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Cognitive Modeling of the Natural Behavior of the Varroa destructor Mite on Video

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Abstract

The present work offers an innovative and automatic approach for detecting, tracking, analyzing, and reporting the natural behavior of the Varroa destructor mite and its activity from videos provided by the Tropical Apicultural Research Center (CINAT) in Costa Rica. These videos correspond to the presence of V. destructor in capped Africanized worker honeybee cells in a controlled environment. The main objective of this paper is to present an automatic report of the identification of the mite behavior based on mite information (bioinspired information). First, a calibration system was implemented to enhance the frame. This calibration was achieved by searching the movement-active area (MAA) and the geometrical definition of the V. destructor mite. Then, an automatic detection and tracking was applied. Finally, an automatic classification was used to establish the mite activity. This approach reached up to 92.83% for all processes: detection, tracking, behavior analysis, and activity reporting, in real time and showing a cognitive model of the mite. The proposed approach provides an automatic tool and objective measurement against manual and qualitative methods traditionally applied in this kind of analysis, with a significant potential to be used as a reference in the modeling of the behavior of the V. destructor mite.

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References

  1. Baley L, Ball BV. Honey bee pathology. London: Academic Press; 1991.

    Google Scholar 

  2. De Jong D. Varroa and other parasites of brood. In: Morse R, Flotum K, editors. Honey bee pests, predators, and diseases, 3. Ohio: A I Root Co; 2000. p. 280–327.

    Google Scholar 

  3. Calderón R, Arce H, Van Veen JW. Detección, distribución y control de Varroa jacobsoni, Oudemans en Costa Rica. Ciencias Veterinarias. 1998;2:31–40.

    Google Scholar 

  4. Ball BV. Acute paralysis virus isolates from honeybee colonies infested with Varroa jacobsoni. J Apic Res. 1998;24:115–9.

    Article  Google Scholar 

  5. Donzé G, Fluri P, Imdorf A. A look under the cap: the reproductive behavior of Varroa in the capped brood of the honey bee. American Bee Journal. 1998;138:528–33.

    Google Scholar 

  6. Calderón RA, Fallas N, Zamora LG, Van Veen JW, Sanchez LA. Behavior of Varroa mites in worker brood cells of Africanized honey bees. Exp Appl Acarol. 2009;49:329–38.

    Article  PubMed  Google Scholar 

  7. Calderón RA, Chaves G, Sanchez LA, Calderón R. Observation of Varroa destructor behavior in capped worker brood of Africanized honey bees. Exp Appl Acarol. 2012;58:279–90.

    Article  PubMed  Google Scholar 

  8. Donzé G, Guerin P. Behavioral attributes and parental care of Varroa mites parasiting honeybee brood. Behav Ecol Sociobiol. 1994;34:305–19.

    Article  Google Scholar 

  9. Yang L, Cheng H, Su J, Li X. Pixel-to-model distance for robust background reconstruction. IEEE Transactions on Circuits and Systems for Video Technology. 2016;26(5):903–16.

    Article  Google Scholar 

  10. Liu H, Yu Y, Sun F, Gu J. Visual-tactile fusion for object recognition. IEEE Trans Autom Sci Eng PP(99), 1–13. doi: 10.1109/TASE.2016.2549552.

  11. Liu H, Sun F. Discovery of topical objects from video: a structured dictionary learning approach. Cogn Comput. 2016;8(3):519–28.

    Article  Google Scholar 

  12. Liu H, Liu Y, Sun F. Robust exemplar extraction using structured sparse coding. IEEE Transactions on Neural Networks and Learning Systems. 2015;26(8):1816–21.

    Article  PubMed  Google Scholar 

  13. Pan J, Li X, Li X, Pang Y. Incrementally detecting moving objects in video with sparsity and connectivity. Cogn Comput. 2016;8(3):420–8.

    Article  Google Scholar 

  14. Li G, Liu Z, Li H, Ren P. Target tracking based on biological-like vision identity via improved sparse representation and particle filtering. Cogn Comput. 2016;8(5):910–23.

    Article  Google Scholar 

  15. Chen SB, Xin Y, Luo B. Action-based pedestrian identification via hierarchical matching pursuit and order preserving sparse coding. Cogn Comput. 2016;8(5):797–805.

    Article  Google Scholar 

  16. Ramírez M, Prendas JP, Travieso CM, Calderón R and Salas O. Detection of the mite Varroa destructor in honey bee cells by video sequence processing. IEEE 16th International Conference on Intelligent Engineering Systems (INES 2012), 2012;103–108.

  17. Mondet F, de Miranda JR, Kretzschmar A, Le Conte Y, Mercer AR. On the front line: quantitative virus dynamics in honeybee (Apis mellifera L.) colonies along a new expansion front of the parasite Varroa destructor. PLoS Pathog. 2014;10(8):e1004323.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Moore PA, Wilson ME, Skinner JA. Honey bee viruses, the deadly Varroa mite associates. Published online on extension.org, 21/08/2014 [http://www.extension.org/pages/71172/honey-bee-viruses-the-deadly-varroa-mite-associates#.VhYj1fl5OSp].

  19. Colla Ruvolo-Takasusuki MC, Alencar-Toledo V, Alves-Lopes D. Relationship between hygienic behavior and Varroa destructor mites in colonies producing honey or royal jelly. Sociobiology, an International Journal on Social Insects. 2012;59(1):241–9.Universidade Estadual de Feira de Santana doi:10.13102/sociobiology.v59i1.682.

  20. Sela I, Shafir S, Maori E, Garbian Y, Ben-Chanoch E, Yarden G, Kalev H. Compositions for controlling Varroa mites in bees. Patent No. US 8,962,584 B2. Date of Patent: 24/02/2015. United States Patent.

  21. Beaurepaire AL, Truong TA, Fajardo AC, Dinh TQ, Cervancia C. Host specificity in the honeybee parasitic mite, Varroa spp. in Apis mellifera and Apis cerana. Published online on plosone.org, 06/08/2015. [DOI: 10.1371/journal.pone.0135103].

  22. Cabrera AR, Shirk PD, Teal PEA, Grozinger CM, Evans JD. Examining the role of foraging and malvolio in host-finding behavior in the honey bee parasite, Varroa destructor (Anderson & Trueman). Arch Insect Biochem Physiol. 2014;85(2):61–75.

    Article  CAS  PubMed  Google Scholar 

  23. Le Conte Y, Huang ZY, Roux M, Zeng ZJ, Christidès JP, Bagnères AG. Varroa destructor changes its cuticular hydrocarbons to mimic new hosts. Biol Lett. 2015;11(6):20150233.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Al Toufailia HM, Amiri E, Scandian L, Kryger P, Ratnieks FW. Towards integrated control of Varroa: effect of variation in hygienic behaviour among honey bee colonies on mite population increase and deformed wing virus incidence. J Apic Res. 2014;53(5):555–62.

    Article  Google Scholar 

  25. Khongphinitbunjong K, de Guzman LI, Tarver MR, Rinderer TE, Chen Y, Chantawannakul P. Differential viral levels and immune gene expression in three stocks of Apis mellifera induced by different numbers of Varroa destructor. J Insect Physiol. 2015;72:28–34.

    Article  CAS  PubMed  Google Scholar 

  26. Perricone ST, Malfroy S. BeeForce Australia part I: involving urban beekeepers in surveillance initiatives for the early detection of Varroa mites. Bee World. 2014;91(2):36–7.

    Article  Google Scholar 

  27. Perricone ST, Malfroy S. Involving urban beekeepers in surveillance initiatives for the early detection of Varroa mites. Bee World. 2014;91(3):70–4.

    Article  Google Scholar 

  28. Emsen B, Hamiduzzaman Md M, Goodwin PH, Guzman-Novoa E. Lower Virus infections in Varroa destructor-Infested and Uninfested brood and adult honey bees (Apis mellifera) of a low mite population growth colony compared to a high mite population growth colony. Published online on plosone.org, 27/02/2015. 10.1371/journal.pone.0118885.

  29. Bahreini R, Currie RW. The influence of Nosema (Microspora: Nosematidae) infection on honey bee (Hymenoptera: Apidae) defense against Varroa destructor (Mesostigmata: Varroidae). J Econ Entomol. 2015;132:57–65.

    Google Scholar 

  30. Eliash N, Singh NK, Kamer Y, Pinnelli GR, Plettner E, Soroker V. Can we disrupt the sensing of honey bees by the bee parasite Varroa destructor? Published online on plosone.org, 16/09/2014. doi: 10.1371/journal.pone.0106889.

  31. Cervo R, Bruschini C, Cappa F, Meconcelli S, Pieraccini G, Pradella D, Turillazzi S. High Varroa mite abundance influences chemical profiles of worker bees and mite–host preferences. J Exp Biol. 2014;217:2998–3001.

    Article  CAS  PubMed  Google Scholar 

  32. Flores JM, Gil S, Padilla F. Reliability of the main field diagnostic methods of Varroa in honey bee colonies. Archivos de zootecnia. 2015;64:161–6.

    Google Scholar 

  33. Roldán J. Detección de Aethina tumida, Nosemiasis y Varroa destructor en la abejas melíferas de la Comarca Lagunera. Tesis publicado en el Repositorio Digital de la Universidad Autónoma Agraria Antonio Narro, 2014. [http://repositorio.uaaan.mx:8080/xmlui/handle/123456789/6761].

  34. Ratti V, Kevan PG, Eberl HJ. A mathematical model of the honeybee–Varroa destructor—acute bee paralysis virus system with seasonal effects. Bulletin of Mathematical Biology, vol. 2015;77:1–28.

    Article  Google Scholar 

  35. Xiao Y. Bio-inspired computing and networking, CRC Press, Taylor and Francis Press, 2011.

  36. Zhao H, Xiang K, Cao S, Wang X. Random walks colour histogram modification for human tracking. IET Comput Vis. 2016; doi:10.1049/iet-cvi.2015.0371.

    Google Scholar 

  37. Yang E, Gwak J, Jeon M. Multi-human tracking using part-based appearance modelling and grouping-based tracklet association for visual surveillance applications. Multimed Tools Appl 2016;1–24.

  38. Cai Z, Hu S, Shi Y, Wang Q, Zhang D. Multiple human tracking based on distributed collaborative cameras. Multimed Tools Appl 2016;1–17.

  39. Rodrigues E, Teixeira JM, Teichrieb V, Bernard E. Multi-objective tracking applied to bat populations. 2016 XVIII Symposium on Virtual and Augmented Reality (SVR), Gramado, Brazil, pp. 155–159, 2016.

  40. Shen M, Li C, Huang W, Szyszka P, Shirahama K, Grzegorzek M, Merhof D, Deussen O. Interactive tracking of insect posture. Pattern Recogn. 2015;48(11):3560–71.

    Article  Google Scholar 

  41. Shen M, Szyszka P, Deussen O, Galizia CG, Merhof D. Automated tracking and analysis of behavior in restrained insects. J Neurosci Methods. 2015;239(15):194–205.

    Article  PubMed  Google Scholar 

  42. Otsu N. A threshold selection method from grey level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 1979;9:62–6.

    Article  Google Scholar 

  43. Gonzalez R, Woods R. Image digital processing. Ed. Prentice-Hall. 2002.

  44. Jing L, L. Zhao Hui, Liang Z. Restoration of motion blurred image with Lucy-Richardson algorithm. Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 967519 (October 8, 2015); doi: 10.1117/12.2199337.

  45. Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Statistic Survey. 2010;4:40–79. doi:10.1214/09-SS054.

    Article  Google Scholar 

Download references

Acknowledgments

This paper had the support of the research project entitled Detección de esporas de Nosema en abejas Africanizadas mediante análisis automático de imágenes (Bio-DENA), enrolled in the School of Mathematics of the Costa Rica Institute of Technology.

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Authors and Affiliations

  1. School of Mathematics, Costa Rica Institute of Technology, Cartago, Costa Rica

    Melvin Ramírez-Bogantes, Juan P. Prendas-Rojas & Geovanni Figueroa-Mata

  2. Tropical Apiculture Research Center, National University of Costa Rica, Heredia, Costa Rica

    Rafael A. Calderon

  3. School of Mathematics, University of Costa Rica, San Pedro, Costa Rica

    Oscar Salas-Huertas

  4. Signals and Communications Department/ Institute for Technological Development and Innovation in Communications, University of Las Palmas, Gran Canaria, Spain

    Carlos M. Travieso

Authors
  1. Melvin Ramírez-Bogantes
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  2. Juan P. Prendas-Rojas
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  3. Geovanni Figueroa-Mata
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  4. Rafael A. Calderon
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  5. Oscar Salas-Huertas
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  6. Carlos M. Travieso
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Correspondence to Carlos M. Travieso.

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None of the studies carried out by the authors involved animal harm.

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Ramírez-Bogantes, M., Prendas-Rojas, J.P., Figueroa-Mata, G. et al. Cognitive Modeling of the Natural Behavior of the Varroa destructor Mite on Video. Cogn Comput 9, 482–493 (2017). https://doi.org/10.1007/s12559-017-9471-7

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