Automated Counting of Sex-Pheromone Attracted Insects Using Trapped Images

  • Wenyong LiEmail author
  • Meixiang Chen
  • Ming Li
  • Chuanheng Sun
  • Lin Wang
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 545)


In this paper, an automatic segmentation and counting method for insect monitoring in orchard was proposed. The method based on image processing consisted of: (1) touching insect detection, (2) local segmentation points search using boundary tracking and morphological thinning operation, recursively, (3) segmentation lines implementation using the shortest distance idea. Algorithm performance was evaluated in terms of segmentation ratio and segmentation accuracy. Compared with the watershed method, the proposed method had improvement in evaluation criteria. Its average segmentation ratio was 1.03 and average segmentation accuracy was 96.7%, respectively. The results demonstrate the proposed method is an alternative solution for insect monitoring in integrated pest management.


Image segmentation Boundary tracking Watershed Insect counting Integrated pest management 



This research was supported by Beijing Natural Science Foundation (6164034) and National Natural Science Foundation of China (61601034). All of the mentioned support and assistance are gratefully acknowledged.


  1. 1.
    Wen, C., Guyer, D.: Image-based orchard insect automated identification and classification method. Comput. Electron. Agric. 89, 110–115 (2012)CrossRefGoogle Scholar
  2. 2.
    Kang, S.-H., Cho, J.-H., Lee, S.-H.: Identification of butterfly based on their shapes when viewed from different angles using an artificial neural network. J. Asia-Pac. Entomol. 17(2), 143–149 (2014)CrossRefGoogle Scholar
  3. 3.
    Kaya, Y., Kayci, L., Uyar, M.: Automatic identification of butterfly species based on local binary patterns and artificial neural network. Appl. Soft Comput. 28, 132–137 (2015)CrossRefGoogle Scholar
  4. 4.
    Chen, Y., Hu, X., Zhang, C.: Algorithm for segmentation of insect pest images from wheat leaves based on machine vision. Trans. Chin. Soc. Agric. Eng. 23(12), 187–191 (2007)Google Scholar
  5. 5.
    Xia, C., et al.: Automatic identification and counting of small size pests in greenhouse conditions with low computational cost. Ecol. Inform. 29, 139–146 (2015). Part 2CrossRefGoogle Scholar
  6. 6.
    Barbedo, J.G.A.: Using digital image processing for counting whiteflies on soybean leaves. J. Asia-Pac. Entomol. 17(4), 685–694 (2014)CrossRefGoogle Scholar
  7. 7.
    Zhang, S., et al.: Algorithm for segmentation of whitefly images based on DCT and region growing. Trans. Chin. Soc. Agric. Eng. 29(17), 121–128 (2013)Google Scholar
  8. 8.
    Wang, Y.-Y., Peng, Y.-J.: Application of watershed algorithm in image of food insects. J. Shandong Univ. Sci. Technol. 26(2), 79–82 (2007)Google Scholar
  9. 9.
    Weng, G.: Monitoring population density of pests based on mathematical morphology. Trans. Chin. Soc. Agric. Eng. 24(11), 135–138 (2008)Google Scholar
  10. 10.
    Yao, Q., et al.: Segmentation of touching insects based on optical flow and NCuts. Biosys. Eng. 114(2), 67–77 (2013)CrossRefGoogle Scholar
  11. 11.
    Zhong, Q.F., et al.: A novel segmentation algorithm for clustered slender-particles. Comput. Electron. Agric. 69(2), 118–127 (2009)CrossRefGoogle Scholar
  12. 12.
    Aymen, M., et al.: Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method. Biomed. Signal Process. Control 8(5), 421–436 (2013)CrossRefGoogle Scholar
  13. 13.
    Zhang, X.D., et al.: A marker-based watershed method for X-ray image segmentation. Comput. Methods Programs Biomed. 113(3), 894–903 (2014)CrossRefGoogle Scholar
  14. 14.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  15. 15.
    Lao, F., et al.: Recognition and conglutination separation of individual hens based on machine vision in complex environment. Trans. Chin. Soc. Agric. Mach. 44(4), 213–216 (2013)Google Scholar
  16. 16.
    Gaetano, R., et al.: Marker-controlled watershed-based segmentation of multiresolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 53(6), 2987–3004 (2015)CrossRefGoogle Scholar
  17. 17.
    Xu, L., Lu, H.: Automatic morphological measurement of the quantum dots based on marker-controlled watershed algorithm. IEEE Trans. Nanotechnol. 12(1), 51–56 (2013)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Wenyong Li
    • 1
    • 2
    Email author
  • Meixiang Chen
    • 2
  • Ming Li
    • 2
  • Chuanheng Sun
    • 2
  • Lin Wang
    • 3
  1. 1.Intelligent System DepartmentNational Engineering Research Center of Information Technology in AgricultureBeijingChina
  2. 2.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  3. 3.School of Electronic Information and AutomationTianjin University of Science and TechnologyTianjinChina

Personalised recommendations