A review of recent sensing technologies to detect invertebrates on crops
- 1.1k Downloads
Precision agriculture needs integrated pest management (IPM), for which detection and identification of target invertebrate species is a prerequisite. Researchers have been developing various technologies to detect pests more efficiently and accurately. However, these existing sensing technologies still have limitations for effective infield applications. This review paper aims to explore the relative technologies and find a sensing method that has potential to detect and identify common invertebrates on crops, such as butterflies, locusts, snails and slugs. It was found that there are two main research branches for invertebrate detection and identification: acoustic sensing and machine vision system (MVS). Acoustic sensing is suitable for detecting and identifying pests in soil, stored grains and wood, while usually acoustic sensors need to be attached to samples for inspection, which causes difficulties for efficient infield applications. MVS has the potential to provide a more effective and flexible way to detect and identify invertebrates on crops. In recent work with MVS, the technologies of invertebrate identification have been intensively studied, however, infield detection is relatively weak. This review points out the current research gaps and then discusses the potential research directions.
KeywordsInvertebrate detection Insect identification IPM Machine vision Acoustic sensing
Artificial neural network
Digital automated identification system
Field of view
Gray-level co-occurrence matrix
Integrated pest management system
Linear discriminant analysis
Laser Doppler vibrometer
Light emitting diode
Logistic model tree
Modulus of elasticity
Machine vision system
Quadratic discriminant analysis
Structure from motion
Scale-invariant feature transform
Support vector machine
Time of flight
- Baggio, D. L., Emami, S., & Escrivá, D. M. (2012). Exploring structure from motion using OpenCV. In Mastering openCV with practical computer vision projects (p. 121). Birmingham, UK: Packt Publishing Ltd. ISBN:978-1-84951-782-9.Google Scholar
- Batista, G., Hao, Y., Keogh, E., & Mafra, N. (2011). Towards automatic classification on flying insects using inexpensive sensors. In IEEE 10th International Conference on Machine Learning and Applications and Workshops (pp. 364–369). doi: 10.1109/ICMLA.2011.145.
- Bradski, G., & Adrian, K. (2008). Projection and 3D vision. In Learning OpenCV (p. 405). Sebastopol, CA, USA: O’ Reilly Media, Inc. ISBN:978-0-596-51613-0.Google Scholar
- Clarry, S. (2013). Insecticide resistance increasing in aphids. Barton, Canberra, Australia: Grains Research and Development Corporation. Retrieved May 10, 2016, from http://www.grdc.com.au/Media-Centre/Ground-Cover/Ground-Cover-Issue-106-Sept-Oct-2013/Insecticide-resistance-increasing-in-aphids.
- Cruz, M. S. (2011). Insect vision: ultraviolet, color and LED light. Athens: University of Georgia Department of Entomology. Retrieved May 5, 2016, from https://www.creelink.com/exLink.asp?13398800OK92K86I31178696.
- Csurka, G., Dance, C. R., Fan, L., Willamowski, J., & Bray, C. (2004). Visual categorization with bags of keypoints. In ECCV International Workshop on Statistical Learning in Computer Vision (pp. 1–22). CiteSeer.Google Scholar
- Demtröder, W. (2013). Applications of laser spectroscopy. In Laser spectroscopy: Basic concepts and instrumentation (pp. 851–878). Berlin, Germany: Springer Verlag. ISBN:3-540-65225-6.Google Scholar
- Fleurat-Lessard, F., Tomasini, B., Kostine, L., & Fuzeau1, B. (2006). Acoustic detection and automatic identification of insect stages activity in grain bulks by noise spectra processing through classification algorithms. In 9th International Working Conference on Stored Product Protection. Passo Fundo: Brazilian Post-Harvest Association (ABRAPOS). ISBN:8560234004.Google Scholar
- Floreano, D., Zufferey, J. C., Srinivasan, M. V., & Ellington, C. (2009). Motion detection chips for robotic platforms. In R. Moeckel & S. C. Liu (Eds.), Flying Insects and Robots (pp. 101–114). Berlin, Germany: Springer.Google Scholar
- GRDC (2012a). Insecticide resistance management and invertebrate pest identificaton fact sheet. Barton, Canberra: Grains Research and Development Corporation. Retrieved September 9, 2015, from http://rawbrown.com.au/pdf/agribusiness/Fact-Sheet-1.pdf.
- GRDC (2012b). Snail management fact sheet. Barton, Canberra: Grains Research and Development Corporation. Retrieved March 3, 2016, from http://grdc.com.au/Resources/Factsheets/2012/09/Snail-Management.
- GRDC (2013). Slug control fact sheet. Barton, Canberra. Retrieved February 10, 2016, from http://grdc.com.au/Resources/Factsheets/2013/03/Slug-control-identification-and-management.
- GRDC (2014a). Budworm in Western Australia. Barton, Canberra: Grains Research and Development corporation. Retrieved September 9, 2015, from http://www.depi.vic.gov.au/agriculture-and-food/pests-diseases-and-weeds/pest-insects-and-mites/redlegged-earth-mite.
- GRDC (2014b). Slugging slugs. Barton, Canberra: Grains Research and Development Corporation. Retrieved August 18, 2015, from http://grdc.com.au/Media-Centre/Hot-Topics/Slugging-slugs.
- Guarnier, A., Main, S., Molari, G., & Rondelli, V. (2011). Automatic trap for moth detection in integrated pest management. Bulletion of Insectology, 64(2), 247–251.Google Scholar
- Guyot, G., Baret, F., & Jacquemoud, S. (1992). Imaging spectroscopy for vegetation studies. In Imaging Spectroscopy: Fundamentals and Prospective Applications (pp.145–165) Netherlands: Springer. ISBN:978-0-7923-1535-3.Google Scholar
- Han, R., & He, Y. (2013). Remote automatic identification system of field pests based on computer vision. Transactions of the Chinese Society of Agricultural Engineering, 29(3), 156–162.Google Scholar
- Herron, G. (2005). Pesticide resistance management. Sydney, NSW: NSW Department of Primary Industries. Retrieved January 3, 2016, from http://ausvegvic.com.au/pdf/VegeNote-Pesticide-resistance-management.pdf.
- Holtzapffe, R., Mewett, O., Wesley, V., & Hattersley, P. (2008). Genetically modified crops: tools for insect pest and weed control in cotton and canola. Canberra, Australia: Department of Agriculture. Retrieved May 10, 2016, from http://trove.nla.gov.au/version/46736091.
- Johansmann, M., Siegmund, G., & Pineda, M. (2005). Targeting the limits of laser doppler vibrometry. Waldbronn: Polytec GmbH. RetrievedMarch 3, 2016, from http://www.polytec.com/fileadmin/user_uploads/Applications/Data_Storage/Documents/LM_TP_Idema_JP_2005_E.pdf.
- Karunakaran, C., Paliwal, J., Jayas, D. S., & White, N. D. G. (2005). Comparison of soft X-rays and NIR spectroscopy to detect insect infestations in grain (Paper no 053139). MI, USA: ASABE, St Joseph. doi: 10.13031/2013.19111.
- Kim, K. M., Lee, J. J., Lee, S. J., & Yeo, H. (2008). Improvement of wood CT images by consideration of the skewing of ultrasound caused by growth ring angle. Wood and Fiber Science, 40(4), 572–579.Google Scholar
- Kogan, M., & Hilton, R. J. (2009). Conceptual framework for integrated pest management (IPM) of tree-fruit pests. Biorational tree-fruit pest management, 1(31), 1–31.Google Scholar
- Leblanc, M. P., Gaunt, D., & Lessard, F. F. (2009). Experimental study of acoustic equipment for real-time insect detection in grain bins. Grain Protector. Chartres. http://www.grainprotector.com/index_htm_files/acoustic%20pest%20detection.pdf. Last Retrieved 1 Jan 2016.
- Liu, H., Lee, S. H., & Saunders, C. (2014). Development of a machine vision system for weed detection during both off-season and in-season in broadacre no-tillage cropping lands. American Journal of Agricultural and Biological Sciences, 9(2), 174–193. doi: 10.3844/ajabssp.2014.174.193.CrossRefGoogle Scholar
- Mankin, R. W., Smith, M. T., Tropp, J. M., Atkinson, E. B., & Jong, D. Y. (2008). Detection of Anoplophora glabripennis (Coleoptera: Cerambycidae) larvae in different host trees and tissues by automated analyses of sound-impulse frequency and temporal patterns. Journal of Economic Entomology, 101(3), 838.CrossRefPubMedGoogle Scholar
- Mankin, R. W., Weaver, D. K., Grieshop, M., Larson, B., & Morrill, W. (2004). Acoustic system for insect detection in plant stems: comparisons of Cephus cinctus in wheat and Metamasius callizona in bromeliads. Journal of Agricultural and Urban Entomology, 21(4), 239–248.Google Scholar
- Martin, B., Juliet, V., Sankaranarayanan, P., Gopal, A., & Rajkumar, I. (2013). Wireless implementation of mems accelerometer to detect red palm weevil on palms. In IEEE International Conference on Advanced Electronic Systems (pp. 248–252). doi: 10.1109/ICAES.2013.6659402, ISBN:978-1-4799-1439-5.
- McGregor, S. E. (1976). Insect pollination of cultivated crop plants (Vol. 496). Washington: United States Department of Agriculture.Google Scholar
- Schellhorn, N., Renwick, A., & Macfadyen, S. (2013). The real cost of pesticides in Australia’s food boom. The Conversation, Australia, Retrieved July 10, 2016, from http://theconversation.com/the-real-cost-of-pesticides-in-australias-food-boom-20757. Last accessed 10 July 2016
- Solis-Sánchez, L. O., García-Escalanted, J. J., Casta˜neda-Miranda, R., Torres-Pacheco, I., Guevara-González, R., et al. (2009). Machine vision algorithm for whiteflies (Bemisia tabaci Genn.) scouting under greenhouse environment. Journal of Applied Entomology, 133(7), 546–552.CrossRefGoogle Scholar
- Stritih, N. (2010). Auditory and vibratory sense of crickets. Polytec GmbH. Retrieved October 10, 2015, from http://www.polytec.com/fileadmin/user_uploads/Applications/LifeSciences_BioMedical/Documents/OM_TP_InFocus_Insect_Sounds_2010_02_E.pdf.
- Wang, Y., & Peng, Y. (2007). Application of watershed algorithm in image of food insects. Journal of Shandong University of Science and Technology, 26(2), 79–82.Google Scholar
- Weng, G. R. (2008). Monitoring population density of pests based on mathematical morphology. Chinese Society of Agricultural Engineering, 24(11), 135–138.Google Scholar
- Yang, Y., Peng, B., & Wang, J. (2011). A system for detection and recognition of pests in stored-grain based on video analysis. In Computer and computing technologies in agriculture IV (pp. 119–124). Berlin, Germany: Springer. ISBN:3642183328, doi: 10.1007/978-3-642-18333-1_16.
- Zeigler, H. P., & Bischof, H. J. (1993). Vision, brain and behavior in birds (p. 415). London, UK: The MIT Press. ISBN 9780262519779.Google Scholar