Weight Prediction System for Nile Tilapia using Image Processing and Predictive Analysis

Fish farmers are likely to cultivate poor quality fish to accommodate the rising demands for food due to the ever-increasing population. Fish growth monitoring greatly helps on producing higher quality fish products which leads to a better impact in the aquatic animal food production industry. However, monitoring through manual weighing and measuring stresses them that affects their health resulting to poorer quality or even fish kills. This paper presents a low-cost monitoring and Hough gradient method-based weight prediction system for Nile Tilapia (Oreochromis niloticus) using Raspberry Pi microcontroller and two low-cost USB cameras. This study aims to improve fish growth rate through monitoring the growth of the fishes with image processing eliminating the traditional way of obtaining fish measurements. By using paired t-test, the acquired values imply that the weight algorithm used to measure the weight of the fishes is accurate and acceptable to use. Growth performance of 10 Nile Tilapia was obtained in two intensive aquaculture setups – one for automated fish weighing through image processing and predictive analysis and the other setup for manual weighing. In response to weight prediction application, the growth of the fishes increased by 47.88%.

@article{Tolentino2020,
  title     = {Weight Prediction System for Nile Tilapia using Image Processing and Predictive Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {8},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Lean Karlo S. Tolentino and Celline P. De Pedro and Jatt D. Icamina and John Benjamin E. Navarro and Luigi James D. Salvacion and Gian Carlo D. Sobrevilla and Apolo A. Villanueva and Timothy M. Amado and Maria Victoria C. Padilla and Gilfred Allen M. Madrigal},
  doi       = {10.14569/IJACSA.2020.0110851},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110851}
}