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Detection of Palm Tree Pests Using Thermal Imaging: A Review

  • Ali Ahmed
  • Abdelhameed Ibrahim
  • Sherif Hussein
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 801)

Abstract

Due to the extensive stretches of date plantation and topography of traditional grooves in countries such as Saudi Arabia and Egypt, the red palm weevil (RPW) early detection is a significant challenge. The RPW is a palm borer insect that develops within the soft tissues of the trunk and crown, eventually leading to tree death. Early detection of RPW infestation is crucial because, at an early stage of infestation, palms can be treated more efficiently and saved, while the determination of treatment efficacy is hugely vital to optimize palm rescue efforts. Detection is often particularly problematic since not all palms can be accessed and inspected directly. Thermography technique can determine the thermal properties of any objects of interest, and it is a non-destructive. In thermography, the invisible radiation patterns are transformed to visible images called thermal images. Those thermal images are acquired using specific sensors that can be coupled with many available optical systems. Due to the simple operating procedure and the noticeable reductions in equipment cost of thermal imaging systems, it gains popularity in pests’ detection. This chapter discusses the state-of-the-art research concerning the detection methods for detecting infected Palm trees. The study will concentrate on the thermal imaging and its application on red palm weevil detection.

Keywords

Palms Palm pests Thermal imaging Detection methods Red palm weevil (RPW) 

References

  1. 1.
    Howard, F.W., Moore, D., Giblin-Davis, R.M., Abad, R.G.: Insects on Palms. CAB eBooks (2001)Google Scholar
  2. 2.
    Alhammadi, M.S., Glenn, E.P.: Detecting date palm trees health and vegetation greenness change on the eastern coast of the United Arab Emirates using SAVI. Int. J. Remote Sens. 29(6), 1745–1765 (2008).  https://doi.org/10.1080/01431160701395195
  3. 3.
    Murphy, S.T., Briscoe, B.R.: The red palm weevil as an alien invasive: biology and the prospects for biological control as a component of ipm. Biocontrol News Inf. 20(1), 35N–46N (1999)Google Scholar
  4. 4.
    Salama, H.S., Zaki, F.N., Abdel-Razek, A.S.: Ecological and biological studies on the red palm weevil rhynchophorus ferrugineus (olivier). Arch. Phytopathol. Plant Protect. 42(4), 392–399 (2009)CrossRefGoogle Scholar
  5. 5.
    Abraham, V.A., Koya, K.M.A., Kurian, C.: Integrated management of red palm weevil (Rhynchophorus ferrugineus F.) in coconut gardens. J. Plant. Crops 16, 159–162 (1989)Google Scholar
  6. 6.
    Faghih, A.A.: The biology of red palm weevil, Rhynchophorus ferrugineus Oliv. (Coleoptera, Curculionidae) in Saravan region (Sistan & Balouchistan province, Iran). Appl. Entomol. Phytopathol. 63(1/2), 16–18 (1996)Google Scholar
  7. 7.
    Afzan Azmi, W., Kah Wai, Y., Abu Bakar, A.: Fecundity, fertility and survival of red palm weevil (rhynchophorus ferrugineus) larvae reared on sago palm. Sains Malaysiana 44(10), 1371–1375 (2015)Google Scholar
  8. 8.
    El-Sabea, A.M.R., Faleiro, J.R., Abo-El-Saad, M.M.: The threat of red palm weevil rhynchophorus ferrugineus to date plantations of the gulf region in the middle-east: an economic perspective. Outlooks Pest Manage. 20(3), 131–134 (2009)Google Scholar
  9. 9.
    Al-Shawaf, A.M., Al-Shagag, A., Al-Bagshi, M., Al-Saroj, S., Al-Bather, S., Al-Dandan, A.M., Abdallah, A.B.: A quarantine protocol against red palm weevil rhynchophorus ferrugineus (olivier) (coleptera: Curculiondae) in date palm. J. Plant Protect. Res. 53(4), 409–415 (2013)Google Scholar
  10. 10.
    Faleiro, J.: A review of the issues and management of the red palm weevil Rhynchophorus ferrugineus (coleoptera: Rhynchophoridae) in coconut and date palm during the last one hundred years. Int. J. Trop. Insect Sci. 26(3), 135–154 (2006).  https://doi.org/10.1079/IJT2006113
  11. 11.
    Scheffrahn, Rudolf H., Robbins, William P., Busey, Philip, Nan-Yao, Su, Mueller, Rolf K.: Evaluation of a novel, hand-held, acoustic emissions detector to monitor termites (isoptera: Kalotermitidae, rhinotermitidae) in wood. J. Econ. Entomol. 86(6), 1720–1729 (1993)CrossRefGoogle Scholar
  12. 12.
    Schlyter, F.: Detection dogs recognize pheromone from spruce bark beetle and follow it source. In: ESA 60th Annual Meeting Knoxville (2012)Google Scholar
  13. 13.
    Nakash, J., Osem, Y., Kehat, M.: A suggestion to use dogs for detecting red palm weevil (rhynchophorus ferrugineus) infestation in date palms in israel. Phytoparasitica 28(2), 153–155 (2000)CrossRefGoogle Scholar
  14. 14.
    Vadivambal, R., Jayas, D.S.: Applications of thermal imaging in agriculture and food industry—a review. Food Bioprocess Technol. 4(2), 186–199, Feb 2011Google Scholar
  15. 15.
    Prakash, A.: Thermal remote sensing: concepts, issues and applications. Int. Arch. Photogram. Remote Sens. 33, 239–243 (2000)Google Scholar
  16. 16.
    Sabins Jr., Lulla, K.: Remote sensing: principles and interpretation. Geocarto Int. 2(1), 66–66 (1987)Google Scholar
  17. 17.
    Ibrahim, A., Horiuchi, T., Tominaga, S., Ella Hassanien, A.: Spectral reflectance images and applications. In: Awad, A., Hassaballah, M. (eds.) Image Feature Detectors and Descriptors. Studies in Computational Intelligence, vol. 630. Springer, Cham (2016)Google Scholar
  18. 18.
    Ibrahim, A., Tominaga, S., Horiuchi, T.: Material classification for printed circuit boards by spectral imaging system. In: Trémeau, A., Schettini, R., Tominaga, S. (eds.), Computational Color Imaging, pp. 216–225. Springer, Berlin, Heidelberg (2009)Google Scholar
  19. 19.
    Ibrahim, A., Tominaga, S., Horiuchi, S.: Unsupervised material classification of printed circuit boards using dimension-reduced spectral information. In: MVA2009 IAPR Conference on Machine Vision Applications, pp. 435–438 (2009)Google Scholar
  20. 20.
    Ibrahim, A., Tominaga, S., Horiuchi, T.: Spectral imaging method for material classification and inspection of printed circuit boards. Opt. Eng. 49, 49–49–10 (2010)Google Scholar
  21. 21.
    Ibrahim, A., Tominaga, S., Horiuchi, T.: Spectral invariant representation for spectral reflectance image. In: 2010 20th International Conference on Pattern Recognition, pp. 2776–2779, Aug 2010Google Scholar
  22. 22.
    Ibrahim, A., Tominaga, S., Horiuchi, T.: Invariant representation for spectral reflectance images and its application. EURASIP J. Image Video Process. 1(2), Jun 2011Google Scholar
  23. 23.
    Ibrahim, A., Tominaga, S., Horiuchi, T.: A spectral invariant representation of spectral reflectance. Opt. Rev. 18(2), 231–236, Mar 2011Google Scholar
  24. 24.
    Maldague, X.P.V., Jones, T.S., Kaplan, H., Marinetti, S., Prystay, M.: Chapter 2: fundamentals of infrared and thermal testing: part 1. principles of infrared and thermal testing, vol. 3. ASNT Press (2001)Google Scholar
  25. 25.
    Rogalski, A., Chrzanowski, K.: Infrared devices and techniques (revision). Metrol. Measure. Syst. 21(4), 565–618 (2014)CrossRefGoogle Scholar
  26. 26.
    Cohen, Y., Alchanatis, V., Prigojin, A., Levi, A., Soroker, V., Cohen, Y.: Use of aerial thermal imaging to estimate water status of palm trees. Prec. Agric. 13(1), 123–140 (2012)CrossRefGoogle Scholar
  27. 27.
    Alchanatis, V., Cohen, Y., Levin, N., Golomb, O., Soroker, V.: Detection of red palm weevil infected trees using thermal imaging. In: Precision Agriculture ’15, p. 322 (2015)Google Scholar
  28. 28.
    Suma, P., La Pergola, A., Cohen, Y., Cohen, Y., Alchanatis, V., Golomb,O., Goldshtein, E., Hetzroni, A., Galazan, L., Kontodimas, D., Pontikakos, C., Zorovoc, M., Soroker, V., Brandstetter, M.: Early detection and monitoring of red palm weevil: approaches and challenges. In: AFPP-Palm Pest Mediterranean Conference (2013)Google Scholar
  29. 29.
    Bokhari, U.G., Abuzuhira, R.: Diagnostic tests for redpalm weevil, Rhynchophorus ferrugineus infested datepalm trees. Arab J. Sci. Res. 10(3), 93–104 (1992)Google Scholar
  30. 30.
    Abe, F., Ohkusu, M., Kubo, T., Kawamoto, S., Sone, K., Hata, K.: Isolation of yeasts from palm tissues damaged by the red palm weevil and their possible effect on the weevil overwintering. Mycoscience 51(3), 215–223, May 2010Google Scholar
  31. 31.
    Al-doski, J., Mansor, S., Shafri, M., Zulhaidi, H.: Thermal imaging for pests detecting-a review. Int. J. Agric. Forest. Plant. 2, 10–30 (2016)Google Scholar
  32. 32.
    Ehrler, W.L.: Cotton leaf temperatures as related to soil water depletion and meteorological factors. Agro. J. 65, 404–409 (1973)CrossRefGoogle Scholar
  33. 33.
    Grant, O.M., Tronina, U., Jones, H.G., Manuela Chaves, M.: Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. J. Exp. Botany 58(4), 815–825 (2007)Google Scholar
  34. 34.
    Mller, M., Alchanatis, V., Cohen, Y., Meron, M., Tsipris, J., Naor, A., Ostrovsky, V., Sprintsin, M., Cohen, S.: Use of thermal and visible imagery for estimating crop water status of irrigated grapevine*. J. Exp. Botany 58(4), 827–838 (2007)CrossRefGoogle Scholar
  35. 35.
    Alchanatis, V., Cohen, Y., Cohen, S., Moller, M., Sprinstin, M., Meron, M., Tsipris, J., Saranga, Y., Sela, E.: Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging. Prec. Agric. 11(1), 27–41 (2010)CrossRefGoogle Scholar
  36. 36.
    Cohen, Y., Alchanatis, V., Meron, M., Saranga, Y., Tsipris, J.: Estimation of leaf water potential by thermal imagery and spatial analysis*. J. Exp. Botany 56(417), 1843–1852 (2005)CrossRefGoogle Scholar
  37. 37.
    Meron, M., Tsipris, J., Orlov, V., Alchanatis, V., Cohen, Y.: Crop water stress mapping for site-specific irrigation by thermal imagery and artificial reference surfaces. Prec. Agric. 11(2), 148–162, Apr 2010Google Scholar
  38. 38.
    Tilling, A.K., OLeary, G.J., Ferwerda, J.G., Jones, S.D., Fitzgerald, G.J., Rodriguez, D., Belford, R.: Remote sensing of nitrogen and water stress in wheat. Field Crops Res. 104(1), 77–85 (2007). Groundbreaking Stuff- Proceedings of the 13th Australian Society of Agronomy Conference, 10–14 Sept 2006, Perth, Western AustraliaGoogle Scholar
  39. 39.
    Ben-Gal, A., Kool, D., Agam, N., van Halsema, G.E., Yermiyahu, U., Yafe, A., Presnov, E., Erel, R., Majdop, A., Zipori, I., Segal, E., Rger, S., Zimmermann, U., Cohen, Y., Alchanatis, V., Dag, A.: Whole-tree water balance and indicators for short-term drought stress in non-bearing barnea olives. Agric. Water Manage. 98(1), 124–133 (2010)Google Scholar
  40. 40.
    Berni, J.A.J., Zarco-Tejada, P.J., Sepulcre-Cant, G., Fereres, E., Villalobos, F.: Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sens. Environ. 113(11), 2380–2388 (2009)CrossRefGoogle Scholar
  41. 41.
    Sepulcre-Cant, G., Zarco-Tejada, P.J., Jimnez-Muoz, J.C., Sobrino, J.A., de Miguel, E., Villalobos, F.J.: Detection of water stress in an olive orchard with thermal remote sensing imagery. Agric. Forest Meteorol. 136(1), 31–44 (2006)CrossRefGoogle Scholar
  42. 42.
    El-Faki, M.S., El-Shafie, H.A.F., Al-Hajhoj, M.B.R.: Potentials for early detection of red palm weevil (coleoptera: Curculionidae)-infested date palm (arecaceae) using temperature differentials. Canad. Entomol. 148(2), 239–245 (2016)Google Scholar
  43. 43.
    Montoya, L.: Geo-data acquisition through mobile gis and digital video: an urban disaster management perspective. Environ. Model. Softw. 18(10), 869–876 (2003). Integrating Environmental Modelling and GI-TechnologyGoogle Scholar
  44. 44.
    Papadopoulos, Nikos T., Katsoyannos, Byron I., Nestle, David: Spatial autocorrelation analysis of a ceratitis capitata (diptera: Tephritidae) adult population in a mixed deciduous fruit orchard in northern greece. Environ. Entomol. 32(2), 319–326 (2003)CrossRefGoogle Scholar
  45. 45.
    Sciarretta, A., Trematerra, P., Baumgrtner, J.: Geostatistical analysis of cydia funebrana (lepidoptera: Tortricidae) pheromone trap catches at two spatial scales. Amer. Entomol. 47(3), 174–185 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ali Ahmed
    • 1
  • Abdelhameed Ibrahim
    • 2
  • Sherif Hussein
    • 2
    • 3
  1. 1.Faculty of Computers and InformationMenoufia UniversityMenoufiaEgypt
  2. 2.Faculty of EngineeringMansoura UniversityMansouraEgypt
  3. 3.D.I.Mendeleyev Institute for MetrologySaint PetersburgRussia

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