ENHANCING FISH REARING EFFICIENCY THROUGH AN AUTOMATED POND MONITORING AND CONTROL SYSTEM
In response to the growing significance of fisheries aquaculture, the adoption of automated fish farming systems has become imperative. The effectiveness of such systems hinges on the meticulous supervision of pond water quality parameters, precise feeding practices, and a dependable power supply. This study introduces an innovative fish pond monitoring and control system that significantly enhances fish rearing efficiency. The system employs a network of sensors, including those for temperature, pH, turbidity, and water level, to gauge water quality metrics. The sensor data is transmitted to the pond manager via a Global System for Mobile communication (GSM) module. Additionally, an automated feeding mechanism, steered by a servo motor and a real-time clock (RTC), ensures timely and appropriate nourishment for the fish population. The inclusion of a turbidity sensor effectively curtails feed wastage and consequent water pollution. The collected sensor readings are promptly displayed on a liquid crystal display (LCD) for observation.
Furthermore, the system integrates an automated water circulation setup, managed by a pumping mechanism and filtration system, aimed at purifying the pond water. The water level sensor plays a pivotal role in maintaining the water volume within the optimal range. The entire monitoring and control process is orchestrated by microcontrollers, specifically Arduino Uno and Arduino Mega, programmed using the C++ language. Analog to digital converters (ADC) facilitate the connection between the microcontrollers and the individual sensors. To validate the system’s efficacy, a prototype fish pond was constructed for experimental demonstration. Rigorous testing was conducted on this prototype to verify the system’s performance.
During a six-week trial period, ten catfish fingerlings were reared using the automated system, with measurements taken for water quality indicators, weight gain, and feed consumption. The assessment utilized metrics such as Feed Conversion Ratio (FCR) and Feeding Efficiency (FE). These metrics were also applied to a parallel manual fish rearing system for comparative analysis. The results demonstrated a notable advantage for the automated system, with an FCR of 1.18 compared to 1.82 in the manual counterpart. This outcome indicated superior feed utilization and productivity in the automated system. Moreover, the FE for the automated system reached 84.7%, contrasting with the 54.9% recorded in the manual system, highlighting the automated system’s more effective feeding practices.
Notably, the average weight gain per fish over the six-week experiment was 28.95g in the automated system, surpassing the 18.45g in the manual system. These findings underscore the remarkable efficiency of the automated system, particularly in terms of feeding effectiveness. This study presents compelling evidence for the robustness and efficiency of the developed automated fish rearing system compared to conventional manual methods.
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