Detecting the Honeydew of Common Pistachio Psylla Pest by Using Image Processing Technique

Document Type : Research Paper

Authors

1 Biosystems Engineering Dept. School of Agriculture, Shahid Bahonar University of Kerman

2 Dept. of Biosystems Engineering, School of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

3 Dept of Plant Pathology, School of Agriculture, Shahid Bahonar Unversity of Kerman, Kerman, Iran

Abstract

Pests and disease control have always been one of the main concerns and challenges of farmers and growers. The use of machine vision and image processing has greatly helped growers in pest management. The purpose of this study was to use image processing technique to detect honeydew produces by pistachio psylla and find the relation between the percent of leaf coverage by honeydew and pistachio psylla infestation. The leaves were collected from the research orchard with various infestation rates. Leaf samples were imaged by three cameras with 7, 13 and 20.7 MP resolutions at the same exposure conditions in imaging chamber with controlled lighting conditions. Images were processed in the Matlab R2019a using Watershed and Otsu segmentation algorithms to find the percentage of leaf surface covered by honeydew. The covered area was calculated using predefined functions in the image processing toolbox. A graphical user interface (GUI) was also designed to make the program more user friendly. Considering TPR mean value of 0.95 and total accuracy of 0.88 for watershed segmentation method showed its acceptable performance in discriminating honeydew out of other objects in images. Coefficient of determination and regression equation between pest population (obtained from manual count by expert) and percentage of leaf area covered by honeydew were obtained for different cameras. Camera with 20.7 MP resolution achieved the best performance with coefficient of determination 0.93 and regression equation y=1.03 x. The results from other cameras were also satisfactory.

Keywords

Main Subjects

References

Barbedo, J. G. A. (2014). Using digital image processing for counting whiteflies on soybean leaves. Journal of Asia-Pacific Entomology, 17(4), 685-694.
Brosnan, T., & Sun, D. W. (2004). Improving quality inspection of food products by computer vision––a review. Journal of Food Engineering, 61(1), 3-16.
Cho, J., Choi, J., Qiao, M., Ji, C. W., Kim, H. Y., Uhm, K. B., & Chon, T. S. (2007). Automatic identification of whiteflies, aphids and thrips in greenhouse based on image analysis. Red, 346(246), 244.
Gonzalez, R. C., & Woods, R. E., (2017). Digital Image Processing Global edition. India, Pearson.
Heidari, A. (2013). Digital image processing using MATLAB (5th ed). Iran, Beh avaran-kelk zarin. (In Farsi)
Maharlooei, M., Sivarajan, S., Bajwa, S. G., Harmon, J. P., & Nowatzki, J. (2017). Detection of soybean aphids in a greenhouse using an image processing technique. Computers and Electronics in Agriculture, 132, 63-70.
Mehrnezhad, M. (2014). The pests of pistachio trees in Iran, natural enemies and control (1st ed). Iran, Sepehr publication center. (In Farsi)
Mongkolchart, N., & Ketcham, M. (2014). The measurement of brown planthopper by image processing. In International Conference on Advanced Computational Technologies & Creative Media. 14-15 Aug. Pattaya, Thailand. pp. 102-105.
Omrani, A., Mohtasebi, S., Rafiee, Sh., Hosseinpur, S. & Aghili-nategh, N. (2014). Apple leaf diseases detection using image analysis techniques. In: Proceedings of 8th National Congress of Agricultural machinery (Biosystems Engineering) and mechanization. 2-4 Feb. Ferdowsi University of Mashhad, Mashhad, Iran. pp. 189-201. (In Farsi)
Qing, Y., Xian, D. X., Liu, Q. J., Yang, B. J., Diao, G. Q., & Jian, T. A. N. G. (2014). Automated counting of rice plant hoppers in paddy fields based on image processing. Journal of Integrative Agriculture, 13(8), 1736-1745.
Senthilnath, J., Dokania, A., Kandukuri, M., Ramesh, K. N., Anand, G., & Omkar, S. N. (2016). Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV. Biosystems Engineering, 146, 16-32.
Sunoj, S., Sivarajan, S., Maharlooei, M., Bajwa, S. G., Harmon, J. P., Nowatzki, J. F., & Cannayen, I. (2017). Identification and Counting of Soybean Aphids from Digital Images Using Shape Classification. Transactions of the ASABE, 60(5), 1467-1477.
Iranian Journal of Biosystems Engineering
Volume 51, Issue 4
February 2021
Pages 737-748

Files

History

  • Receive Date: 18 February 2020
  • Revise Date: 21 December 2020
  • Accept Date: 26 December 2020

Share

How to cite

Statistics