispectral visible-near infrared spectroscopy and chemometrics, J. Spectr. Imaging, vol.5, pp.1-9, 2016.

A. I. Ropodi, E. Z. Panagou, and G. E. Nychas, Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat, Food Control, vol.73, pp.57-63, 2017.

M. Vre?ak, M. H. Olesen, R. Gislum, F. Bavec, and J. R. Jørgensen, The use of image-spectroscopy technology as a diagnostic method for seed health testing and variety identification, PLoS ONE, vol.11, 2016.

S. Jacquot, R. Karoui, K. Abbas, A. Lebecque, C. C. Bord et al., Potential of multispectral Imager to characterize anisotropic French PDO cheeses: A feasibility study, Int. J. Food Prop, vol.18, pp.213-230, 2015.

R. Khodabakhshian, B. Emadi, M. Khojastehpour, M. R. Golzarian, and A. Sazgarnia, Development of a multispectral imaging system for online quality assessment of pomegranate fruit, Int. J. Food Prop, vol.20, pp.107-118, 2016.

M. M. Løkke, H. F. Seefeldt, and M. Edelenbos, Freshness and sensory quality of packaged wild rocket, Postharvest Biol. Technol, vol.73, pp.99-106, 2012.

M. M. Løkke, H. F. Seefeldt, T. Skov, and M. Edelenbos, Color and textural quality of packaged wild rocket measured by multispectral imaging, Postharvest Biol. Technol, vol.75, pp.86-95, 2013.

M. Klukkert, J. X. Wu, J. Rantanen, J. M. Carstensen, T. Rades et al., Multispectral UV imaging for fast and non-destructive quality control of chemical and physical tablet attributes, Eur. J. Pharm. Sci, vol.90, pp.85-95, 2016.

D. D. Gómez, J. M. Carstensen, and B. Ersbell, Precise Multi-Spectral Dermatological Imaging, Proceedings of the Nuclear Science Symposium Conference Record, pp.3262-3266, 2004.

C. H. Trinderup, A. Dahl, K. Jensen, J. M. Carstensen, and K. Conradsen, Comparison of a multispectral vision system and a colorimeter for the assessment of meat color, Meat Sci, vol.102, pp.1-7, 2015.

F. Ma, B. Zhang, W. Wang, P. Li, X. Niu et al., Potential use of multispectral imaging technology to identify moisture content and water-holding capacity in cooked pork sausages, J. Sci. Food Agric, vol.98, pp.1832-1838, 2018.

A. Ropodi, D. Pavlidis, F. Mohareb, E. Panagou, and G. Nychas, Multispectral image analysis approach to detect adulteration of beef and pork in raw meats, Food Res. Int, vol.67, pp.12-18, 2015.

S. D. Osborne and R. B. Jordan, Method of wavelength selection for partial least squares, Analyst, vol.122, pp.1531-1537, 1997.

Q. Dai, J. Cheng, D. Sun, and X. Zeng, Advances in Feature Selection Methods for Hyperspectral Image Processing in Food Industry Applications: A Review, Crit. Rev. Food Sci. Nutr, vol.55, pp.1368-1382, 2014.

K. Dammer, B. Möller, B. Rodemann, and D. Heppner, Detection of head blight (Fusarium ssp.) in winter wheat by color and multispectral image analyses, Crop Prot, vol.30, pp.420-428, 2011.

S. Shrestha, L. Deleuran, M. Olesen, and R. Gislum, Use of multispectral imaging in varietal identification of tomato, Sensors, vol.15, pp.4496-4512, 2015.

S. Shrestha, L. C. Deleuran, and R. Gislum, Use of multispectral images and chemometrics in tomato seed studies, Proceedings of the 31st ISTA Congress, International Seed Testing Association, pp.15-21, 2016.

C. Liu, W. Liu, X. Lu, W. Chen, F. Chen et al., Non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants using multispectral imaging and chemometric methods, J. Agric. Sci, vol.154, pp.1-12, 2014.

M. Shahin, D. Hatcher, and S. Symons, Development of multispectral imaging systems for quality evaluation of cereal grains and grain products, Computer Vision Technology in the Food and Beverage Industries, pp.451-482, 2012.

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, Two-wavelength spectral imaging-based Thai rice breed identification, International Society for Optics and Photonics, p.77150, 2010.

T. Wilkes, G. Nixon, C. Bushell, A. Waltho, A. Alroichdi et al., Feasibility study for applying spectral imaging for wheat grain authenticity testing in pasta, Food Nutr. Sci, vol.7, pp.355-361, 2016.

S. Chevallier, D. Bertrand, A. Kohler, and P. Courcoux, Application of PLS-DA in multivariate image analysis, J. Chemom. A J. Chemom. Soc, vol.20, pp.221-229, 2006.

B. Novales and D. Bertrand, Histologic labeling of seeds by multivariate fluorescence imaging, Proceedings of the SPIE Conference on Precision Agriculture and Biological Quality, pp.164-171, 1998.

M. H. Olesen, P. Nikneshan, S. Shrestha, A. Tadayyon, L. C. Deleuran et al., Viability prediction of Ricinus cummunis L. seeds using multispectral imaging, Sensors, vol.15, pp.4592-4604, 2015.

, Sensors, vol.19, p.32, 2019.

N. Shetty, M. H. Olesen, R. Gislum, L. C. Deleuran, and B. Boelt, Use of partial least squares discriminant analysis on visible-near infrared multispectral image data to examine germination ability and germ length in spinach seeds, J. Chemom, vol.26, pp.462-466, 2012.

L. C. Rajjou, M. Duval, K. Gallardo, J. Catusse, J. Bally et al., Seed germination and vigor, Annu. Rev. Plant Biol, vol.63, pp.507-533, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01000608

P. Neergaard, Quarantine for Seed, Seed Pathology, pp.681-711, 1977.

L. J. Du-toit and P. Hernandez-perez, Efficacy of hot water and chlorine for eradication of Cladosporium variabile, Stemphylium botryosum, and Verticillium dahliae from spinach seed, Plant Dis, vol.89, pp.1305-1312, 2005.

M. Pasikatan and F. Dowell, Sorting systems based on optical methods for detecting and removing seeds infested internally by insects or fungi: A review, Appl. Spectrosc. Rev, vol.36, pp.399-416, 2001.

H. Kalkan, P. Beriat, Y. Yardimci, and T. Pearson, Detection of contaminated hazelnuts and ground red chili pepper flakes by multispectral imaging, Comput. Electron. Agric, vol.77, pp.28-34, 2011.

J. Santos, J. Maia, and I. Cruz, Damage to germination of seed corn caused by maize weevil (Sitophilus zeamais) and Angoumois grain moth (Sitotroga cerealella). Pesqui. Agropecuaria Bras, vol.25, pp.1687-1692, 1990.

F. Ma, J. Wang, C. Liu, X. Lu, W. Chen et al., Discrimination of kernel quality characteristics for sunflower seeds based on multispectral imaging approach, Food Anal. Methods, vol.8, pp.1629-1636, 2015.

L. Benoit, R. Benoit, É. Belin, R. Vadaine, D. Demilly et al., On the value of the Kullback-Leibler divergence for cost-effective spectral imaging of plants by optimal selection of wavebands, Mach. Vis. Appl, vol.27, pp.625-635, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01392039