Internet of Things-Based Automatic Fish Feeding System Using ESP32 with Real-Time Monitoring

Abstract

Uncontrolled feeding can reduce the efficiency of fish farming. This study develops an Internet of Things-based automatic fish feeding system using ESP32, integrated with a load cell sensor, an ultrasonic sensor, and an RTC module for scheduled feed dispensing and real-time monitoring through the Blynk application. The test results showed that the ultrasonic sensor had an error of 0%, while the load cell had an average error of 5.61%. Operational testing over 14 days resulted in feed dispensing ranging from 206 to 219 g per session. Compared with the manual method, which had an average error of 48.9%, the automatic system provided more consistent and measurable feed portions. These results indicate that an IoT-based system can improve precision in feed management for fish farming