Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-15T01:29:46.817Z Has data issue: false hasContentIssue false
  • English
  • Français

Quantifying Lygus (Hemiptera: Miridae) damage in faba bean (Fabaceae) seeds using shortwave-infrared imaging

Published online by Cambridge University Press:  18 June 2019

A.M. Smith ,
B. Rivard ,
J. Feng  and
H.A. Carcamo
A.M. Smith*
Affiliation:
Agriculture and Agri-Food Canada, Science and Technology Branch, Lethbridge Research and Development Centre, 5403 1st Avenue South, Lethbridge, Alberta, T1J 4B1, Canada
B. Rivard
Affiliation:
Department of Earth and Atmospheric Sciences, 2-063 Centennial Centre for Interdisciplinary Science, University of Alberta, Edmonton, Alberta, T6G 2E3, Canada
J. Feng
Affiliation:
Department of Earth and Atmospheric Sciences, 2-063 Centennial Centre for Interdisciplinary Science, University of Alberta, Edmonton, Alberta, T6G 2E3, Canada
H.A. Carcamo
Affiliation:
Agriculture and Agri-Food Canada, Science and Technology Branch, Lethbridge Research and Development Centre, 5403 1st Avenue South, Lethbridge, Alberta, T1J 4B1, Canada
*
1Corresponding author (e-mail: anne.smith@canada.ca)
Get access Rights & Permissions [Opens in a new window]

Abstract

Lygus Hahn (Hemiptera: Miridae) feeding in faba beans (Vicia faba Linnaeus (Fabaceae)) often results in a reduction in seed quality and economic losses. Traditionally, seed damage is assessed subjectively through visual examination by a trained individual, but the use of non-destructive imaging to evaluate seed quality is gaining momentum. The focus of this study was to determine the ability to quantify Lygus species damage in faba bean using shortwave-infrared imaging and two analysis techniques: (1) spectral angle mapper and (2) simple reflectance indices. Seed samples were visually assessed for damage before imaging in 242 wavebands between 980 and 2500 nm. Four spectral intervals, involving 102 wavebands, were identified as optimal for the detection of seed damage using spectral angle mapper. A strong relationship was obtained between the area of seed damage derived using spectral angle mapper and visually (R2 = 0.95). Seed damage derived by thresholding of two normalised faba bean damage indices involving reflectance at 1086 and 1313 nm and 2218 and 2342 nm also showed a strong relationship with the visual assessment (R2 = 0.92). The two image analysis techniques provided similar results. The study suggests that imaging in the shortwave-infrared wavelengths and the derivation of simple indices can effectively quantify faba bean damage by Lygus feeding.

Type
Physiology, Biochemistry, Development, and Genetics
Information
The Canadian Entomologist , Volume 151 , Issue 4 , August 2019 , pp. 442 - 455
Creative Commons
Parts of this are a work of Her Majesty the Queen in Right of Canada.
Copyright
© Entomological Society of Canada 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Subject Editor: Suzanne Blatt

References

Aenugu, H.P.R., Kumar, D.S., Srisudharson, , Parthiban, N., Ghosh, S., and Banji, D. 2011. Near infra red spectroscopy – an overview. International Journal of ChemTech Research, 3: 825–836.Google Scholar
Agriculture and Agri-Food Canada. 2005. Crop profile for dry bean in Canada [online]. http://publications.gc.ca/collections/collection_2009/agr/A118-10-4-2005E.pdf [accessed 8 April 2019].Google Scholar
Baker, F.K., Synyder, W.C., and Holland, A.H. 1946. Lygus bug injury of lima bean in California. Phytopathology, 36: 493–503.Google ScholarPubMed
Bock, C.H., Parker, P.E., Cook, A.Z., and Gottwald, T.R. 2008. Characteristics of the perception of different severity measures of citrus canker and the relationships between the various symptom types. Plant Disease, 92: 927–939.CrossRefGoogle Scholar
Bock, C.H., Poole, G.H., Parker, P.E., and Gottwald, T.R. 2010. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Sciences, 29: 59–107.CrossRefGoogle Scholar
Bushing, R.W. and Burton, V.E. 1974. Partial pest management programs on dry large lima beans in California: regulation of L. hesperus. Journal of Economic Entomology, 67: 259–261.CrossRefGoogle Scholar
Canadian Agri-Food Trade Alliance. 2017. Agri-food exports by sector – pulses. Available from http://cafta.org/agri-food-exports/cafta-exports [accessed 8 April 2019].Google Scholar
Canadian Grain Commission. 2018. Official grain grading guide [online]. Available from https://grainscanada.gc.ca/en/grain-quality/official-grain-grading-guide/official-grain-grading-guide-2018-en.pdf [accessed 8 April 2019].Google Scholar
Chelladurai, V., Karuppiah, K., Jayas, D.S., Fields, P.G., and White, N.D.G. 2014. Detection of Callosobruchus maculatus (F.) infestation in soybean using soft x-ray and NIR hyperspectral imaging techniques. Journal of Stored Products Research, 57: 43–48.CrossRefGoogle Scholar
Cohen, A.C. and Wheeler, A.G. 1998. Role of saliva in the highly destructive fourlined plant bug (Hemiptera: Miridae: Mirinae). Annals of the Entomological Society of America, 91: 94–100.CrossRefGoogle Scholar
Curran, P.J. 1989. Remote sensing of foliar chemistry. Remote Sensing of Environment, 30: 271–278.CrossRefGoogle Scholar
Delwiche, S.R., Souza, E.J., and Kim, M.S. 2013. Limitations of single kernel near-infrared hyperspectral imaging of soft wheat for milling quality. Biosystems Engineering, 115: 260–273.CrossRefGoogle Scholar
Dumont, J., Hirvonen, T., Heikkinen, V., Mistretta, M., Granlund, L., Himanen, K., et al. 2015. Thermal and hyperspectral imaging for Norway spruce (Picea abies) seeds screening. Computers and Electronics in Agriculture, 116: 118–124.CrossRefGoogle Scholar
Elvidge, C.D. 1990. Visible and near infrared reflectance characteristics of dry plant materials. International Journal of Remote Sensing, 11: 1775–1795.CrossRefGoogle Scholar
Hori, K. 1976. Physiological changes in host and insect. In Lygus bug: host plant interactions. Edited by Scott, D.R. and O’Keeffe, L.E.. University Press of Idaho, Moscow, Idaho, United States of America. Pp. 19–25.Google Scholar
Huang, M., Wang, Q.G., Zhu, Q.B., Qin, J.W., and Huang, G. 2015. Review of seed quality and safety tests using optical sensing technologies. Seed Science and Technology, 43: 337–366.CrossRefGoogle Scholar
Jones, J. 1999. Lygus bugs in canola [online]. Agdex 622–20. Alberta Agriculture, Food and Rural Development, Edmonton, Alberta, Canada. Available from https://open.alberta.ca/dataset/622-20 [accessed 7 April 2019].Google Scholar
Kaliramesh, S., Chelladurai, V., Jayas, D.S., Alagusundaram, K., White, N.D.G., and Fields, P.G. 2013. Detection of infestation by Callosobruchus maculatus in mung bean using near-infrared hyperspectral imaging. Journal of Stored Products Research, 52: 107–111.CrossRefGoogle Scholar
Khattat, A.R. and Stewart, R.K. 1975. Damage by tarnished plant bug to flowers and setting pods of green beans. Journal of Economic Entomology, 68: 633–635.CrossRefGoogle Scholar
Lin, L.I.K. 1992. Assay validation using the concordance correlation coefficient. Biometrics, 48: 599–604.CrossRefGoogle Scholar
Liu, D., Ning, X., Li, Z., Yang, D., Li, H., and Gao, L. 2015. Discriminating and elimination of damaged soybean seeds based on image characteristics. Journal of Stored Products Research, 60: 67–74.CrossRefGoogle Scholar
Mahajan, S., Das, A., and Sardana, H.K. 2015. Image acquisition techniques for assessment of legume quality. Trends in Food Science & Technology, 42: 116–133.CrossRefGoogle Scholar
Paz Celorio-Mancera, M., Allen, M.L., Powell, A.L., Ahmadi, H., Salemi, M.R., Phinney, B.S., et al. 2008. Polygalacturonase causes Lygus-like damage on plants: cloning and identification of western tarnished plant bug (Lygus hesperus) polygalacturonases secreted during feeding. Arthropod-Plant Interactions, 2: 215–225.CrossRefGoogle Scholar
Polesello, A., Giangiancomo, R., Forni, E., and Braga, F. 1990. The use of NIR spectrophotometry to estimate the pectic substances in fruit and fruit products. Carbohydrate Polymers, 12: 27–38.CrossRefGoogle 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.CrossRefGoogle Scholar
Ridgway, C. and Chambers, J. 1998. Detection of insects inside wheat kernels by NIR imaging. Journal of Near Infrared Spectrospcopy, 6: 115–119.CrossRefGoogle Scholar
Ridgway, R., Cowe, I.A., and Chambers, J. 1999. Detection of grain weevils inside single wheat kernels by a very near infrared two-wavelength model. Journal of Near Infrared Spectrospcopy, 7: 213–221.CrossRefGoogle Scholar
Rogge, D.M., Rivard, B., Zhang, J., Sanchez, A., Harris, J., and Feng, J. 2007. Integration of spatial-spectral information for the improved extraction of endmembers. Remote Sensing of Environment, 110: 287–303.CrossRefGoogle Scholar
Saccon, F.A.M., Parcey, D., Paliwal, J., and Sherif, S.S. 2017. Assessment of Fusarium and deoxynivalenol using optical methods. Food and Bioprocess Technology, 10: 34–50.CrossRefGoogle Scholar
Serranti, S., Cesare, D., and Bonifazi, G. 2013. The development of a hyperspectral imaging method for the detection of Fusarium-damaged, yellow berry and vitreous Italian durum wheat kernels. Biosystems Engineering, 115: 20–30.CrossRefGoogle Scholar
Shackel, K.A., Celorio-Mancera, M.D.L.P., Ahmadi, H., Greve, L.C., Teuber, L.R., Backus, E.A., and Labavitch, J.M. 2005. Micro-injection of Lygus salivary gland proteins to simulate feeding damage in alfalfa and cotton flowers. Archives of Insect Biochemistry and Physiology, 58: 69–83.CrossRefGoogle ScholarPubMed
Shahin, M.A., Hatcher, D.W., and Symons, S.J. 2012. Development of multispectral imaging systems for quality evaluation of cereal grains and grain products. In Computer vision technology in the food and beverage industries. Edited by Sun, D.-W.. Woodhead Publishing, Philadelphia, Pennsylvania, United States of America. Pp. 451–482.CrossRefGoogle Scholar
Shatadal, P. and Tan, J. 2003. Identifying damaged soybeans by color image analysis. Applied Engineering in Agriculture, 19: 65–69.CrossRefGoogle Scholar
Singh, C.B., Jayas, D.S., Paliwal, J., and White, N.D.G. 2009a. Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging. Journal of Stored Products Research, 45: 151–158.CrossRefGoogle Scholar
Singh, C.B., Jayas, D.S., Paliwal, J., and White, N.D.G. 2009b. Detection of sprouted and midge-damaged wheat kernels using near-infrared hyperspectral imaging. Cereal Chemistry, 86: 256–260.CrossRefGoogle Scholar
Singh, C.B., Jayas, D.S., Paliwal, J., and White, N.D.G. 2010a. Detection of midge-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Biosystems Engineering, 105: 380–387.CrossRefGoogle Scholar
Singh, C.B., Jayas, D.S., Paliwal, J., and White, N.D.G. 2010b. Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Computers and Electronics in Agriculture, 73: 118–125.CrossRefGoogle Scholar
Singh, C.B., Jayas, D.S., Paliwal, J., and White, N.D.G. 2012. Fungal damage detection in wheat using short-wave near-infrared hyperspectral and digital colour imaging. International Journal of Food Properties, 15: 11–24.CrossRefGoogle Scholar
Tian, M., Feng, J., Rivard, B., and Zhao, C. 2016. A method to compute the n-dimensional solid spectral angle between vectors and its use for band selection in hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 50: 141–149.CrossRefGoogle Scholar
Young, O.P. 1986. Host plants of the tarnished plant bug, Lygus lineolaris (Heteroptera: Miridae). Annals Entomological Society of America, 79: 747–762.CrossRefGoogle Scholar