Sensing Technologies used for Monitoring and Detecting Insect Infestation in Stored Grain

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    In India, the production of grain has been steadily increasing. Improper storage of grain results in higher losses in terms of quality as well as quantity. Contamination of grain occurs due to insects and micro-organisms present in it. Their presence and growth highly depends on environmental factors. When grain gets infested, volatile compounds get accumulated and release odour. The rapid growth of sensing technology makes the early and accurate detection of insects/fungi more promising. This paper discusses about different kinds of sensing technologies such as environmental sensing, acoustic sensing, odour sensing and image sensing, their working, challenges and issues, advantages and limitations. Future trends of using sensing technology are also discussed.

     

     


  • Keywords


    Acoustic sensing; E-nose; Insect infestation; Sensors; Stored grain

  • References


      [1] HH Shepard, “Insects infesting stored grain and seeds”, Minnesota Bulletin 340, 1947.

      [2] S. Neethirajan et al, “Detection Technique for Stored-Product Insects in Grain” Food Control-Elsevier, Vol. 18, 2007, pp. 2007.

      [3] R.K. Upadhyay and S. Ahmad, “Management Strategies for Control of Stored Grain Insect Pests in Farmer Stores and Public Ware Houses”, World Journal of Agricultural Sciences, Vol. 7, 2011, pp. 527-549.

      [4] S. Neethirajan, D.S Jayas, “Sensors for grain storage” ASABE: An Meeting presentation paper,Minnesota, 2007.

      [5] Maier D.E. et al, “Monitoring carbon dioxide concentration for early detection of spoilage in stored grain”, 10th International Working Conference on Stored Product Protection, June 2010.

      [6] Fuji Jian and Digvir S. Jayas, “Temperature monitoring”, Journal -Stored Product Protection, 2012, pp. 271-281.

      [7] Chandra B. Singh and John M. Fielke, “Recent Developments in Stored Grain Sensors, Monitoring and Management Technology” IEEE Instrumentation & Measurement Magazine, Vol. 20 , Issue 3, June 2017, pp. 32-55

      [8] S. Neethirajan, D. S. Jayas & S. Sadistap, “Carbon Dioxide (CO2) Sensors for the Agri-food Industry-A Review”, Food and Bioprocess Technology-Springer, Vol. 2, 2009, pp. 115–121.

      [9] Phillip K.Harein, Arthur F. Press, “Mortality of stored-peanut insects exposed to mixtures of atmospheric gases at various temperatures”, Journal of stored products- Elsevier, Vol. 4, Issue 1, May 1968.

      [10] S. Neethirajan et al, “Development of carbon dioxide (CO2) sensor for grain quality monitoring”, Biosystem Engineering–Elsevier, 2010.

      [11] Ricardo Bartosik et al, “CO2 Monitoring of Grain Stored in Silobag Through a Web Application, EFITA-WCCA-CIGR Conference on Sustainable Agriculture through ICT innovation, June 2013, Italy.

      [12] H.B. Gonzales et al, “Simultaneous Monitoring of Stored Grain with Relative Humidity, Temperature, and Carbon Dioxide Sensors” , Biological American Society of Agricultural Engineers, Vol. 25(4), 200.,pp: 595‐604.

      [13] M.O. Onibonoje, A.M. Jubril, O.K. Owolarafe, “Determination of Bulk Grains Moisture Content in a Silo Using Distributed Sensor Network”, IFE Journal of Technology, Vol. 21(2), 2012, pp. 55-59.

      [14] F. Fleurat-Lessard, B. Tomasini, L. Kostine, B. Fuzeau, “Acoustic detection and automatic identification of insect stages activity in grain bulks by noise spectra processing through classification algorithms”, 9th International conference on stored product protection, Brazil, 2006 , pp. 476-486.

      [15] R.W. Mankin, D.W. Hanstrum, M.T. Smith, A.L. Roda, ‘Perspective and Promise: a century of insect acoustic detection and monitoring”, American Entomologist, Vol. 57, Issue 1, 1 January 2011, pp. 30–44.

      [16] Panagiotis A. Eliopoulos , Ilyas potamities, D. kontodimas, “Estimation of population density of stored grain pests via bioacoustic detection” Crop protection – Elsevier, Vol. 85, July 2016, pp. 71-78.

      [17] R. W. Mankin D. Shuman J. A. Coffelt, “Noise Shielding of Acoustic Devices for Insect Detection, Journal of Economic Entomology, Vol. 89, Issue 5, October 1996, pp. 1301–1308.

      [18] David W. Hagstrum, Paul W. Flinn, “Comparison of Acoustical Detection of Several Species of Stored-Grain Beetles (Coleoptera: Curculionidae, Tenebrionidae, Bostrichidae, Cucujidae) Over a Range of Temperatures”, Journal of Economic Entomology, Vol. 86, Issue 4, 1 August 1993, pp: 1271–1278.

      [19] R. Hickling, W. Wei and D. W. Hagstrum, “Studies of Sound Transmission in of Stored Grain for Acoustic of Insects Various Types Detection”, Applied Acoustics-Elsevier, Vol. 50, No. 4, 1997, pp. 263 278

      [20] K. W. Vick, J. C. Webb, B. A. Weaver C. Litzkow, “Sound Detection of Stored-Product Insects That Feed Inside Kernels of Grain”, Journal of Economic Entomology, Vol. 81, Issue 5, 1 October 1988, pp. 1489–1493.

      [21] K.M Coggins, J. Principe, “Detection and classification of insect sounds in a grain silo using a neural network”, IEEE International Conference on Neural Networks, August 2002, pp: 1760-1765.

      [22] L. Schwab, P. Degoul, “Automatic acoustical surveillance system of grains in silos” 2005

      [23] R. W. Mankin, A. Mizrach, A. Hetzroni, S. Levsky, Y. Nakache, V. Soroker,Temporal and Spectral Features of Sounds of Wood-Boring Beetle Larvae: Identifiable Patterns of Activity Enable Improved Discrimination from Background Noise” Journal of Florida Entomologist, Vol. 91, No. 2, June 2008, pp. 241-248

      [24] Denis Kiobia,”Characterization of sounds in maize produced by internally feeding insects: investigations to develop inexpensive devices for detection of Prostephanus truncatus (Coleoptera: Bostrichidae) and Sitophilus zeamais (Coleoptera: Curculionidae) in small-scale storage facilities in sub-Saharan Africa” Florida Entomologist , Vol. 98, No. 2, 2015, pp. 405-409

      [25] Panagiotis A. Elipoulos, Ilyas Patamitis, Dimitris Ch. Kontodimas, “Estimation of population density of stored grain pests via bioacoustic detection”, Crop Protection–Elsevier,Vol. 85, July 2016, pp. 71-78.

      [26] A. Jonsson, F. Winquist, “Electronic nose for microbial quality classification of grains”, International Journal of Food Microbiology, Vol. 35, Issue 2, April 1997, pp. 187-193.

      [27] Alphus D. Wilson, Manuela Baietto, “Applications and Advances in Electronic-Nose Technologies”, Sensors-MDPI, Vol. 9, 2009, pp. 5099-5148.

      [28] J.R Stetter, M.W. Findlay, “Quality classification of grain using a sensor array and pattern recognition”, Analytica Chimica Acta, Vol 284, Issue 1, December 1993, pp. 1-11.

      [29] T. Borjesson, t. eklov, A. Jonsson, “ Electronic Nose for Odour Classification of Grains” Journal of Analytical techniques and instrumentation, Vol. 73,Issue 4, 1996, pp. 457-461.

      [30] Jie Hu, “Application of PCA Method on Pest Information Detection of Electronic Nose”, IEEE International conference on information acquisition, August 2006, China, pp. 1465-1468.

      [31] Hongmei Zhang, Jun Wang, Xiaojing Tian, Huichun Yu, Yong Yu, “Optimization of sensor array and detection of stored duration of wheat by electronic nose” , Journal of Food Engineering-Elsevier, Vol. 82, 2007, pp. 403–408.

      [32] Bo Zhou, Jun Wang, “Detection of Insect Infestations in Paddy Field using an Electronic Nose” International Journal Of Agriculture & Biology, Vol. 13, 2011, pp. 707-712.

      [33] Wu, J., D.S. Jayas, Q. Zhang, N.D.G. White and R.K.. York. ,”Feasibility of the application of electronic nose technology to detect insect infestation in wheat” Canadian Biosystems Engineering, Vol. 55, January 2013.

      [34] Sai Xu, Zhiyan, Keliang Li, “Recognition of the duration and prediction of insect prevalence of stored rough rice infested by the red flour beetle (tribolium casteneum herbst) using and electronic nose” Sensors-MDPI, Vol. 17 , No 4, April 2017.

      [35] G. khesri et al, “Use of an electronic nose for the early detection and differentiation between spoilage fungi” Letters in Applied Microbiology, Vol. 27, 1998, 27, pp. 261–264.

      [36] Xiaoguo Ying et al. , “E-nose based rapid prediction of early mouldy grain using probabilistic neural networks” Bioengineered Taylor & Francis Group, LLC, Vol. 6, No 4, 2015 pp. 222-226.

      [37] P. Tirelli, N.A. Borghese, F. Pedersini, G. Galassi, R. Oberti, “ Automatic monitoring of pest insects traps by Zigbee based wireless networking of image sensors” IEEE conference on Instrumentation and Measurement Technology Conference (I2MTC), 2011

      [38] Maxime Martineau et al, “A survey on image-based insects classification”, Journal on pattern recognition-Elsevier, Vol. 65, May 2017, pp. 273-284.


 

View

Download

Article ID: 20456
 
DOI: 10.14419/ijet.v7i4.6.20456




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.