Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-27T09:54:46.973Z Has data issue: false hasContentIssue false
  • English
  • Français

Potential application of digital image-processing method and fitted logistic model to the control of oriental fruit moths (Grapholita molesta Busck)

Published online by Cambridge University Press:  18 April 2016

Z.G. Zhao ,
E.H. Rong ,
S.C. Li ,
L.J. Zhang ,
Z.W. Zhang ,
Y.Q. Guo  and
R.Y. Ma
Z.G. Zhao
Affiliation:
College of Agronomy, Shanxi Agricultural University, Taigu 030801, China
E.H. Rong
Affiliation:
Center of Laboratory, Shanxi Agricultural University, Taigu 030801, China
S.C. Li
Affiliation:
College of Agronomy, Shanxi Agricultural University, Taigu 030801, China
L.J. Zhang
Affiliation:
College of Agronomy, Shanxi Agricultural University, Taigu 030801, China
Z.W. Zhang
Affiliation:
College of Forestry, Shanxi Agricultural University, Taigu 030801, China
Y.Q. Guo
Affiliation:
College of Agronomy, Shanxi Agricultural University, Taigu 030801, China
R.Y. Ma*
Affiliation:
College of Agronomy, Shanxi Agricultural University, Taigu 030801, China
*
*Author for correspondence Tel: +86-0354-6289555 Fax: +86-0354-6289555 E-mail: maruiyan2004@163.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Monitoring of oriental fruit moths (Grapholita molesta Busck) is a prerequisite for its control. This study introduced a digital image-processing method and logistic model for the control of oriental fruit moths. First, five triangular sex pheromone traps were installed separately within each area of 667 m2 in a peach orchard to monitor oriental fruit moths consecutively for 3 years. Next, full view images of oriental fruit moths were collected via a digital camera and then subjected to graying, separation and morphological analysis for automatic counting using MATLAB software. Afterwards, the results of automatic counting were used for fitting a logistic model to forecast the control threshold and key control period. There was a high consistency between automatic counting and manual counting (0.99, P < 0.05). According to the logistic model, oriental fruit moths had four occurrence peaks during a year, with a time-lag of 15–18 days between adult occurrence peak and the larval damage peak. Additionally, the key control period was from 28 June to 3 July each year, when the wormy fruit rate reached up to 5% and the trapping volume was approximately 10.2 per day per trap. Additionally, the key control period for the overwintering generation was 25 April. This study provides an automatic counting method and fitted logistic model with a great potential for application to the control of oriental fruit moths.

Keywords

Grapholita molesta Buscksex pheromone trapautomatic countingimage processing
Type
Research Papers
Information
Bulletin of Entomological Research , Volume 106 , Issue 4 , August 2016 , pp. 457 - 463
Check for updates
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Beucher, S. (1992) The watershed transformation applied to image segmentation. Scanning Microscopy-Supplement 6, 299–299.Google Scholar
Du, J., Guo, J.T., Zhang, Y.S. & Wu, J.X. (2009) Effect of temperature on development and reproduction of Grapholitha molesta (Busck)(Lepidoptera: Tortricidae). Acta Agriculturae Boreali-Occidentalis Sinica 18(6), 314–318.Google Scholar
Gao, Y.B., Lu, Z.Z., Sun, Y.J. & Kong, Q.Q. (2005) Studies on forecasting the occurrence of soybean moth (Leguminivora glycinivorella) and its application. Jilin Agricultural Sciences 30(3), 18–20, 37.Google Scholar
Kim, Y., Jung, S., Kim, Y. & Lee, Y. (2011) Real-time monitoring of oriental fruit moth, Grapholita molesta, populations using a remote sensing pheromone trap in apple orchards. Journal of Asia-Pacific Entomology 14(3), 259–262.CrossRefGoogle Scholar
Marotz, J., Lübbert, C. & Eisenbeiss, W. (2001) Effective object recognition for automated counting of colonies in Petri dishes (automated colony counting). Computer Methods and Programs in Biomedicine 66(2/3), 183–198.CrossRefGoogle ScholarPubMed
Mu, S. (2003) The separateness of overlapping insect images based on mathematical morphology. Computer Engineering & Applications 18, 219–220.Google Scholar
Qiao, M., Lim, J., Ji, C.W., Chung, B.-K., Kim, H.-Y., Uhm, K.-B., Myung, C.S., Cho, J. & Chon, T.-S. (2008) Density estimation of Bemisia tabaci (Hemiptera: Aleyrodidae) in a greenhouse using sticky traps in conjunction with an image processing system. Journal of Asia-Pacific Entomology 11(1), 25–29.CrossRefGoogle Scholar
Si, L.L., Cao, K.Q., Liu, J.P., Yang, J.Y. & Zhen, W.C. (2006) Establishment of a real-time monitoring and forecasting system on main crop diseases and pests of China based on GIS. Acta Phytophylacica Sinica 33(3), 012.Google Scholar
Taylor, L.R. (1977) Aphid forecasting and the rothamsted insect survey. Journal of the Royal Agricultural Society of England 138, 75–97.Google Scholar
van der Geest, L.P. & Evenhuis, H.H. (1991) Tortricid Pests: Their Biology, Natural Enemies and Control. Elsevier Science Publishers, Amsterdam, the Netherlands.Google Scholar
Walton, V.M., Daane, K.M. & Pringle, K.L. (2004) Monitoring Planococcus ficus in South African vineyards with sex pheromone-baited traps. Crop Protection 23(11), 1089–1096.CrossRefGoogle Scholar
Wang, N.H., Li, D. & Pan, H. (2009) Information service platform of forest pest forecast based on WebGIS. Journal of Forestry Research 20(3), 275–278.CrossRefGoogle Scholar
Weng, G.R. (2008) Monitoring population density of pests based on mathematical morphology [J]. Transactions of the Chinese Society of Agricultural Engineering 24(11), 135–138.Google Scholar
Wickwire, K. (1977) Mathematical models for the control of pests and infectious diseases: a survey. Theoretical Population Biology 11(2), 182–238.CrossRefGoogle ScholarPubMed
Witzgall, P., Kirsch, P. & Cork, A. (2010) Sex pheromones and their impact on pest management. Journal of Chemical Ecology 36(1), 80–100.CrossRefGoogle ScholarPubMed
Yan, X., Chen, Y., Zhang, Y., Yuan, B.J., Cao, Z.P. & Zhou, P.K. (2011) Comparative analysis on automated separation methods of clustered regions composed of convex particles. Chinese Journal of Stereology and Image Analysis 17(3), 137–141.Google Scholar
Yao, Q., Lü, J., Yang, B.J., Xue, J., Zheng, H.H. & Tang, J. (2011) Progress in research on digital image processing technology for automatic insect identification and counting. China Agriculture Science 44(14), 2886–2899.Google Scholar
You, M. & Pang, X. (1995) A Computer simulation model of population dynamics of brown planthopper, Nilaparvata wgens stål. Insect Science 2(2), 163–178.CrossRefGoogle Scholar
Yu, X.W. & Shen, Z.R. (2001) Segmentation technology for digital image of insects [J]. Transactions of the Chinese Society of Agricultural Engineering 17(3), 137–141.Google Scholar
Zhang, J.W., Wang, Y.M. & Shen, Z.R. (2006) Novel method for estimating cereal aphid population based on computer vision technology. Transactions of the Chinese Society of Agricultural Engineering 22(9), 159–162.Google Scholar
Zhang, Y.Q., Wu, W.F. & Wang, G. (2011) Separation of corn seeds images based on threshold changed gradually. Transactions of the Chinese Society of Agricultural Engineering 27(7), 200–204.Google Scholar
Zhao, Z.G., Gao, L.H., Yang, H.J., Zhang, J.T., Wang, X. & Ma, R.Y. (2013 a) Research on mating timing rhythm of oriental fruit moth monitored by sex pheromone lure. Journal of Shanxi Agricultural Sciences 41(4), 366–368.Google Scholar
Zhao, Z.G., Rong, E.H., Zhao, Z.H., Kong, W.N., Zhang, J.T. & Ma, R.Y. (2013 b) Mathematical model of insect logistic increasing and economic threshold based on sex pheromone trap. Acta Ecologica Sinica 33(16), 5008–5016.CrossRefGoogle Scholar