A low-cost smart fishing application using buoy-mounted sensors.
The Fishing industry plays an important role in livelihood, food security, and the economy. However, unfavorable water quality parameters (e.g. oxygen, pH level, water level, temperature, etc.) affect fish production. Guerrero III (2019) found that high water temperature is the major cause of the Philippines's low production of tilapia—a common breed of fish in the country. Current water quality monitoring technologies (Martin et al., 2020) lack prospective geospatial applications.
PISDA offers a solution that will integrate geospatial data with testing of water parameters for real-time monitoring and tending of fishing areas.
PISDA: Position Inspector and Sensor Dependent Fishing Assistant is an application that provides the real-time position of water parcels, alerts, tracks, and displays anomalies in the water quality of fishing areas. PISDA utilizes water buoys as a medium for sensing devices measuring qualities of the water (e.g. oxygen and pH level, and temperature), mounted with a GNSS receiver. Fish farmers can immediately tend to and regulate a specific parcel with critical water quality parameters.
Where: Since GALILEO offers open access to precise positioning and timing, PISDA can utilize these data in providing the location of fishing spots (fish farms, sea, etc.) to track and know critical water quality parameters.
How: A GNSS receiver is mounted on a sensor-dependent buoy. Through this, the location information of individual fish pens is provided. The integration of these will help users to properly manage possible anomalies.
Open-Service Free of Charge - Makes the project cheaper.
Fully Interoperable - Guarantees real-time operation.
Free Global High-Accuracy Service - Ensures that the location of water parcels is down to 20-cm accuracy.
Resistant to Multipath - along with antenna design, multipath errors are reduced.
*For a three sensor buoy (oxygen, pH level, temperature)
**Excluding in-app advertisement and subscription revenue. Also, saltwater fishing farms/areas and seaweed farms are not included due to the lack of area data.
The Product - Our team offers a sensor-mounted buoy, a cost-effective water technology that enables smart fish farming for fishing industries across the world.
The Process - PISDA, deliberately designed for sensors to work fittingly is also attached with GNSS receiver to integrate spatial information together with its water quality parameters. A free application is available for real-time and friendly-user data provision for users.
The Position - This product is offered for Filipino fish/seaweed farmers, aquaculturists, concerned government agencies to ensure that the fishing industry in the country is efficient and well-monitored.
The Paradigm - This product satisfies the triple bottom line through its purpose and key activities. Through the incorporation of GNSS technology into the fisheries industry, income can be generated while supporting sustainable fish farming with minimal cost.
PISDA in the market - PISDA is the first ever technology used for farm fishing with two integrated smart tools (i.e., GNSS receiver and water quality sensors). This product is economical and provides boost for maritime productivity.
PISDA will use water quality sensor-mounted buoys that will be registered to the app by the consumer (similar to smartwatch technology). The buoys use GNSS-Reflectometry for precise and continuous positioning (horizontally and vertically). The buoys will have a specialized GNSS antenna to reduce multipath and other errors. IoT technology will be utilized for the buoys to transmit data towards the app's database.
We plan to have different variations of the product (more sensors, e.g., water turbidity, chl-a, etc.) to cater to the budget of the consumers
Anchoring/Tethering may be necessary for certain fish pens/ponds/sea.
Our team consists of highly competitive BS Geodetic Engineering students from the University of the Philippines Diliman with adequate knowledge about GNSS and GIS.
We heed geospatial information with accuracy and precision.
Kim Elijah A. Aguilan
Ginell Elyza M. Buenavista
Ainalyn A. Nerves
Francesca Deighl R. Rivera
Cristan Dave C. Zablan