Author(s): Fittrie Mellianawaty Pratiwy and Kiki Haetami

Abstract: Efficient feeding plays a vital role in the success of fish farming operations. In recent years, there has been a growing need to develop auto-feeder technologies that not only optimize feed utilization but also reduce labor costs and environmental impact. This abstract explores the various types of auto-feeder technologies that contribute to achieving feed independence in fish farming. The paper delves into three primary categories of auto-feeders: demand-based feeders, time-based feeders, and sensor-based feeders. Demand-based feeders employ advanced algorithms to dispense feed based on the fish's appetite, ensuring optimal feeding rates and minimizing wastage. Time-based feeders provide feed at pre-determined intervals, offering a more straightforward approach but requiring careful calibration. Sensor-based feeders utilize real-time data from environmental sensors, such as water quality parameters and fish behavior, to adjust feeding schedules and quantities accordingly. The abstract highlights the advantages and limitations of each auto-feeder type, considering factors such as feed conversion efficiency, growth performance, and labor requirements. Furthermore, it discusses the potential integration of artificial intelligence and machine learning algorithms to enhance the auto-feeder technologies' performance and adaptability. The findings suggest that auto-feeder technologies have the potential to revolutionize fish farming practices by enabling feed independence. These technologies contribute to reducing feed waste, improving growth rates, and optimizing feeding strategies. However, further research is needed to overcome challenges such as accurate appetite estimation, sensor calibration, and cost-effectiveness. Ultimately, the abstract emphasizes the importance of auto-feeder technologies in achieving sustainable and efficient fish farming practices, paving the way for increased productivity and reduced environmental impact in the aquaculture industry.

DOI: 10.22271/fish.2023.v11.i4a.2819

Pages: 16-18  |  317 Views  174 Downloads

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