Designing Fish Optic Mobile Application for Fish Disease Identification

Authors

  • Romiza Md Nor Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Muhammad Amin Abdullah Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nurul Syafiqah Aminuddin Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nur Shahidatul Shaurah Sharifunizam Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Aliaa Zafirah Zainal Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Huzaifah A Hamid Universiti Teknologi MARA, Perlis Branch, Arau Campus

DOI:

https://doi.org/10.24191/jcrinn.v7i1.278

Keywords:

fish optic, SDLC, HCI, mobile application

Abstract

The signs and symptoms of fish disease can be traced by checking on the eye surface which is the cornea of fisheye. The Fish Optic mobile application aims to help students study the fisheye anatomy and to trace the symptoms of diseases on fish. The Fish Optic user mobile application uses Human-Centered System Development Life Cycle (HCSDLC) which consists of four phases which are project selection and planning, analysis, design and implementation. As HCSDLC emphasizes on user involvement throughout all phases, an interview was conducted, and a post task walkthrough was performed. User Acceptance Test formative evaluation was then conducted by distributing questionnaire. Some recommendations are also discussed for future works to improve and refine the design of the Fish Optic mobile application to enhance user experience. It can be concluded that using HCSDLC method throughout the design of Fish Optic mobile application contributes to a well-defined systems requirement to support user needs and to accommodate the lack of human understanding that frustrates users in their daily routines.

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References

Chakravorty, H. (2020). To detection of fish disease using augmented reality and image processing. Advances in Image and Video Processing, Volume8, (1), 1-4.

Chebet, L. (2010). “Rapid” (alternative) methods for evaluation of fish freshness and quality

ElBatsh, K. (2020). Developing Fish Recognition Mobile Application “WiKiFish” Part of the proposed Model for Fish Market Management System (SAMAKA).

Liliana Blondina Athanasopoulos, E. M. (2018). Studies on The Use of New Methods in View of The Early Diagnosis of Fish Diseases. Research Journal of Agricultural Science

Olafsdottir, G., Martinsdóttir, E., Oehlenschläger, J., Dalgaard, P., Jensen, B., Undeland, I. & Nilsen, H. (1997). Methods to evaluate fish freshness in research and industry. Trends in food science & technology, 8(8), 258-265.

Nguyen Ngoc, H., Lasa, G., & Iriarte, I. (2022). Human-centred design in industry 4.0: case study review and opportunities for future research. Journal of Intelligent Manufacturing, 33(1), 35-76.

Rossi, F., Benso, A., Di Carlo, S., Politano, G., Savino, A., & Acutis, P. L. (2016, May). FishAPP: A mobile App to detect fish falsification through image processing and machine learning techniques. In 2016 IEEE international conference on automation, quality and testing, robotics (AQTR) (pp. 1-6). IEEE.

So, Y. (2017). Designing for mobile apps: Overall principles, common patterns, and interface guidelines.

Valentin Lyubchenko, R. M. (2016). Digital Image Processing Techniques for Detection and Diagnosis of Fish Diseases. International Journal of Advanced Research in Computer Science and Software Engineering

Visual 3D Science. (2020). My Eye Anatomy. Retrieved from https://play.google.com/store/apps/details id=com.visual3dscience.Eye&hl=en&gl=US

Zhang, P., Carey, J., Te'eni, D., & Tremaine, M. (2005). Integrating human computer interaction development into the systems development life cycle: A methodology. Communications of the Association for Information Systems, 15(1), 29.

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Published

2022-03-30

How to Cite

Md Nor, R., Abdullah, M. A. ., Aminuddin, N. S. ., Sharifunizam, N. S. S. ., Zainal, A. Z. ., & A Hamid, H. . (2022). Designing Fish Optic Mobile Application for Fish Disease Identification. Journal of Computing Research and Innovation, 7(1), 138–146. https://doi.org/10.24191/jcrinn.v7i1.278

Issue

Section

General Computing