Academic Research Journal of
Agricultural Science and Research
Academic Research Journal of Agricultural Science and Research
Vol. 3(9), pp. 258-266. September, 2015.
Full Length Research
Computer Vision System Coupled with an Artificial Neural Network to Quality Evaluation of Rainbow Trout Eggs
Ghasem Bahrami1, Sajad Kiani2* and Hossein Rezai 3
1MSc Graduated Student of Mechanics of Agricultural Machinery, Faculty of Agriculture, Shiraz University, Fars, Iran.
2 Ph.D Candidate of Biosystems Engineering Dep. Tarbiat Modares University, Tehran, Iran.
*Corresponding authors; e-mails: email@example.com.
3Coach of Mechanics of Agricultural Machinery, Faculty of Agriculture, University of Malayer, Ilam, Iran.
Accepted 27 July 2015
in most of the proliferation and breeding sites of cold-water fishes has
been propagated and inbred. One of the proliferation steps of this type
of fishes is the separating fertile and living fish eggs from the
infertile or dead ones and counting them for sale. In spite of various
apparatuses and methods of proliferation, the recognition of fertile
from dead fish eggs is essential. In this study the ability of machine
vision system coupled with soft computing methods such as Artificial
Neural Networks (ANN) was examined to quality assessment of fish eggs.
In this regard, the captured images were transferred to the LAB color
domain, because this domain is less affected by the camera and lighting
conditions then several color and textural features were extracted from
the images of rainbow trout fish eggs. Finally extracted features were
introduced to ANN as an input layer. As a conclusion results showed that
with an optimum adjustment of ANN, the alive and dead fish eggs were
classified with 99% accuracy. The outcome of this investigation can be
used in the fish egg quality assessment.
How to cite this article: Bahrami G, Kiani S, Rezai H (2015). Computer Vision System Coupled with an Artificial Neural Network to Quality Evaluation of Rainbow Trout Eggs. Acad. Res. J. Agri. Sci. Res. 3(9): 258-266.