How We Test
We test fish feeders to answer one central question: Will this device help your fish thrive while preserving water quality and your peace of mind? Our methodology blends controlled measurements with long-term, real-tank observation.
Test Environment
- Tanks: Freshwater and saltwater systems in common sizes; covered and open-top; varying humidity.
- Species profiles: Community fish, bottom dwellers, and picky eaters to assess food delivery and behavior impacts.
- Baseline logging: At least 7 days of manual feeding logs before automated trials.
What We Measure
- Portion accuracy and consistency: We weigh 10+ consecutive dispenses using controlled food sizes and record variance.
- Moisture control: We run high-humidity trials with desiccants and track clumping, jamming, and degradation over time.
- Feed window and dispersion: We evaluate how evenly food spreads and how quickly it becomes available to different strata.
- Water quality response: We monitor ammonia, nitrite, nitrate, and phosphate before and after schedule changes.
- Reliability and failure modes: We test battery life, power interruptions, hopper bridging, app disconnects, and motor stalls.
- Usability: Setup clarity, calibration steps, app experience, and cleaning/maintenance time.
- Noise and stress: We observe startle responses and adaptation over repeated cycles.
Protocol Highlights
- Calibration runs: 3–5 dry runs into a catch cup with a precision scale before tank deployment.
- Real-world duration: Minimum 2-week in-tank tests to reveal drift, moisture effects, and schedule adherence.
- Cross-food testing: Pellets, flakes, and granules across typical diameters; we note which forms each feeder handles best.
- Contingency checks: Behavior and parameters during travel-mode scenarios to validate remote reliability.
Scoring and Recommendations
We score feeders across health impact, water-quality risk, reliability, and ease of use. Recommendations state ideal use cases (species, tank size, food type) and any required setup tweaks to achieve best results. Limitations are explicit so you can judge fit for your system.
Continuous Improvement
We refine methods as new devices and foods emerge. Reader data is welcome—if you share logs, we anonymize and incorporate insights into future guidance.