Identification of Idiocerus stali (Hem.: Cicadellidae) Using Image Processing and Artificial Neural Networks

Document Type : Research Article

Authors

• Z. Azizpour ¹
• H. Vahedi ¹
• A. N. Lorestani ²

¹ Plant Protection Department, Agricultural Faculty, Razi University, Kermanshah, Iran

² Mechanical Engineering of Biosystems Department, Agricultural Faculty, Razi University, Kermanshah, Iran

https://doi.org/10.22067/jam.v12i2.81004

Abstract

Introduction
Pistachio or Green Gold is one of the most important agricultural crops and is especially important for Iranian exports. A group of pistachio's pests mainly feed on pistachio, among which Idiocerus stali is very important. Conventional methods for identifying insects using identification keys are time-consuming and costly. Due to the rapid development of the Pistachio industry, the use of artificial intelligence techniques such as image processing, for identification and population monitoring is highly recommended. On the other hand, little research was carried out on I. stali. Therefore, in this research, I. stali was selected as a target insect for the identification and counting on sticky yellow cards using image processing techniques and artificial neural networks. The purpose of this study was to determine the feasibility of I. stali identification algorithm by image processing, to determine the possibility of separation and counting of I. stali from other non-target insects by artificial neural network and to determine its accuracy in identification of I. Stali.
Materials and Methods
Idiocerus stali was selected as the target insect for identification. Sticky yellow cards were used for collecting samples. Taking the photos with the help of a SONY Handycam Camera, which had a 12-megapixel resolution and G lens, was carried out (SONY, HDR-XR500, CMOS, SONY Lens G, Made in Japan). Then insects were counted on each card manually and the data was recorded. The data, which were digital images of yellow sticky cards, were imported into the MatLab R2017b software environment. A total of 357 color properties and 20 shape's features for the identification of I. stali were extracted by an image processing algorithm. Color properties were divided into two categories of mean and standard deviation and characteristics related to vegetation indices. An ANN-PSO (Artificial Neural Network hybrid method-Particle Swarm Optimization) algorithm was used to select the effective features. The selected effective characteristics for insect classification were: Color index for extra collective vegetation related to HSL color space, normalized difference index for LCH color space, gray channel for color space YCbCr, second component index minus third component for color space YCbCr, area and mean of the first, second and third components of color space Luv.
Results and Discussion
Comparing the results with the results of Qiao et al. (2008), we found that in his study, which divided the data into three categories, for medium and high-density groups, the detection rate was 95.2% and 94.6%, respectively. On the other hand, in low densities (less than 10 trapped insects); its detection rate was 72.9%, while the detection rate of the classifier system designed in this study for different densities of trapped insects, was identical and equal to 99.59%. Also, comparing the results of this study with Espinoza et al. (2016), we found that their algorithm in whiteflies detection had a high accuracy of about 0.96 on a sticky yellow card, while the Thrips identification algorithm accuracy was 0.92 on a sticky blue card. As stated above, the correct detection rate of I. stali by the algorithm designed in this study was 99.72%.
Conclusion
The results showed the feasibility of the new method for identifying the pest insects without destroying them on the farm and in natural light conditions and in a short time and with very high accuracy. This suggests that this algorithm can be applied to the machine vision system and can be used in future in the construction of agricultural robots.

Keywords

• Artificial Neural Network
• Classification
• Identification
• Image processing

20.1001.1.22286829.1401.12.2.1.3

Main Subjects

• Image Processing

Open Access

©2022 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1. Baiocchi, D. 2013. The Anthaxia (Anthaxia) manca (Linnaeus, 1767) species-group in Iran, with description of a new species and a new synonymy (Coleoptera: Buprestidae). Zootaxa 3616: 455-481.
2. Barbedo, J. G. A. 2014. Using digital image processing for counting whiteflies on soybean leaves. Journal of Asia-Pacific Entomology 17: 685-694.
3. Boissard, P., V. Martin, and S. Moisan. 2008. A cognitive vision approach to early pest detection in greenhouse crops. Computers and Electronics in Agriculture 62: 81-93.
4. Cho, J., J. Choi, M. Qiao, C.-W. Ji, H.-Y. Kim, K.-B. Uhm, and T.-S. Chon. 2007. Automatic identification of whiteflies, aphids and thrips in greenhouse based on image analysis. International Journal of Mathematics and Computers in Simulation 7 (1): 46-53.
5. Espinoza, K., D. L. Valera, J. A. Torres, A. López, and F. D. Molina-Aiz. 2016. Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture. Computers and Electronics in Agriculture 127: 495-505.
6. FAO. 2017. Food and agriculture organization of the United Nations. Retrieved May 16, 2019; Available at: http://fao.org/faostat/en/#data.
7. Ghaiwat, S. N. and P. Arora. 2014. Detection and classification of plant leaf diseases using image processing techniques: A review. International Journal of Recent Advances in Engineering and Technology 2 (3): 1-7.
8. Gonzalez, R. C., and R. E. Woods. 2018. Digital Image Processing, 4th ed. Pearson, 1306 pp., New York, USA.
9. Gonzalez, R. C., R. E. Woods, and S. L. Eddins. 2004. Digital Image Processing Using MATLAB, (Second ed.).USA. New Jersey: Pearson Prentice Hall.
10. Heidari, A., and J. Amiri Parian. 2019. Identification and Counting of Two Important Greenhouse Pests by Image Processing, Case Study: Whitefly and Thrips. Journal of Agricultural Machinery 9 (2): 309-320. (In Persian).
11. Jalilvand, N. and S. Kashani-Zadeh. 2013. Study on the Biology of Idiocerus stali in Qazvin Climate. Agricultural Research and Natural Resources Journal 3 (15): 22-30. (In Persian).
12. Karbasi, A. R., and A. H. Taohidi. 2015. Factors Affecting Iranian Pistachio Export. Agricultural Economics Research 7 (1): 91-112. (In Persian).
13. Kazmi, W., F. J. Garcia-Ruiz, J. Nielsen, J. Rasmussen, and H. J. Andersen. 2015. Detecting creeping thistle in sugar beet fields using vegetation indices. Computers and Electronics in Agriculture 112: 10-19.
14. Kennedy, J., and R. Eberhart. 1995. Particle Swarm Optimization, 1942-1948 pp., Proceedings of the International Conference on Neural Networks, 27 Nov-1 Dec. 1995, Perth, Australia.
15. Kia, M. 2009. Neural Networks, (Second ed.) Tehran, Iran: Kian Rayaneh Sabz Publisher. 232 pp. (In Persian).
16. Mehrnejad, M. R. 2001. The current status of pistachio pests in Iran. In: Proceedings of the XI GREMPA Seminar on Pistachios and Almonds. (Cahiers Options Méditerranéennes; n. 56). Zaragoza: CIHEAM. 315-322. Retrieved November 21, Available at: http://om.ciheam.org/option.php?IDOM=769.
17. Modarres Awal, M. 2009. Classification of insects, (1st ed.). Mashhad, Iran: Ferdowsi University Publisher. (In Persian).
18. Mozaffarian, F., and M. R. Wilson. 2016. A checklist of the leafhoppers of Iran (Hemiptera: Auchenorrhyncha: Cicadellidae). Zootaxa, 4062 (1): 1-63.
19. Panduranga Vitala, T., G. S. V. Prasada Rajub, K. Sreeramamurthy, and V. P. Venkata Charan. 2015. A Probabilistic Neural Network Approach for Classification of Datasets Collected From North Coastal Districts of AP, India Using MatLab 715-721 pp., Proceedings of the International Conference on Computer, Communication and Convergence (ICCC 2015), Odisha, India.
20. Qiao, M., J. Lim, C. W. Ji, B. K. Chung, H. Y. Kim, K. B. Uhm, C. S. Myung, J. Cho, T. S. Chon, D. Herák, A. K., A. Sedláček, and G. Gűrdil. 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: 25-29.
21. Šimundić, A. M. 2009. Measures of Diagnostic Accuracy: Basic Definitions. The Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (EJIFCC) 19: 203-211.
22. Solomon, C., and T. Breckon. 2011. Fundamentals of Digital Image Processing. (First ed.). West Sussex, UK: John Wiley & Sons, Ltd, Publication, 328 pp.
23. Wisaeng, K. 2013. A Comparison of Decision Tree Algorithms for UCI Repository Classification. International Journal of Engineering Trends and Technology (IJETT) 4: 3393-3397.