KIVU: Lake Intelligence Platform for Fish Cage Farmers
A team of five developers built KIVU, an aquaculture and environmental intelligence platform for fish cage farmers on Lake Victoria.
Stock photo for illustration only, not from the actual event
- KIVU is an aquaculture intelligence platform built by a team of five developers.
- The system is powered by Go 1.22, PostgreSQL 15, and PostGIS 3.3.
- Its risk engine uses a transparent, rule-based approach for easy farmer comprehension.
- It integrates Copernicus satellite data and SMS alerts for remote fish farmers.
Cage aquaculture is expanding rapidly across Lake Victoria—touching Kenya, Uganda, and Tanzania—bringing massive opportunities alongside severe risks. Dissolved oxygen levels can plummet below 3 milligrams per liter with little warning, while temperature spikes and algal blooms stress the fish population.
Most farmers still rely on intuition, visual checks, and late-night phone calls. To address this, a team of five built KIVU, acting as a bridge between the farmer's mobile phone and the lake by providing real-time telemetry and risk assessment engines.
The product architecture is structured cleanly, utilizing Go 1.22 with Gin and sqlx for the backend API. For data storage, it pairs PostgreSQL 15 with PostGIS 3.3 to handle accurate geospatial types, alongside JWT and bcrypt authentication tailored for mobile accessibility.
Stock photo for illustration only, not from the actual event
The AI risk engine was deliberately designed to be rule-based and explainable rather than a black-box model, ensuring farmers can clearly understand why a specific safety alert has been triggered.
"Explainable beats black-box for domain users who have to trust the system with livestock."
KIVU Development TeamPostGIS plays a critical role in managing geospatial logic, storing fish cages as points and lake zones as polygons. Using GiST indexes allows the platform to calculate accurate distances and evaluate expansion suitability around areas like Homa Bay, Mbita Channel, and Rusinga.
Serving both the API and the static frontend from a single Go binary simplifies deployment considerably, making it an ideal approach for demos and hackathons. Implementing proper PostGIS data types from day one also prevents common spatial data pitfalls.
Looking ahead, the team plans to integrate real IoT sensors, enhance machine learning models, implement multi-language SMS and USSD support for last-mile reach, and develop a mobile-first progressive web app.
Source:
Dev.to
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