Implementation of IoT Technology for Optimization Shrimp Feeding in SMKN2 Kalianda Shrimp Ponds

Abstract

Shrimp farming in Indonesia, particularly at SMKN 2 Kalianda, faces inefficiencies in manual feeding methods, leading to wasted feed and suboptimal shrimp growth. This research proposes an IoT-based automatic shrimp feeding system designed to address these issues by automating the feeding process and integrating real-time water temperature monitoring. The system utilizes the NodeMCU ESP8266 microcontroller, servo motors for feed distribution, and a DS18B20 sensor for monitoring water temperature. Data is communicated via HTTP to a web application, providing real-time monitoring capabilities. The system was successfully tested at SMKN 2 Kalianda's shrimp pond, demonstrating efficient feed distribution, with distances of up to 1.5 meters. It also ensured timely and accurate feeding, reduced labor costs, and minimized feed wastage. Additionally, the system can be further enhanced by integrating data analytics and machine learning to optimize feeding schedules and water quality management. These advancements promise to improve shrimp farming efficiency, reduce human error, and contribute to sustainable aquaculture practices.