Automated Dual-Source Squid Dryer with Image Processing Monitoring

Abstract

The aim of this project was to develop and fabricate an indoor dual-source drying system that uses IoT to detect moisture during drying. By comparing traditional drying methods to their automated system, the researchers were able to save a total of 34 hours while effectively monitoring humidity and temperature in real-time using image processing techniques. Development and Evaluation of an Automated Dual-Source Squid Dryer with Image Processing Monitoring for Enhanced Drying Efficiency. The study was conducted in Brgy. Canlanipa, Surigao City, Surigao del Norte, with a duration of 1 year. The study involved designing and developing an indoor dual-source dryer system with moisture detection through image processing monitoring, using Arduino Uno, Raspberry Pi 3b, sensors, and a motor. A working prototype was created, validated, and subjected to thorough testing using wet squid samples to evaluate its performance, leading to necessary adjustments and improvements based on feedback and test results. This study developed an automated dual-source squid dryer with image processing monitoring. The system demonstrated faster drying time (14 hours) compared to traditional methods (48 hours) while maintaining good quality. The indoor drying system proved advantageous, being weather-independent and achieving dry squid with 10% moisture content. The automated dual-source squid dryer with image processing monitoring achieved a shorter drying time of 14 hours, outperforming traditional methods that took 48 hours, while ensuring high-quality results. This highlights the system's efficiency and dependability for indoor squid drying, unaffected by weather conditions.