Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: A review

Applied Spectroscopy Reviews

Volumn 48, Issue 2, 2013, Pages 142-159

  Dale, Laura M ^a,b   Thewis, André ^a   Boudry, Christelle ^a   Rotar, Ioan ^b   Dardenne, Pierre ^c   Baeten, Vincent ^c   Pierna, Juan A Fernández ^c  

^a UNIVERSITY OF LIÈGE   (Belgium)

^b UNIVERSITY OF AGRICULTURAL SCIENCES AND VETERINARY MEDICINE   (Romania)

^c WALLOON AGRICULTURAL RESEARCH CENTRE   (Belgium)

Author keywords

agriculture; Agro food industry; Airborne system; Ground based HSI; NIR spectroscopy; NIR HSI; Satellite system

Indexed keywords

AIRBORNE SYSTEMS; GROUND BASED; NIR SPECTROSCOPY; NIR-HSI; SATELLITE SYSTEM;

AGRICULTURE; FOOD PRODUCTS; IMAGING TECHNIQUES; NEAR INFRARED SPECTROSCOPY; VEGETATION; YIELD STRESS;

INFRARED DEVICES;

EID: 84872065271     PISSN: 05704928     EISSN: 1520569X     Source Type: Journal    
DOI: 10.1080/05704928.2012.705800     Document Type: Article

References (89)

1. Hart, J., Norris, K., and Golumbie, C. (1962) Determination of the moisture content of seeds by near-infrared spectroscopy. Cereal Chem., 39:94-99.
2. Murray, I. (1986) Near infrared reflectance analysis in forages. In Recent Advances in Animal Nutrition, Haresign, W. and Cole, D.J., Eds. Butterworth: London, pp. 141-156.
3. Biston, R., Dardenne, P., and Demarquilly, C. (1989) Determination of forage in vivo digestibility by NIRS. In Desroches, R., ed. Proceedings of the XVI International Grassland Congress, Association Française pour la Production Fourragere Versailles: Nice, France, 4-11 October, 1989, pp. 895-896.
4. Dardenne, P., Agneessens, R., and Biston, R. (1992) La spectrométrie dans le proche infrarouge, outil analytique de gestion de la qualite des produits agricoles et de l'environnement. Bull. des Rech. Agron. Gembloux, 27:39-51.
5. Vidican, R.M., Rotar, I., and Sima, N.F. (2000) Tehnica NIRS (near infrared reflectance spectroscopy) şi aplicaţiile sale ̂in analiza calitǎ;ţii furajelor. Simpo. Agricultura şi Alimentaţia, USAMV Cluj, Romania:187-191.
6. Decruyenaere, V., Lecomte, P., Demarquilly, C., Aufrere, J., Dardenne, P., Stilmant, D., and Buldgen, A. (2009) Evaluation of green forage intake and digestibility in ruminants using near infrared reflectance spectroscopy (NIRS): Developing a global calibration. Animal Feed Science and Technology, 148 (2-4):138-156.
7. Rotar, I., Dale, L.M., Vidican, R.M., Mogos, A., and Ceclan, O.A. (2009). Research on protein content and toral nitrogen of maize cob and strains by FT-NIR spectrometry. Bull. UASVM Cluj, 66 (1):465-467.
8. Chen, Y.R., Chao, K., and Kim, M.S. (2002) Machine vision technology for agricultural applications. Comput. Electron. Agr., 36:173-191. (Pubitemid 35409381)
9. Salguero-Chaparro, L., Baeten, V., Abbas, O., and Peña- Rodríguez, F. (2012) On-line analysis of intact olive fruits by VIS-NIR spectroscopy: Optimisation of the acquisition parameters, J. Food Eng, 112 (3):152-157. doi: 10.1016/j.jfoodeng.2012.03.034
10. Fernández-Ibanez, V., Soldado, A.,Martinez-Fernández, A., and de la Roza-Delgado, B. (2009) Application of near infrared spectroscopy for rapid detection of aflatoxin B1 inmaize and barley as analytical quality assessment. Food Chem., 113:629-634.
11. Santos, C., Fraga, M.E., Kozakiewicz, Z., and Lima, N. (2010) Fourier transform infrared as a powerful technique for the identification and characterization of filamentous fungi and yeasts. Res. Microbiol., 161:168-175.
12. Pei, X., Tandon, A., Alldrick, A., Giorgi, L., Huang, W., and Yang, R. (2011) The China melamine milk scandal and its implications for food safety regulation. Food Pol., 36:412-420.
13. Yang, Z., Hana, L., Fernández Pierna, J.A., Dardenne, P., and Baeten, V. (2011) Review: The potential of near infrared microscopy to detect, identify and quantify processed animal by-products. J. Near Infrared Spectros., 19 (4):211-231.
14. ElMasry, G. and Sun, D.W. (2010) Principles of hyperspectral imaging technology. In Hyperspectral Imaging for Food Quality Analysis and Control, Sun, D.-W., ed. Academic Press: San Diego, pp. 3-43.
15. Burger, J. and Geladi, P. (2006) Hyperspectral NIR imaging for calibration and prediction: A comparison between image and spectrometer data for studying organic and biological samples. Analyst, 131:1152-1160. (Pubitemid 44465546)
16. Shaw, G. and Manolakis, D. (2002) Signal processing for hyperspectral image exploitation. IEEE Signal Process. Mag., 19 (1):12-16. (Pubitemid 34237204)
17. Shippert, P. (2003) Introduction to hyperspectral image analysis. Space Comm., 3: p. 13.
18. Clark, R.N. and Swayze, G.A. (1995) Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice and snow, and other materials: The USGS tricorder algorithm. Paper presented at the Fifth Annual JPL Airborne Earth ScienceWorkshop, In Green, R.O., ed. Summaries of the Fifth Annual JPL Airborne Earth Science Workshop, Pasadena, CA, USA, 23-26 January, JPL Publication, 95(1):39-40.
19. Ben-Dor, E., Patin, K., Banin, A., and Karnieli, A. (2002) Mapping of several soil properties using DAIS-7915 hyperspectral scanner data. A case study over clayey soils in Israel. Int. J. Rem. Sens., 23 (6):1043-1062. (Pubitemid 34244118)
20. Tatzer, P., Wolf, M., and Panner, T. (2005) Industrial application for inline material sorting using hyperspectral imaging in the NIR range. R. Time Imag., 11:99-107. (Pubitemid 40819850)
21. Fischer, C. and Kakoulli, I. (2006) Multispectral and hyperspectral imaging technologies in conservation: Current research and potential applications. Rev. Conservat., 7:3-16.
22. Rapantzikos, K. and Balas, C. (2005) Hyperspectral imaging: Potential in non-destructive analysis of palimpsests. IEEE ICIP 2005, Genova, Italy, 11-14 September 2005, 2:618-621. (Pubitemid 44539015)
23. Kellicut, D.C., Weiswasser, J.M., Arora, S., Freeman, J.E., Lew, R.A., Shuman, C., Mansfield, J.R., and Sidawy, A.N. (2004) Emerging technology: Hyperspectral imaging. Perspect. Vasc. Surg., 16:53-57.
24. Harvey, A.R., Lawlor, J., McNaught, A.I., and Fletcher-Holmes, D.W. (2002) Hyperspectral imaging for the detection of retinal disease. Proc. SPIE, 4816:325-335.
25. Levenson, R.M., Wachman, E.S., Niu, W., and Farkas, D.L. (1998) Spectral imaging in biomedicine: A selective overview. Proc. SPIE, 3438:300-312. (Pubitemid 35005801)
26. Bartick, E.G., Schwartz-Perlman, R., Bhargava, R., Schaeberle,M., Fernandez, D., and Levin, I. (2002) Spectrochemical analysis and hyperspectral imaging of latent fingerprints. Paper presented at the 16th IAFSMeeting, Montpellier, France, 2-7 September 2002, Monduzzi Editore, Bologne, Italy:61-64.
27. Pan, Z., Healey, G., Prasad, M., and Tromberg, B. (2003) Face recognition in hyperspectral images. IEEE Trans. Pattern Anal. Mach. Intell., 25 (12):1552-1560.
28. Siddiqi, A.M., Li, H., Faruque, F., Williams, W., Lai, K., Hughson, M., Bigler, S., Beach, J., and Johnson,W. (2008) Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells. Cancer, 114:13-21. (Pubitemid 351304608)
29. Roggo,Y., Edmond, A.,Chalus, P., and Ulmschneider,M. (2005) Infrared hyperspectral imaging for qualitative analysis of pharmaceutical solid forms. Anal. Chim. Acta, 535:79-87. (Pubitemid 40410499)
30. Gendrin, C., Roggo, Y., and Collet, C., (2007) Content uniformity of pharmaceutical solid dosage forms by near infrared hyperspectral imaging: A feasibility study. Talanta, 73 (4):733-741. (Pubitemid 47379768)
31. Vermeulen, P., Fernández Pierna, J.A., Burger, J., Tossens, A., Dardenne, P., and Baeten, V. (2010) NIR hyperspectral imaging and chemometrics as a lab tool for the quality control of agricultural products. Poster session presented at Chemometrics and Analytical Chemistry (CAC 2010), Antwerp, Belgium, 18-21 October, 2010.
32. Fernández Pierna, J.A., Baeten, V., Dubois, J., Burger, J., Lewis, E.N., and Dardenne, P. (2009) NIR imaging - Theory and applications. In Comprehensive Chemometrics, Vol. 4, Brown, S., Tauler, R., and Walczak, B., Eds. Elsevier: Oxford, pp. 173-196.
33. Vigneau, N., Ecarnot, M., Rabatel, G., and Roumet, P. (2011) Potential of field hyperspectral imaging as a nondestructive method to assess leaf nitrogen content in wheat. Field Crop. Res., 122:25-31.
34. Piqueras, S., Burger, J., Tauler, R., and de Juan, A. (2012) Relevant aspects of quantification and sample heterogeneity in hyperspectral image resolution. Chemometr. Intell. Lab. Syst., 117:169-182.
35. Amigo, J.M., Cruz, J., Bautista, M., Maspoch, S., Coello, J., and Blanco, M. (2008) Study of pharmaceutical samples by NIR chemical image and multivariate analysis. Trends Anal. Chem., 27 (8):696-713.
36. Siripatrawan, U., Makino, Y., Kawagoe, Y., and Oshita, S. (2011) Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging. Talanta, 85:276-281.
37. Fernández Pierna, J.A., Baeten, V., and Dardenne, P. (2006) Screening of compound forages using NIR hyperspectral data. Chemometr. Intell. Lab. Syst., 84:114-118. (Pubitemid 44708401)
38. Chang, C.I. (2000) An information theoretic-based approach to spectral variability, similarity and discriminability for hyperspectral image analysis. IEEE Trans. Inform. Theory, 46 (5):1927-1932.
39. Gowen, A.A., O'Donnell, C.P., Cullen, P.J., Downey, G., and Frias, J.M. (2007) Hyperspectral imaging - An emerging process analytical tool for food quality and safety control. Trends Food Sci. Tech., 18:590-598. (Pubitemid 350017801)
40. Gómez-Sanchis, J., Martín-Guerrero, J.D., Soria-Olivas, E., Martínez-Sober, M., Magdalena- Benedito, R., and Blasco, J. (2012) Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques. Expert Syst. Appl., 39:780-785.
41. López-Alonso, J.M. and Alda, J. (2002) Bad pixel identification by means of principal components analysis. Opt. Eng., 41:2152-2157. (Pubitemid 35298620)
42. van der Meer, F.D., van derWerff, H.M.A., van Ruitenbeek, F.J.A., Hecker, C.A., Bakker,W.H., Noomen, M.F., van der Meijde, M., Carranza, E.J.M., Boudewijn de Smeth, J., and Woldai, T. (2012) Multi- and hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation, 14 (1):112-128.
43. Landgrebe, D. (2002) Hyperspectral Image data analysis. IEEE Signal Process. Mag., 19:17-28. (Pubitemid 34237205)
44. Johnson, B., Joseph, R., Nischan, M., Newbury, A., Kerekes, J., Barclay, H., Willard, B., and Zayhowski, J. (1999) A compact, active hyperspectral imaging system for the detection of concealed targets. Proc. SPIE, 3710:144-153.
45. Broge, N.H. and Leblanc, E. (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Rem. Sens. Environ. 76 (2000):156-172. (Pubitemid 32442453)
46. El-Shikha, D.,Waller, P., Hunsaker, D., Clarke, T., and Barnes, E. (2007) Ground-based remote sensing for assessing water and nitrogen status of broccoli. Agr.WaterManag., 92 (3):183-193. (Pubitemid 47285578)
47. Merton, R.N. (1999) Multi-temporal analysis of community scale vegetation stress with imaging spectroscopy. Doctoral Thesis, Department of Geography, University of Auckland, New Zealand.
48. Zhang, M., Qin, Z., Liu, X., and Ustin, S.L. (2003) Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 4:295-310. (Pubitemid 38242216)
49. Parker Williams, A. and Hunt, E.R., Jr. (2002) Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Rem. Sens. Environ. 82 (2-3):446-456.
50. Fitzgerald, J.G. (2004) Notes and unique phenomena precision agriculture. Portable hyperspectral tunable imaging system (PHyTIS) for precision agriculture. Agron. J., 96:311-315.
51. Pengra, B.W., Johnston, C.A., and Loveland, T.R. (2007) Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor. Rem. Sens. Environ., 108:74-81. (Pubitemid 46578133)
52. Andrew, M.E. and Ustin, S.L. (2008) The role of environmental context in mapping invasive plants with hyperspectral image data. Rem. Sens. Environ., 112 (12):4301-4317.
53. Fiorani, F., Rascher, U., Jahnke, S., and Schurr, U. (2011) Imaging plants dynamics in heterogenic environments. Curr. Opin. Biotechnol., 23:1-9.
54. Yang, C., Everitt, J.H., and Davis, M.R. (2003) A CCD Camera-based hyperspectral imaging system for stationary and airborne applications. Geocarto International, 18 (2):71-80.
55. Schut, A.G.T., Van Der Heijden, A.M., Hoving, I., Stienezen, M.W., Van Evert, F.K., and Meuleman, J. (2006) Imaging spectroscopy for on-farm measurement of grassland yield and quality. Agron. J., 98:1318-1325. (Pubitemid 44477439)
56. Suzuki, Y., Okamoto, H., Takahashi, M., Kataoka, T., and Shibata, Y. (2012) Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging. Grassland Science, 58 (1):1-7.
57. Okamoto, H., Murata, T., Kataoka, T., and Hata, S.I. (2007) Plant classification for weed detection using hyperspectral imaging with wavelet analysis. Weed Biol. Manag., 7 (1):31-37. (Pubitemid 46278633)
58. Suzuki,Y., Tanaka,K., Kato,W.,Okamoto, H., Kataoka, T., Shimada, H., Sugiura, T., and Shima, E. (2008) Field mapping of chemical composition of forage using hyperspectral imaging in a grass meadow. Grassland Science, 54 (4):179-188.
59. Fernández Pierna, J.A., Michotte Renier, A., Baeten, V., and Dardenne, P. (2004) IR camera and chemometrics (SVM): The winner combination for the detection ofMBM. Paper presented at the Stratfeed Symposium, Namur, Belgium, 16-18 June, 2004.
60. Riccioli, C., Pérez-Marín, D., Guerrero-Ginel, J.E., Saeys, W., and Garrido-Varo, A. (2011) Pixel selection for near-infrared chemical imaging (NIR-CI) discrimination between fish and terrestrial animal species in animal protein by-product meals. Appl. Spectros., 65:771-781.
61. Kim, I., Kim, M.S., Chen, Y.R., and Kong, S.G. (2004) Detection of skin tumors on chicken carcasses using hyperspectral fluorescence imaging. Agr. Eng., 47 (5):1785-1792. (Pubitemid 40033658)
62. Lawrence, K.C., Windham, W.R., Park, B., and Buhr, R.J. (2003) An hyperspectral imaging system for identification of faecal and ingesta contamination on poultry carcasses. J. Near Infrared Spectros., 11:269-281. (Pubitemid 37524201)
63. Park, B., Lawrence, K.C., Windham, W.R., and Smith, D.P. (2005) Detection of cecal contaminants in visceral cavity of broiler carcasses using hyperspectral imaging. Appl. Eng. Agr., 21:627-635. (Pubitemid 41294820)
64. Wang, N. and ElMasry, G. (2010) Bruise detection of apples using hyperspectral imaging. Agr. Eng., 295-320.
65. Nagata, M., Tallada, J.G., Kobayashi, T., Cui, Y., and Gejima, Y. (2004) Predicting maturity quality parameters of strawberries using hyperspectral imaging. ASAE: St. Joseph, MI. ASAE/CSAE Paper No. 043033.
66. Nagata, M. and Tallada, J.G. (2008) Quality evaluation of strawberries. In Computer Vision Technology for Food Quality Evaluation, Sun, D.W., Ed. Academic Press: Amsterdam, pp. 265-287.
67. Cheng, X., Chen, Y.R., Tao, Y., Wang, C.Y., Kim, M.S., and Lefcourt, A.M. (2004) A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chilling damage inspection. Society, 47 (4):1313-1320. (Pubitemid 39384467)
68. Lu, R. and Peng, Y. (2006) Hyperspectral scattering for assessing peach fruit firmness. Biosyst. Eng., 93 (2):161-171. (Pubitemid 43207615)
69. Wu, J., Peng, Y., Li, Y., Wang, W., Chen, J., and Dhakal, S. (2012) Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique. J. Food Eng., 109:267-273.
70. Naganathan, G.K., Grimes, L.M., Subbiah, J., Calkins, C.R., Samal, A., andMeyer, G.E. (2008) Visible/near-infrared hyperspectral imaging for beef tenderness prediction. Comput. Electron. Agr., 65 (2):225-233.
71. Kim, C., Naganathan, G.K., Subbiah, J., Lu, R., Calkins, C.R., and Samal, A. (2008) Optical scattering in beef steak to predict tenderness using hyperspectral imaging in theVIS-NIR region. Sensing and Instrumentation for Food Quality, 2:189-196.
72. Gómez-Sanchis, J., Moltó, E., Gomez-Chova, L., Aleixos, N., Camps-Valls, G., and Juste, F. (2008) Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. J. Food Eng., 89 (1):80-86.
73. Qin J., Burks, T.F., Ritenour, M.A., and Gordon Bonn, W. (2009) Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. J. Food Eng., 93:183-191.
74. Fernández Pierna, J.A., Vermeulen, P., Amand, O., Tossens, A., Dardenne, P., and Baeten, V. (2012) NIR Hyperspectral imaging spectroscopy and chemometrics for the detection of undesirable substances in food and feed. Chemometr. Intell. Lab. Syst, 117:233-239. doi: 10.1016/j.chemolab.2012.02.004
75. Abdel-Nour, N. and Ngadi, M. (2011) Detection of omega-3 fatty acid in designer eggs using hyperspectral imaging. Int. J. Food Sci. Nutr., 62 (4):418-422.
76. Peirs, A., Scheerlinck, N., Perez, A.B., Jancsok, P., and Nicolai, B.M. (2002) Uncertainty analysis and modelling of the starch index during apple fruit maturation. Postharvest Biology and Technology, 26 (2):199-207. (Pubitemid 34979193)
77. Peirs, A., Scheerlinck, N., De Baerdemaeker, J., and Nicolai, B.M. (2003) Starch index determination of apple fruit by means of an hyperspectral near infrared reflectance imaging system. J. Near Infrared Spectros., 11 (5):379-389. (Pubitemid 38209889)
78. Menesatti, P., Zanella, A., D'Andrea, S., Costa, C., Paglia, G., and Pallottino, F. (2008) Supervised multivariate analysis of hyper-spectral NIR images to evaluate the starch index of apples. Food Bioproc. Tech., 2 (3):308-314.
79. Weinstock, A.B., Janni, J., Hagen, L., and Wright, S. (2006) Prediction of oil and oleic acid concentrations in individual corn (Zea mays L.) kernels using near-infrared reflectance hyperspectral imaging and multivariate analysis. Appl. Spectros., 60 (1):9-16. (Pubitemid 43188292)
80. Xing, J., Van Hung, P., Symons, S., Shahin, M., and Hatcher, D. (2009) Using a short wavelength infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels. Sensing and Instrumentation for Food Quality, 3:211-218.
81. Xing, J., Symons, S., Hatcher, D., and Shahin, M. (2011) Comparison of short-wavelength infrared (SWIR) hyperspectral imaging system with an FT-NIR spectrophotometer for predicting alpha-amylase activities in individual Canadian Western Red Spring (CWRS) wheat kernels. Biosyst. Eng., 108:303-310.
82. Burns, D.A. and Ciruczak, E.W. (2008) Handbook of Near-Infrared Analysis, 3rd ed. Taylor and Francis, Philadelphia.
83. Williams, P., Geladi, P., Fox, G., and Manley, M. (2009) Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Anal. Chim. Acta, 653:121-130.
84. Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U., and Herppich, W.B. (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput. Electron. Agr., 75:304-312.
85. Firrao, G., Torelli, E., Gobbi, E., Raranciuc, S., Bianchi, G., and Locci, R. (2010) Prediction of milled maize fumonisin contamination by multispectral image analysis. J. Cereal Sci., 52:327-330.
86. Del Fiore, A., Reverberi, M., Ricelli, A., Pinzari, F., Serranti, S., Fabbri, A.A., Bonifazi, G., and Fanelli, C. (2010) Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. Int. J. Food Microbiol., 144:64-71.
87. Vermeulen, P., Fernández Pierna, J.A., Sinnaeve, G., Dardenne, P., and Baeten, V. (2009) Detection of ergot bodies in cereals by NIRS and hyperspectral NIR imaging. Poster session presented at the 14th ICNIRS, 7-13 November, Bangkok, Thailand.
88. Zijlstra, C., Lund, I., Justesen, A.F., Nicolaisen, M., Kryger Jensen, P., Bianciotto, V., Posta, K., Balestrini, R., Przetakiewicz, A., Czemborf, E., and van de Zande, J. (2011) Combining novel monitoring tools and precision application technologies for integrated high-tech crop protection in the future (a discussion document). Pest Manag. Sci., 67:616-625.
89. Vermeulen, P., Fernández Pierna, J., Tossens, A., Amand, O., Dardenne, P., and Baeten, V. (2011) Identification and quantification of cyst nematode in sugar beet seeds by hyperspectral NIR imaging. Paper read at the 14th ICNIRS, Bangkok, Thailand, 7-13 November, 2009, 997-1002.