Milkfish fry manual counting in local hatcheries is labor-intensive, inaccurate, inefficient, and consume long hours.
To improve the fry counting process, the M-eye Fry Counter, a technology utilizing machine vision and automation, is being developed through a project being implemented by the Metals Industry Research and Development Center of the Department of Science and Technology (DOST-MIRDC).
Although the project is still in the research and development (RandD) phase, the team has designed and developed a working prototype and has already achieved significant accomplishments in providing a non-invasive, highly-accurate, faster, and cost-effective solution for counting milkfish fry.
Every year, the Philippines requires approximately 5 billion milkfish fry.
However, the project noted that existing manual counting methods remain labor-intensive and have limited operational efficiency and technological advancement in hatchery operations.
Imported fry-counting technologies are available, but they are often expensive and lack local technical support.
To address these challenges, DOST-MIRDC is implementing the project, ‘M-Eye Fry Counter: An Innovative Machine Vision-based Precision Fry Counting Solution to Streamline Milkfish Fry Distribution.’
Funded by the DOST-Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (DOST-PCAARRD), the project aims to design and develop a machine vision-based milkfish fry counter that will help streamline hatchery operations and improve counting accuracy.
The project has reported significant progress in the development and testing of the automated fry counting system. Through different series of testing, the prototype has achieved an accuracy rate of over 95 percent.
However, further refinements are still ongoing to achieve a better level of accuracy.
The project team said that accuracy evaluations were based on comparisons with manual counting methods, which can often result in errors that can affect the baseline reference used in evaluating system performance.
One of the key improvements observed in the updated versions of the system is the significant reduction in counting time, which demonstrates the M-Eye Fry Counter’s potential to enhance efficiency compared with traditional manual counting methods.
To further validate the reliability of the technology, additional stress tests and experimental trials will be conducted for its field testing.
The team is also exploring opportunities to reduce production costs, particularly through local fabrication and bulk manufacturing.
Despite these developments, the project still remains in the RandD stage. Further field validation across various milkfish-producing areas in the country is necessary to ensure the technology’s effectiveness, reliability, and adaptability under different hatchery conditions.
In line with its objective of automating fry counting, the project team emphasized that the technology is not intended to displace laborers.
Instead, it is expected to enhance operational efficiency by allowing farm workers and operators to focus on other essential hatchery and farm management tasks.