DOI : https://doi.org/10.18517/ijaseit.10.4.10188

Algorithm for an Automated Clarias gariepinus Fecundity Estimation Technique Using Spline Interpolation and Gaussian Quadrature

Abdul Aziz K Abdul Hamid (1), Norfazlina Amirudin (2), Masduki Mohammad Morni (3), Sumazly Sulaiman (4)
(1) School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
(2) School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
(3) Centre for Foundation and Liberal Education, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
(4) School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
Fulltext View | Download
How to cite (IJASEIT) :
[1]
A. A. K. Abdul Hamid, N. Amirudin, M. Mohammad Morni, and S. Sulaiman, “Algorithm for an Automated Clarias gariepinus Fecundity Estimation Technique Using Spline Interpolation and Gaussian Quadrature”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 4, pp. 1465–1470, Aug. 2020.
Citation Format :
Fecundity is essential in the field of population ecology, where the number of eggs is measured to get the actual reproductive rate of an organism. The estimation of fecundity is essential for an accurate study of biology and population dynamics of fish species. This estimation can be developed using the gravimetric method (weight method) to calculate the number of eggs. However, this method still requires experienced technicians and much time and effort to compute the number of eggs manually. The increasing growth in both hardware and software have led to many improvements in imaging technology. Hence, this research addresses the problem of employing constructing a computer vision algorithm. This paper introduced the automatic fecundity estimation method, which applied simple mathematic theories and image processing algorithm to estimate the fecundity of African catfish (Clarias gariepinus). From the image of the fish, the fish’s eye was be detected using a modified Haar Cascade Classifier Algorithm and appointed axis line where the eye becomes the origin point. Next, we identify the region of interest, which reflects the fish's fecundity to obtain the pixels corresponding to the silhouette of the region as coordinates in Euclidean space, which are then represented with a function using cubic interpolation function. Using this function, we compute the region of interest using an integral numerical approach, e.g., Gaussian Quadrature. From the result, we compared with the ground truth to get the estimation of the number of eggs.

K. Bithy, M. I. Miah, M. S. Haque, K. R. Hasan, and M. F. Islam, “Estimation of the fecundity of Jat Puti , Puntius sophore ( Hamilton ),” J. Environ. Sci. Nat. Resour., vol. 5, no. 2, pp. 295-300, 2012.

J. Ekasari et al., “Biofloc technology application in African catfish fingerling production: The effects on the reproductive performance of broodstock and the quality of eggs and larvae,” Aquaculture, vol. 464, pp. 349-356, 2016.

D. Admassu, L. Abera, and Z. Tadesse, “Fecundity and breeding season of the African Catfish , Clarias gariepinus ( Burchell ), in Lake Babogaya ,” vol. 3, no. 8, pp. 295-303, 2015.

A. Biswas and S. Ghosh, “Estimation of Fecundity,” 2015.

H. Murua, G. Kraus, F. Saborido-Rey, P. R. Witthames, A. Thorsen, and S. Junquera, “Procedures to estimate fecundity of marine fish species in relation to their reproductive strategy,” J. Northwest Atl. Fish Sci. J.Northw.Atl.Fish Sci., vol. 33, no. December, pp. 33-54, 2003.

H. Diaz, J. Conde, and M. Bevilacqua, “A Volumetric Method for Estimating Fecundity in Decapoda ,” Mar. Ecol. Prog. Ser., vol. 10, pp. 203-206, 1983.

J. M. Pintor et al., “Govocitos: A software tool for estimating fish fecundity based on digital analysis of histological images,” Comput. Electron. Agric., vol. 125, pp. 89-98, 2016.

K. D. Friedland, D. Ama-Abasi, M. Manning, L. Clarke, G. Kligys, and R. C. Chambers, “Automated egg counting and sizing from scanned images: Rapid sample processing and large data volumes for fecundity estimates,” J. Sea Res., vol. 54, no. 4, pp. 307-316, 2005.

C. A. B. Mello, W. P. dos Santos, M. A. B. Rodrigues, A. L. B. Candeilas, C. M. G. Gusmao, and N. M.Portela, :“Automatic Counting of Aeeds aegypti Eggs in images of Ovitrap,” Recent Adv. Biomed. Eng., vol. 3, no. 5, pp. 211-221, 2009.

P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” Proc. 2001 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition. CVPR 2001, vol. 1, pp. I-511-I-518, 2001.

R. Lienhart and J. Maydt, “An extended set of Haar-like features for rapid object detection,” Proceedings. Int. Conf. Image Process., vol. 1, pp. I-900-I-903, 2002.

P. I. Wilson and J. Fernandez, “Facial Feature Detection Using Haar Classifiers,” J. Comput. Sci. Coll., vol. 21, no. 4, pp. 127-133, 2006.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).