An Approach for Counting Breeding Eels Using Mathematical Morphology Operations and Boundary Detection

Tran, An Cong; Chau, Anh Nhut Nguyen; Tran, Nghi Cong; Nguyen, Hai Thanh

doi:10.2478/acss-2022-0012

Abstract

The Mekong Delta region of Vietnam has great potential for agricultural development thanks to natural incentives. Many livestock industries have developed for a long time and play an important role in the country with many agricultural export products. In the era of breakthrough technologies and advances in information technology, many techniques are used to support the development of smart agriculture. In particular, computer vision techniques are widely applied to help farmers save a lot of labour and cost. This study presents an approach for counting eels based on Mathematical Morphology Operations and Boundary Detection from images of breeding eels captured with the proposed photo box. The proposed method is evaluated using data collected directly from a breeding eel farm in Vietnam. The authors of the research evaluate and investigate the length distribution of eels to select the appropriate size for counting tasks. The experiments show positive results with an average Mean Absolute Error of 2.2 over a tray of more than 17 eels. The contribution of the research is to provide tools to support farmers in eel farms to save time and effort and improve efficiency.

References

[1] N. T. Tuan, “An overview of the anguillid eel culture in Vietnam,” Journal of Aquaculture and Marine Biology, vol. 10, no. 3, pp. 96–101, Jun. 2021. https://doi.org/10.15406/jamb.2021.10.00312
Search in Google Scholar Back to article
[2] G. Farjon, O. Krikeb, A.B. Hillel, and V. Alchanatis, “Detection and counting of flowers on apple trees for better chemical thinning decisions,” Precision Agric, vol. 21, pp. 503–521, 2020. https://doi.org/10.1007/s11119-019-09679-1
Search in Google Scholar Back to article
[3] B. T. Kitano, C. C. T. Mendes, A. R. Geus, H. C. Oliveira, and J. R. Souza, “Corn plant counting using deep learning and UAV images,” IEEE Geoscience and Remote Sensing Letters, pp. 1–5, Aug. 2019. https://doi.org/10.1109/LGRS.2019.2930549
Search in Google Scholar Back to article
[4] M. Machefer, F. Lemarchand, V. Bonnefond, A. Hitchins, and P. Sidiropoulos, “Mask r-CNN refitting strategy for plant counting and sizing in UAV imagery,” Remote Sensing, vol. 12, no. 18, Art. no. 3015, Sep. 2020. https://doi.org/10.3390/rs12183015
Search in Google Scholar Back to article
[5] D. Li, Z. Miao, F. Peng, L. Wang, Y. Hao, Z. Wang, T. Chen, H. Li, and Y. Zheng, “Automatic counting methods in aquaculture: A review,” Journal of the World Aquaculture Society, vol. 52, no. 2, pp. 269–283, Apr. 2021. https://doi.org/10.1111/jwas.12745
Search in Google Scholar Back to article
[6] Q. Zhang, Y. Liu, C. Gong, Y. Chen, and H. Yu, “Applications of deep learning for dense scenes analysis in agriculture: A review,” Sensors, vol. 20, no. 5, Art. no. 1520, Mar. 2020. https://doi.org/10.3390/s20051520708550532164200
Search in Google Scholar Back to article
[7] Y. Zhong, J. Gao, Q. Lei, and Y. Zhou, “A vision-based counting and recognition system for flying insects in intelligent agriculture,” Sensors, vol. 18, no. 5, Art. no. 1489, May 2018. https://doi.org/10.3390/s18051489598214329747429
Search in Google Scholar Back to article
[8] Q.-J. Wang, S.-Y. Zhang, S.-F. Dong, G.-C. Zhang, J. Yang, R. Li, and H.-Q. Wang, “Pest24: A large-scale very small object data set of agricultural pests for multi-target detection,” Computers and Electronics in Agriculture, vol. 175, Art. no. 105585, Aug. 2020. https://doi.org/10.1016/j.compag.2020.105585
Search in Google Scholar Back to article
[9] M. Tian, H. Guo, H. Chen, Q. Wang, C. Long, and Y. Ma, “Automated pig counting using deep learning,” Computers and Electronics in Agriculture, vol. 163, Art. no. 104840, Aug. 2019. https://doi.org/10.1016/j.compag.2019.05.049
Search in Google Scholar Back to article
[10] S. Xie, R. Girshick, P. Dolla´r, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” arXiv, 2016. [Online]. Available: https://arxiv.org/abs/1611.0543110.1109/CVPR.2017.634
Search in Google Scholar Back to article
[11] W. Li, P. Chen, B. Wang, and C. Xie, “Automatic localization and count of agricultural crop pests based on an improved deep learning pipeline,” Scientific Reports, vol. 9, Art. no. 7024, May 2019. https://doi.org/10.1038/s41598-019-43171-0650493731065055
Search in Google Scholar Back to article
[12] E. A. Awalludin, T. N. T. Arsad, and W. N. J. H. W. Yussof, “A review on image processing techniques for fisheries application,” Journal of Physics: Conference Series, vol. 1529, no. 5, Art. no. 052031, May 2020. https://doi.org/10.1088/1742-6596/1529/5/052031
Search in Google Scholar Back to article
[13] D. Li and L. Du, “Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish,” Artificial Intelligence Review, vol. 55, pp. 4077–4116, Nov. 2021. https://doi.org/10.1007/s10462-021-10102-3
Search in Google Scholar Back to article
[14] J. Hu, D. Li, Q. Duan, Y. Han, G. Chen, and X. Si, “Fish species classification by color, texture and multi-class support vector machine using computer vision,” Computers and Electronics in Agriculture, vol. 88, pp. 133–140, Oct. 2012. https://doi.org/10.1016/j.compag.2012.07.008
Search in Google Scholar Back to article
[15] L. Li and J. Hong, “Identification of fish species based on image processing and statistical analysis research,” in 2014 IEEE International Conference on Mechatronics and Automation, Tianjin, China, Aug. 2014, pp. 1155–1160. https://doi.org/10.1109/ICMA.2014.6885861
Search in Google Scholar Back to article
[16] K. M. Knausgård, A. Wiklund, T. K. Sørdalen, K. T. Halvorsen, A. R. Kleiven, L. Jiao, and M. Goodwin, “Temperate fish detection and classification: a deep learning based approach,” Applied Intelligence, vol. 52, no. 6, pp. 6988–7001, Mar. 2021. https://doi.org/10.1007/s10489-020-02154-9
Search in Google Scholar Back to article
[17] M. Jahanbakht, W. Xiang, N. J. Waltham, and M. R. Azghadi, “Distributed deep learning in the cloud and energy-efficient real-time image processing at the edge for fish segmentation in underwater videos,” IEEE Access, vol. 10, pp. 117796–117807, Aug. 2022. https://doi.org/10.1109/ACCESS.2022.3202975
Search in Google Scholar Back to article
[18] S. Saputra, A. Yudhana, and R. Umar, “Implementation of naïve Bayes for fish freshness identification based on image processing,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 3, pp. 412–420, Jun. 2022. https://doi.org/10.29207/resti.v6i3.4062
Search in Google Scholar Back to article
[19] A. M. A. Aziz, I. Ahmad, S. M. M. Maharum, and Z. Mansor, “The development of a fish counting monitoring system using image processing,” in Advanced Materials and Engineering Technologies. Advanced Structured Materials, A. Ismail, W.M. Dahalan, and A. Öchsner, Eds. Springer, Cham, 2022, pp.255–262. https://doi.org/10.1007/978-3-030-92964-0_25
Search in Google Scholar Back to article
[20] L. T. M. D. Braga, A. Giraldo, and A. L. Godinho, “Evaluation of three methods for manually counting fish in dam turbines using DIDSON,” Hydrobiologia, vol. 849, pp. 309–321, May 2021. https://doi.org/10.1007/s10750-021-04605-x
Search in Google Scholar Back to article
[21] C. S. Costa, V. A. G. Zanoni, L. R. V. Curvo, M. de Arau´jo Carvalho, W. R. Boscolo, A. Signor, M. dos Santos de Arruda, H. H. P. Nucci, J. M. Junior, W. N. Gonc¸alves, O. Diemer, and H. Pistori, “Deep learning applied in fish reproduction for counting larvae in images captured by smartphone,” Aquacultural Engineering, vol. 97, Art. no. 102225, May 2022. https://doi.org/10.1016/j.aquaeng.2022.102225
Search in Google Scholar Back to article
[22] Y. Wageeh, H. E.-D. Mohamed, A. Fadl, O. Anas, N. ElMasry, A. Nabil, and A. Atia, “YOLO fish detection with euclidean tracking in fish farms,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 5–12, Jan. 2021. https://doi.org/10.1007/s12652-020-02847-6
Search in Google Scholar Back to article
[23] J.-T. Yu, R.-S. Jia, Y.-C. Li, and H.-M. Sun, “Automatic fish counting via a multi-scale dense residual network,” Multimedia Tools and Applications, vol. 81, pp. 17223–17243, Mar. 2022. https://doi.org/10.1007/s11042-022-12672-y
Search in Google Scholar Back to article
[24] J. Wu, Y. Zhou, H. Yu, Y. Zhang, and J. Li, “A novel fish counting method with adaptive weighted multi-dilated convolutional neural net-work,” in 2021 20th International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS), London, United Kingdom, Dec. 2021, pp. 178–183. https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00039
Search in Google Scholar Back to article
[25] T. H. Khai, S. N. H. S. Abdullah, M. K. Hasan, and A. Tarmizi, “Underwater fish detection and counting using mask regional convolutional neural network,” Water, vol. 14, no. 2, Art. no. 222, Jan. 2022. https://doi.org/10.3390/w14020222
Search in Google Scholar Back to article
[26] N. Garcia-d’Urso, A. Galan-Cuenca, P. Pérez-Sánchez, P. Climent-Pérez, A. Fuster-Guillo, J. Azorin-Lopez, M. Saval-Calvo, J. E. Guillén-Nieto, and G. Soler-Capdepón, “The DeepFish computer vision dataset for fish instance segmentation, classification, and size estimation,” Scientific Data, vol. 9, Art. no. 287, Jun. 2022. https://doi.org/10.1038/s41597-022-01416-0
Search in Google Scholar Back to article
[27] P. Schober, M. H. Najafi, and N. Taherinejad, “High-accuracy multiply-accumulate (MAC) technique for unary stochastic computing,” IEEE Transactions on Computers, vol. 71, no. 6, pp. 1425–1439, Jun. 2022. https://doi.org/10.1109/tc.2021.3087027
Search in Google Scholar Back to article
[28] MathWorks team, “Bioinformatics Toolbox: User’s Guide (R2022a): Types of Morphological Operations - MATLAB & Simulink.” MathWorks [Online]. Available: https://www.mathworks.com/help/images/morphological-dilationand-erosion.html
Search in Google Scholar Back to article
[29] E. R. Dougherty, An Introduction to Morphological Image Processing. Bellingham, WA: SPIE Press, Jan. 1992.
Search in Google Scholar Back to article
[30] J. J. Serra, Image Analysis and Mathematical Morphology. San Diego, CA: Academic Press, 1983.
Search in Google Scholar Back to article
[31] W.-J. Kim, S.-D. Kim, and K. Kim, “Fast algorithms for binary dilation and erosion using run-length encoding,” ETRI Journal, vol. 27, no. 6, pp. 814–817, Dec. 2005. https://doi.org/10.4218/etrij.05.0205.0013
Search in Google Scholar Back to article
[32] K. Valladares-Yánez, A. Monroy-Meza, R. Suárez-Rivera, J. Rodriguez-Reséndiz, G. Pérez-Soto, and K. Camarillo-Gómez, “Development and implementation of a vision system for decision making in the movements control of humanoid robots,” in 2018 XX Congreso Mexicano de Robótica (COMRob), Ensenada, Mexico, Sep 2018, pp. 1–6. https://doi.org/10.1109/comrob.2018.8689417
Search in Google Scholar Back to article
[33] M. Kahra, V. Sridhar, and M. Breuß, “Fast morphological dilation and erosion for grey scale images using the Fourier transform,” in Scale Space and Variational Methods in Computer Vision. SSVM 2021. Lecture Notes in Computer Science, A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, and L. Simon, Eds., vol 12679. Springer, Cham. Apr. 2021, pp. 65–77. https://doi.org/10.1007/978-3-030-75549-2_6
Search in Google Scholar Back to article

An Approach for Counting Breeding Eels Using Mathematical Morphology Operations and Boundary Detection

Abstract

Paradigm

My account