Smart segregation system: discrimination of degradable and non-degradable waste using deep learning towards environmental sustainability/ Ace Jester Alicante, Paulo R. Bendijo, Kenneth I. Lopena, Kaye Charm Chiara Malubay, and Maureen E. Ramirez.--

Bachelor's thesis

College of Industrial Technology.-- Bachelor of engineering technology major in computer engineering technology: Technological University of the Philippines,
2024.

Includes bibliographic references and index.

Solid waste management continues to encounter significant challenges, particularly in
waste segregation, where manual sorting is often inefficient and obsolete. With the aim of
resolving this serious concern, an eco-friendly, low-cost, and automated waste sorting
system was designed to detect, sort, and classify waste into four different categories:
biodegradable, non-biodegradable, hazardous, and unsorted. The system integrates
hardware components such as a camera module, servo motors, ultrasonic sensors, and a
Raspberry Pi, combined with advance image processing techniques supported by machine
learning and deep learning algorithms. Real-time system status is displayed on an LCD
display, while bin capacity levels are visually and audibly indicated using LEDs and
buzzers. The entire system was developed using an iterative prototyping approach,
allowing for continuous improvement to enhance its performance. You Only Look Once
version 8 deep learning model, also known as YOLOv8, was trained on a custom waste
dataset for precise object detection and identification, with TensorFlow Lite seamlessly
integrated to optimize efficient real-time processing. Smooth coordination was achieved
between the camera module, servo motors, sorting mechanism, ultrasonic sensors, and the
Raspberry Pi acting as the main controller. Extensive testing was conducted to assess
classification accuracy, response time, system efficiency, processor performance, and
detection reliability. Black-box testing confirmed the system functionality, while evaluated
using ISO/IEC 25010 standards focusing on functionality, performance efficiency,
usability, reliability, and maintainability through survey questionnaire with 10 waste
management experts as respondents. The system achieved a user satisfaction score of 4.5
out of 5 on the Likert scale, which falls under the 'Very Good' category. This study
establishes the vast potential of combining hardware with machine learning and deep
learning algorithms to design innovative and environmentally friendly solutions for waste
management automation.