Automatic detection and counting of Callosobruchus maculatus (Fab.) Eggs on Vigna radiata seeds, using ImageJ

J. Georgina; C. H. Arun; M. Ramani Bai

doi:10.7324/JABB.2021.9219

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Abstract

Callosobruchus maculatus is a major pest of stored pulses causing huge loss especially during the post-harvest period. Counting of obscure C. maculatus eggs performed in research studies are related to pest-control, pest-status, population demography, reproductive parameters, and also in sampling procedures. Counting of numerous tiny eggs is always performed manually which is laborious, time consuming, and tedious. Therefore, an efficient, automated egg counting of C. maculatus, was performed with image processing techniques using ImageJ software. Batch processing of 60 digital images was executed with inclusion of preprocessing, thresholding and filtering using Band-pass, Mexican hat, and Fast filters of ImageJ software. In terms of accuracy, Band-pass method performed best with a mean percentage error difference of 15.55, while Mexican hat and Fast filters recorded 25.66 and 32.41, respectively. Pearson’s correlation was also highest (0.908) in Band-pass method. While comparing the execution time for the different methods, Fast Filter method showed highest percentage efficiency improvement of 65.53%. Egg counting time was 852 s in case of manual count but in automated count with Band-pass, Mexican hat, and Fast Filters, it was 41, 32, and 13 s, respectively. Laborious manual counting of C. maculatus eggs in future can be replaced by this automated procedure with good accuracy and rapid execution time.

Keyword: Callosobruchus maculatus Automated egg count Image processing Digital images Manual count.

Citation:

Georgina J, Arun CH, Ramani Bai M. Automatic detection and counting of Callosobruchus maculatus (Fab.) Eggs on Vigna radiata seeds, using ImageJ. J App Biol Biotech. 2021;9(2):182-186. https://doi.org10.7324/JABB.2021.9219

Copyright: Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.

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1. Stevenson PC, Green PW, Farrell IW, Brankin A, Mvumi BM, Belmain SR. Novel agmatine derivatives in Maerua edulis with bioactivity against Callosobruchus maculatus, a cosmopolitan storage insect pest. Front Plant Sci 2018;9:1506. https://doi.org/10.3389/fpls.2018.01506
2. Yamane T, Goenaga J, Rönn JL, Arnqvist G. Male seminal fluid substances affect sperm competition success and female reproductive behavior in a seed beetle. PLoS One 2015;10:e0123770. https://doi.org/10.1371/journal.pone.0123770
3. Mkenda PA, Stevenson PC, Ndakidemi P, Farman DI, Belmain SR. Contact and fumigant toxicity of five pesticidal plants against Callosobruchus maculatus (Coleoptera: Chrysomelidae) in stored cowpea (Vigna unguiculata). Int J Trop Insect Sci 2015;35:172-84. https://doi.org/10.1017/S174275841500017X
4. Credland PF, Wright AW. Factors affecting female fecundity in the cowpea seed beetle, Callosobruchus maculatus (Coleoptera: Bruchidae). J Stored Prod Res 1989;25:125-36. https://doi.org/10.1016/0022-474X(89)90034-9
5. Wang MH, Horng SB. Egg dumping and life history strategy of Callosobruchus maculatus. Physiol Entomol 2004;29:26-31. https://doi.org/10.1111/j.0307-6962.2004.0357.x
6. Giga DP, Canhão J. interference of oviposition behaviour in the cowpea weevils Callosobruchus rhodesianus (Pic) and Callosobruchus maculatus (F.). Int J Trop Insect Sci 1997;17:251-5. https://doi.org/10.1017/S1742758400016441
7. Akintayo A, Tylka GL, Singh AK, Ganapathysubramanian B, Singh A, Sarkar S. A deep learning framework to discern and count microscopic nematode eggs. Sci Rep 2018;8:1-11. https://doi.org/10.1038/s41598-018-27272-w
8. Arena ET, Rueden CT, Hiner MC, Wang S, Yuan M, Eliceiri KW. Quantitating the cell: Turning images into numbers with ImageJ. Wiley Interdiscip Rev Dev Biol 2017;6:e260. https://doi.org/10.1002/wdev.260
9. Nouhaud P, Mallard F, Poupardin R, Barghi N, Schlötterer C. High-throughput fecundity measurements in Drosophila. Sci Rep 2018;8:1-6. https://doi.org/10.1038/s41598-018-22777-w
10. Colas F, Tardivel M, Perchoc J, Lunven M, Forest B, Guyader G, et al. The ZooCAM, a new in-flow imaging system for fast onboard counting, sizing and classification of fish eggs and metazooplankton. Prog Oceanogr 2018;166:54-65. https://doi.org/10.1016/j.pocean.2017.10.014
11. Mussadiq Z, Laszlo B, Helyes L, Gyuricza C. Evaluation and comparison of open source program solutions for automatic seed counting on digital images. Comput Electron Agric 2015;117:194-9. https://doi.org/10.1016/j.compag.2015.08.010
12. Fernandez-Gallego JA, Kefauver SC, Gutiérrez NA, Nieto- Taladriz MT, Araus JL. Wheat ear counting in-field conditions: High throughput and low-cost approach using RGB images. Plant Methods 2018;14:22. https://doi.org/10.1186/s13007-018-0289-4
13. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods 2012;9:671-5. https://doi.org/10.1038/nmeth.2089
14. Young K, Morrison H. Quantifying microglia morphology from photomicrographs of immunohistochemistry prepared tissue using ImageJ. J Vis Exp 2018;5:57648. https://doi.org/10.3791/57648
15. Karpova EK, Komyshev EG, Genaev MA, Adonyeva NV, Afonnikov DA, Eremina MA, et al. Quantifying Drosophila adults with the use of a smartphone. Biol Open 2020;9:bio054452. https://doi.org/10.1242/bio.054452
16. Grishagin IV. Automatic cell counting with ImageJ. Anal Biochem 2015;473:63-5. https://doi.org/10.1016/j.ab.2014.12.007
17. Cadena-Herrera D, Esparza-De Lara JE, Ramírez-Ibañez ND, López-Morales CA, Pérez NO, Flores-Ortiz LF, et al. Validation of three viable-cell counting methods: Manual, semi-automated, and automated. Biotechnol Rep 2015;1:9-16. https://doi.org/10.1016/j.btre.2015.04.004

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1. Stevenson PC, Green PW, Farrell IW, Brankin A, Mvumi BM, Belmain SR. Novel agmatine derivatives in Maerua edulis with bioactivity against Callosobruchus maculatus, a cosmopolitan storage insect pest. Front Plant Sci 2018;9:1506. https://doi.org/10.3389/fpls.2018.01506

2. Yamane T, Goenaga J, Rönn JL, Arnqvist G. Male seminal fluid substances affect sperm competition success and female reproductive behavior in a seed beetle. PLoS One 2015;10:e0123770. https://doi.org/10.1371/journal.pone.0123770

3. Mkenda PA, Stevenson PC, Ndakidemi P, Farman DI, Belmain SR. Contact and fumigant toxicity of five pesticidal plants against Callosobruchus maculatus (Coleoptera: Chrysomelidae) in stored cowpea (Vigna unguiculata). Int J Trop Insect Sci 2015;35:172-84. https://doi.org/10.1017/S174275841500017X

4. Credland PF, Wright AW. Factors affecting female fecundity in the cowpea seed beetle, Callosobruchus maculatus (Coleoptera: Bruchidae). J Stored Prod Res 1989;25:125-36. https://doi.org/10.1016/0022-474X(89)90034-9

5. Wang MH, Horng SB. Egg dumping and life history strategy of Callosobruchus maculatus. Physiol Entomol 2004;29:26-31. https://doi.org/10.1111/j.0307-6962.2004.0357.x

6. Giga DP, Canhão J. interference of oviposition behaviour in the cowpea weevils Callosobruchus rhodesianus (Pic) and Callosobruchus maculatus (F.). Int J Trop Insect Sci 1997;17:251-5. https://doi.org/10.1017/S1742758400016441

7. Akintayo A, Tylka GL, Singh AK, Ganapathysubramanian B, Singh A, Sarkar S. A deep learning framework to discern and count microscopic nematode eggs. Sci Rep 2018;8:1-11. https://doi.org/10.1038/s41598-018-27272-w

8. Arena ET, Rueden CT, Hiner MC, Wang S, Yuan M, Eliceiri KW. Quantitating the cell: Turning images into numbers with ImageJ. Wiley Interdiscip Rev Dev Biol 2017;6:e260. https://doi.org/10.1002/wdev.260

9. Nouhaud P, Mallard F, Poupardin R, Barghi N, Schlötterer C. High-throughput fecundity measurements in Drosophila. Sci Rep 2018;8:1-6. https://doi.org/10.1038/s41598-018-22777-w

10. Colas F, Tardivel M, Perchoc J, Lunven M, Forest B, Guyader G, et al. The ZooCAM, a new in-flow imaging system for fast onboard counting, sizing and classification of fish eggs and metazooplankton. Prog Oceanogr 2018;166:54-65. https://doi.org/10.1016/j.pocean.2017.10.014

11. Mussadiq Z, Laszlo B, Helyes L, Gyuricza C. Evaluation and comparison of open source program solutions for automatic seed counting on digital images. Comput Electron Agric 2015;117:194-9. https://doi.org/10.1016/j.compag.2015.08.010

12. Fernandez-Gallego JA, Kefauver SC, Gutiérrez NA, Nieto- Taladriz MT, Araus JL. Wheat ear counting in-field conditions: High throughput and low-cost approach using RGB images. Plant Methods 2018;14:22. https://doi.org/10.1186/s13007-018-0289-4

13. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods 2012;9:671-5. https://doi.org/10.1038/nmeth.2089

14. Young K, Morrison H. Quantifying microglia morphology from photomicrographs of immunohistochemistry prepared tissue using ImageJ. J Vis Exp 2018;5:57648. https://doi.org/10.3791/57648

15. Karpova EK, Komyshev EG, Genaev MA, Adonyeva NV, Afonnikov DA, Eremina MA, et al. Quantifying Drosophila adults with the use of a smartphone. Biol Open 2020;9:bio054452. https://doi.org/10.1242/bio.054452

16. Grishagin IV. Automatic cell counting with ImageJ. Anal Biochem 2015;473:63-5. https://doi.org/10.1016/j.ab.2014.12.007

17. Cadena-Herrera D, Esparza-De Lara JE, Ramírez-Ibañez ND, López-Morales CA, Pérez NO, Flores-Ortiz LF, et al. Validation of three viable-cell counting methods: Manual, semi-automated, and automated. Biotechnol Rep 2015;1:9-16. https://doi.org/10.1016/j.btre.2015.04.004

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