Automating insect monitoring utilizing unsupervised near-infrared sensors

0
158
Automating insect monitoring using unsupervised near-infrared sensors

  • Stork, N. E. How many species of insects and other terrestrial arthropods are there on earth? (2017). https://doi.org/10.1146/annurev-ento-020117.

  • Scudder, G. Insect Biodiversity: Science and Society—Google Books (Wiley-Blackwell, 2009).

    Google Scholar 

  • Lami, F., Boscutti, F., Masin, R., Sigura, M. & Marini, L. Seed predation intensity and stability in agro-ecosystems: Role of predator diversity and soil disturbance. Agric. Ecosyst. Environ. 288, 106720 (2020).

    Google Scholar 

  • Gallai, N., Salles, J. M., Settele, J. & Vaissière, B. E. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol. Econ. 68, 810–821 (2009).

    Google Scholar 

  • Consoli, F. L., Parra, J. R. P. & Zucchi, R. A. Egg Parasitoids in Agroecosystems with Emphasis on Trichogramma (Springer Science, 2010).

    Google Scholar 

  • Sánchez-Guillén, R. A., Córdoba-Aguilar, A., Hansson, B., Ott, J. & Wellenreuther, M. Evolutionary consequences of climate-induced range shifts in insects. Biol. Rev. 91, 1050–1064 (2016).

    PubMed 

    Google Scholar 

  • Zalucki, M. P. et al. Estimating the economic cost of one of the world’s major insect pests, Plutella xylostella (Lepidoptera: Plutellidae): Just how long is a piece of string?. J. Econ. Entomol. 105, 1115–1129 (2012).

    PubMed 

    Google Scholar 

  • Dornelas, M. & Daskalova, G. N. Nuanced changes in insect abundance. Science (80-). 368, 368–369 (2020).

    CAS 
    ADS 

    Google Scholar 

  • Didham, R. K. et al. Interpreting insect declines: Seven challenges and a way forward. Insect Conserv. Divers. 13, 103–114 (2020).

    Google Scholar 

  • Greenwood, B. M., Bojang, K. & Whitty, C. J. M. Malaria. Lancet 365, 98 (2005).

    Google Scholar 

  • Dangles, O. & Casas, J. Ecosystem services provided by insects for achieving sustainable development goals. Ecosyst. Serv. 35, 109–115 (2019).

    Google Scholar 

  • Burkholder, W. E. & Ma, M. Pheromones for monitoring and control of stored-product insects. Annu. Rev. Entomol. 30, 257–272 (1985).

    CAS 

    Google Scholar 

  • Morris, R. F. Sampling insect populations. Annu. Rev. Entomol. 5, 243–264 (1960).

    Google Scholar 

  • Strickland, A. H. Sampling crop pests and their hosts. Annu. Rev. Entomol. 6, 201–220 (1961).

    Google Scholar 

  • Bannerman, J. A., Costamagna, A. C., McCornack, B. P. & Ragsdale, D. W. Comparison of relative bias, precision, and efficiency of sampling methods for natural enemies of soybean aphid (Hemiptera: Aphididae). J. Econ. Entomol. 108, 1381–1397 (2015).

    CAS 
    PubMed 

    Google Scholar 

  • Osborne, J. L. et al. Harmonic radar: A new technique for investigating bumblebee and honey bee foraging flight. VII Int. Symp. Pollinat. 437, 159–164 (1996).

    Google Scholar 

  • Zink, A. G. & Rosenheim, J. A. State-dependent sampling bias in insects: Implications for monitoring western tarnished plant bugs. Entomol. Exp. Appl. 113, 117–123 (2004).

    Google Scholar 

  • Rancourt, B., Vincent, C. & De Oliveira, A. D. Circadian activity of Lygus lineolaris (Hemiptera: Miridae) and effectiveness of sampling techniques in strawberry fields. J. Econ. Entomol 93, 1160–1166 (2000).

    CAS 
    PubMed 

    Google Scholar 

  • Binns, M. R. & Nyrop, J. P. Sampling insect populations for the purpose of IPM decision making. Annu. Rev. Entomol. 37, 427–453. https://doi.org/10.1146/annurev.ento.37.1.427 (1992).

    Article 

    Google Scholar 

  • Portman, Z. M., Bruninga-Socolar, B. & Cariveau, D. P. The state of bee monitoring in the United States: A call to refocus away from bowl traps and towards more effective methods. Ann. Entomol. Soc. Am. 113, 337–342 (2020).

    Google Scholar 

  • Montgomery, G. A., Belitz, M. W., Guralnick, R. P. & Tingley, M. W. Standards and best practices for monitoring and benchmarking insects. Front. Ecol. Evolut. 8, 579193 (2021).

    Google Scholar 

  • Bick, E., Dryden, D. M., Nguyen, H. D. & Kim, H. A novel CO2-based insect sampling device and associated field method evaluated in a strawberry agroecosystem. J. Econ. Entomol. 113, 1037–1042 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • Wen, C. & Guyer, D. Image-based orchard insect automated identification and classification method. Comput. Electron. Agric. 89, 110–115 (2012).

    Google Scholar 

  • Chen, Y., Why, A., Batista, G., Mafra-Neto, A. & Keogh, E. Flying insect classification with inexpensive sensors. J. Insect Behav. 27, 657–677 (2014).

    Google Scholar 

  • Potamitis, I. & Rigakis, I. Novel noise-robust optoacoustic sensors to identify insects through wingbeats. IEEE Sens. J. 15, 4621–4631 (2015).

    CAS 
    ADS 

    Google Scholar 

  • Eliopoulos, P. A., Potamitis, I., Kontodimas, D. C. & Givropoulou, E. G. Detection of adult beetles inside the stored wheat mass based on their acoustic emissions. J. Econ. Entomol. 108, 2808–2814 (2015).

    CAS 
    PubMed 

    Google Scholar 

  • Ärje, J. et al. Automatic image-based identification and biomass estimation of invertebrates. Methods Ecol. Evol. 11, 922–931 (2020).

    Google Scholar 

  • Hobbs, S. E. & Hodges, G. An optical method for automatic classification and recording of a suction trap catch. Bull. Entomol. Res. 83, 47–51 (1993).

    Google Scholar 

  • O’Neill, M. A., Gauld, I. D., Gaston, K. J. & Weeks, P. Daisy: An automated invertebrate identification system using holistic vision techniques. in Proceedings of the Inaugural Meeting BioNET-INTERNATIONAL Group for Computer-Aided Taxonomy (BIGCAT) 13–22 (1997).

  • Chesmore, E. D. Methodologies for automating the identification of species. in First BioNet-International Work. Gr. Autom. Taxon. 3–12 (2000).

  • Martineau, M. et al. A survey on image-based insect classification. Pattern Recognit. 65, 273–284 (2017).

    ADS 

    Google Scholar 

  • Silva, D. F., De Souza, V. M. A., Batista Geapa, K. E. & Ellis, D. P. W. Applying machine learning and audio analysis techniques to insect recognition in intelligent traps. in Proceedings—2013 12th International Conference on Machine Learning and Applications, ICMLA 2013. (2013).

  • Capinera, J. L. & Walmsley, M. R. Visual responses of some sugarbeet insects to sticky traps and water pan traps of various colors. J. Econ. Entomol., 71(6), 926–927 (1978).

    Google Scholar 

  • Moore, A., Miller, J. R., Tabashnik, B. E. & Gage, S. H. Automated identification of flying insects by analysis of wingbeat frequencies. J. Econ. Entomol. 79, 1703–1706 (1986).

    Google Scholar 

  • Riley, J. R. Angular and temporal variations in the radar cross-sections of insects. Proc. Inst. Electr. Eng. (IET) 120, 1229–1232 (1973).

    Google Scholar 

  • Reed, S. C., Williams, C. M. & Chadwick, L. E. Frequency of wing-beat as a character for separating species races and geographic varieties of Drosophila. Genetics 27, 349 (1942).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mankin, R. W., Hagstrum, D. W., Smith, M. T., Roda, A. L. & Kairo, M. T. K. Perspective and promise: a century of insect acoustic detection and monitoring. Am. Entomol. 57(1), 30–44 (2011).

    Google Scholar 

  • Drake, V. A. & Reynolds, D. R. Radar Entomology: Observing Insect Flight and Migration (Cabi, 2012).

    Google Scholar 

  • Long, T. et al. Entomological radar overview: System and signal processing. IEEE Aerosp. Electron. Syst. Mag. 35, 20–32 (2020).

    Google Scholar 

  • Drake, V. A., Hatty, S., Symons, C. & Wang, H. Insect monitoring radar: Maximizing performance and utility. Remote Sens. 12, 596 (2020).

    ADS 

    Google Scholar 

  • Brydegaard, M. & Jansson, S. Advances in entomological laser radar. IET Int. Radar Conf. https://doi.org/10.1049/joe.2019.0598 (2018).

    Article 

    Google Scholar 

  • Jansson, S. Entomological Lidar: Target Characterization and Field Applications (Department of Physics, Lund University, 2020).

    Google Scholar 

  • Malmqvist, E. From Fauna to Flames: Remote Sensing with Scheimpflug-Lidar (Department of Physics, Lund University, 2019).

    Google Scholar 

  • Mankin, R. W., Hagstrum, D. W., Smith, M. T., Roda, A. L. & Kairo, M. T. K. Perspective and promise: A century of insect acoustic detection and monitoring. Am. Entomol. 57, 30–44 (2011).

    Google Scholar 

  • Miller-Struttmann, N. E., Heise, D., Schul, J., Geib, J. C. & Galen, C. Flight of the bumble bee: Buzzes predict pollination services. PLoS ONE 12, 1–14 (2017).

    Google Scholar 

  • Li, Y. et al. Mosquito detection with low-cost smartphones: Data acquisition for malaria research. arXiv:1711.06346 [stat.ML] (2017).

  • Mukundarajan, H., Hol, F. J. H., Castillo, E. A., Newby, C. & Prakash, M. Using mobile phones as acoustic sensors for high-throughput mosquito surveillance. Elife 6, 1–26 (2017).

    Google Scholar 

  • Osborne, J. L. et al. A landscape-scale study of bumble bee foraging range and constancy, using harmonic radar. J. Appl. Ecol. 36, 519–533 (1999).

    Google Scholar 

  • Smith, A. D., Riley, J. R. & Gregory, R. D. A method for routine monitoring of the aerial migration of insects by using a vertical-looking radar. Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 340, 393–404 (1993).

    Google Scholar 

  • Chapman, J. W., Smith, A. D., Woiwod, I. P., Reynolds, D. R. & Riley, J. R. Development of vertical-looking radar technology for monitoring insect migration. Comput. Electron. Agric. 35(2–3), 95–110 (2002).

    Google Scholar 

  • Schaefer, G. W. & Bent, G. A. An infra-red remote sensing system for the active detection and automatic determination of insect flight trajectories (IRADIT). Bull. Entomol. Res. 74, 261–278 (1984).

    Google Scholar 

  • Farmery, M. J. Optical studies of insect flight at low altitude. (Doctoral dissertation, University of York, 1981).

  • Farmery, M. J. The effect of air temperature on wingbeat frequency of naturally flying armyworm moth (Spodoptera exempta). Entomol. Exp. Appl. 32, 193–194 (1982).

    Google Scholar 

  • Malmqvist, E. & Brydegaard, M. Applications of KHZ-CW lidar in ecological entomology. EPJ Web Conf. 119, 25016. https://doi.org/10.1051/epjconf/2016I11925016 (2016).

    Article 

    Google Scholar 

  • Brydegaard, M. et al. Lidar reveals activity anomaly of malaria vectors during pan-African eclipse. Sci. Adv. 6, eaay5487 (2020).

    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 

  • Malmqvist, E. et al. The bat–bird–bug battle: Daily flight activity of insects and their predators over a rice field revealed by high-resolution Scheimpflug Lidar. Roy. Soc. Open Sci. 5(4), 172303 (2018).

    ADS 

    Google Scholar 

  • Fristrup, K. M., Shaw, J. A. & Tauc, M. J. Development of a wing-beat-modulation scanning lidar system for insect studies. Lidar Remote Sens. Environ. Monit. 2017, 15. https://doi.org/10.1117/12.2274656 (2017).

    Article 

    Google Scholar 

  • Hoffman, D. S., Nehrir, A. R., Repasky, K. S., Shaw, J. A. & Carlsten, J. L. Range-resolved optical detection of honeybees by use of wing-beat modulation of scattered light for locating land mines. Appl. Opt. 46, 3007–3012 (2007).

    PubMed 
    ADS 

    Google Scholar 

  • Jansson, S., Malmqvist, E. & Mlacha, Y. Real-time dispersal of malaria vectors in rural Africa monitored with lidar. Plos one. 16(3), e0247803 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jansson, S. & Brydegaard, M. Passive kHz lidar for the quantification of insect activity and dispersal. Anim. Biotelemet. 6, 6 (2018).

    Google Scholar 

  • Jansson, S. P. & Sørensen, M. B. An optical remote sensing system for detection of aerial and aquatic fauna. U.S. Patent Application No. 16/346,322 (2019).

  • Malmqvist, E., Jansson, S., Török, S. & Brydegaard, M. Effective parameterization of laser radar observations of atmospheric fauna. IEEE J. Sel. Top. Quant. Electron. 22, 1 (2015).

    Google Scholar 

  • Drake, V. A., Wang, H. K. & Harman, I. T. Insect Monitoring Radar: Remote and network operation. Comput. Electron. Agric. 35, 77–94 (2002).

    Google Scholar 

  • Kirkeby, C. et al. Advances in automatic identification of flying insects using optical sensors and machine learning. Sci. Rep. 11, 1555 (2021).

    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 

  • Jacques, S. L. Erratum: Optical properties of biological tissues: A review (Physics in Medicine and Biology (2013) 58). Phys. Med. Biol. 58, 5007–5008 (2013).

    Google Scholar 

  • Li, M. et al. Bark beetles as lidar targets and prospects of photonic surveillance. J. Biophoton. https://doi.org/10.1002/jbio.202000420 (2020).

    Article 

    Google Scholar 

  • Brydegaard, M. Advantages of shortwave infrared LIDAR entomology. in Laser Applications to Chemical, Security and Environmental Analysis LW2D-6 (Optical Society of America, 2014).

    Google Scholar 

  • Brydegaard, M., Jansson, S., Schulz, M. & Runemark, A. Can the narrow red bands of dragonflies be used to perceive wing interference patterns? Ecol. Evol. 8(11), 5369–5384 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Gebru, A. et al. Multiband modulation spectroscopy for the determination of sex and species of mosquitoes in flight. J. Biophotonics 11(8), e201800014 (2018).

    PubMed 

    Google Scholar 

  • Potamitis, I. Classifying insects on the fly. Ecol. Inform. 21, 40–49 (2014).

    Google Scholar 

  • Heathcote, G. D. The comparison of yellow cylindrical, flat and water traps, and of Johnson suction traps, for sampling aphids. Ann. Appl. Biol. 45, 133–139 (1957).

    Google Scholar 

  • Vaishampayan, S. M., Kogan, M., Waldbauer, G. P. & Woolley, J. Spectral specific responses in the visual behavior of the greenhouse whitefly, Trialeurodes vaporariorum (Homoptera: Aleyrodidae). Entomol. Exp. Appl. 18, 344–356 (1975).

    Google Scholar 

  • Mound, L. A. Studies on the olfaction and colour sensitivity of Bemisia tabaci (Genn.) (Homoptera, Aleyrodidae). Entomol. Exp. Appl. 5, 99–104 (1962).

    Google Scholar 

  • Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Van Der Kooi, C. J., Stavenga, D. G., Arikawa, K., Belušič, G. & Kelber, A. Evolution of insect color vision: From spectral sensitivity to visual ecology. Annu. Rev. Entomol. 66, 435–461 (2021).

    PubMed 

    Google Scholar