An IoT-Based Smart Feeding System for Koi Fish Using Mamdani Fuzzy Logic - April Firman Daru; Alauddin Maulana Hirzan; Galuh Ardiansyah Putra; Paminto Agung Christianto (University of Science and Computer Technology, 2026)
Koi feeding management requires precision in both feeding timing and feed quantity to maintain fish health and reduce mortality rates. Manual feeding practices are often inconsistent due to human limitations, leading to overfeeding or underfeeding. This study proposes an IoT-based smart feeding system for koi fish that integrates Mamdani fuzzy logic to determine adaptive feeding durations based on feed stock conditions. The system employs a NodeMCU ESP8266 microcontroller, an ultrasonic sensor for feed-level monitoring, a servo motor for feed dispensing, and the Blynk platform for real-time remote monitoring and control over the internet. Mamdani fuzzy inference is utilized to classify feed levels into linguistic variables (low, medium, and high) and generate appropriate feeding actions. Experimental results demonstrate that the proposed system operates reliably, with an average measurement error of 1.59%, indicating high accuracy in feed-level detection. The fuzzy logic controller effectively adjusts feeding duration according to feed availability, enabling consistent and controlled feeding schedules. The proposed system offers a practical and low-cost solution for intelligent koi fish feeding management and can be extended to broader applications in smart aquaculture systems.