INTELLIGENT GARBAGE SEGREGATION SORTING MACHINE
TAMALA, JAMES LESTER S.
UNDERGRADUATE CAPSTONE PROJECT SUBMITTED TO THE
FACULTY OF THE DEPARTMENT OF INFORMATION TECHNOLOGY,
COLLEGE OF INFORMATION SCIENCES AND COMPUTING,
CENTRAL MINDANAO UNIVERSITY, IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE
BACHELOR OF SCIENCE IN INFORMATION TECHNOLOGY
JUNE 2023
i
Republic of the Philippines
CENTRAL MINDANAO UNIVERSITY
Musuan, Maramag, Bukidnon
College of Information Sciences and Computing
Department of Information Technology
APPROVAL SHEET
The undergraduate capstone project attached hereto entitled,
“INTELLIGENT GARBAGE SEGREGATION SORTING MACHINE” (Research
No. 21343), prepared and submitted by JAMES LESTER S. TAMALA, in partial
fulfillment of the requirements for the degree Bachelor of Science in Information
Technology (Data Network), is hereby endorsed.
NATHALIE JOY G. CASILDO
Chair, Thesis Advisory Committee
Date
ROMER IAN O. PASOC
Member, Thesis Advisory Committee
Date
DONAH RAE P. VERULA
Member, Thesis Advisory Committee
Date
Recommending Approval:
JOHN D. TAJONES
Department Chair
Date
JINKY G. MARCELO
Research Coordinator
Date
Accepted in partial fulfillment of the requirements for the degree in Bachelor of
Science in Information Technology.
Approved:
KENT LEVI V. BONIFACIO
College Dean
Date
Noted:
JUPITER V. CASAS
`Director for Research
Date
ii
Copyright © 2023 by James Lester S. Tamala
All Rights Reserved
ii
AUTHOR’S BIOGRAPHICAL SKETCH
James Lester S. Tamala was born and raised in
their humble home at Purok 1 New Compostela
Damulog Bukidnon, Bukidnon on July 25, 2001.
He is the only child of Mr. Harvey Tomarong
Tamala
and
Mrs.
Aileen
Sotto
Tamala.
Currently, he resides in New Compostela,
Damulog Bukidnon with his family.
He completed his primary education at New
Compostela Elementary School, graduated as
the Salutatorian of his class. He pursued his
junior high education at Xavier de Damulog
High School, where he achieved class honors.
In senior high school, he specialized in Humanities and Social Sciences Strand
(HUMMS) at Xavier de Damulog High School. He continued his education at
Central Mindanao University, pursuing a Bachelor of Science in Information
Technology.
During his senior high school years, James Lester S. Tamala actively took on
leadership roles and served as an officer of Citizenship Advancement Training
(CAT) (2017-2019). He has been recognized on the Dean's List during college
from 2022 to 2023.
JAMES LESTER S. TAMALA
iii
ACKNOWLEDGEMENT
First and foremost, the proponent wishes to thank the Almighty God for
the blessings of knowledge, protection, assistance, good health, and
unconditional love. This project could not have been completed without the
significant assistance of the capstone project adviser and panel. The proponent
extends sincere appreciation to their capstone adviser, Ma'am Nathalie Joy G.
Casildo, for her unwavering support and dedication. The proponent is
immensely grateful for the adviser's investment of time, knowledge, patience,
and effort, which greatly assisted in the successful completion of this capstone
project.
Gratitude is also extended to Sir Romer Ian O. Pasoc and Ma'am Donah
Rae P. Verula for generously sharing their knowledge and ideas. Their
contributions significantly enriched the project and expanded the proponent's
understanding. The proponent extends heartfelt thanks to the three individuals
mentioned for their guidance and mentorship throughout this endeavor.
Additionally, the proponent would like to express gratitude to Miss Daphne
Gonzaga Salomon, for lending her Image Classification Model, which played a
vital role in completing the system. The proponent is genuinely thankful for her
assistance and support.
Lastly, the proponent extends deep appreciation to their beloved
parents, Papa Harvey and Mama Aileen, for their unwavering love, patience,
joy, and especially their financial support. The proponent acknowledges that
their parents' presence and assistance were indispensable in reaching this
point. The proponent also expresses gratitude to their friends, relatives,
advisers, faculties, classmates, and all those who have been part of their
journey. The proponent is immensely grateful for their invaluable help and
unwavering support, which played a significant role in successfully concluding
this project.
The proponent acknowledges and extends heartfelt thanks to all the
individuals mentioned above. Their support, guidance, and encouragement
have been instrumental in the completion of this project.
iv
EXECUTIVE SUMMARY
Poor waste management was one of the common problems in society.
Unsegregated waste materials caused pollution like water contamination, soil
pollution, and air pollution which had harmful effects. Even though society
provided a garbage container for each different garbage material, people still
threw their waste carelessly due to improper disposal. Previous studies from
other researchers had also used advanced technology to construct a smart
trashcan, with common project features including wet and dry rubbish
classifications using a moisture sensor and a metal detector. Other studies, on
the other hand, only monitored the garbage levels in the garbage bins. Some
researchers included computer visions in their systems by collecting data sets
and training them in various methods.
To address this problem, a garbage sorting machine was designed to
classify the garbage materials, which were biodegradable materials and nonbiodegradable materials, by implementing an image-based classification model
on the system. Additionally, the system could notify the user about the level of
garbage in the trash can by integrating an ultrasonic sensor and buzzer module
into the system.
The
system
was
composed
of
four
major
components:
the
microprocessor that processed the collected data, the sensor that was
responsible for image classification, the motors that were responsible for
motorizing mechanism activities, and lastly, the notification module which was
accountable for alerting the users.
v
Table of Contents
TITLE
PAGE
TITLE PAGE…………………………………………………………………………..i
COPYRIGHT PAGE ......................................................................................... ii
AUTHOR’S BIOGRAPHICAL SKETCH ........................................................... iii
ACKNOWLEDGEMENT .................................................................................. iv
EXECUTIVE SUMMARY .................................................................................. v
Table of Contents ............................................................................................ vi
List of Figures ................................................................................................ viii
List of Tables .................................................................................................... x
List of Appendices ........................................................................................... xi
Chapter I ........................................................................................................... 1
INTRODUCTION .............................................................................................. 1
1. Background of the project .......................................................................... 1
2. Statement of the Problem .......................................................................... 2
3. Objectives of the Project ............................................................................ 3
4. Scopes and Limitations.............................................................................. 3
5. Significance of the Project ......................................................................... 4
CHAPTER II ..................................................................................................... 5
REVIEW OF RELATED LITERATURE/SYSTEMS ........................................... 5
1. Review of related literature about smart dust bin technology .................... 5
2. Review of related literature about Garbage Sensing machines ................. 9
3. Review of related literature about Confusion Matrix ................................ 10
Chapter III ....................................................................................................... 12
TECHNICAL BACKGROUNDS ...................................................................... 12
1. Raspberry Pi ............................................................................................ 12
2. Raspberry Pi Camera Module ................................................................. 12
3. Servo Motor ............................................................................................. 12
4. Tensor Flow Lite ...................................................................................... 12
5. Ultrasonic Sensor .................................................................................... 13
6. Buzzer .................................................................................................... 13
7. Open CV .................................................................................................. 13
8. RGB Light Emitting Diode (LED) ............................................................. 13
Chapter IV ...................................................................................................... 14
METHODOLOGY ........................................................................................... 14
vi
1. Conceptual Diagram ................................................................................ 14
2. Process Block Diagram ........................................................................... 16
3. Architectural Diagram .............................................................................. 18
4. Schematic diagram ................................................................................. 20
5. List of Materials ...................................................................................... 21
6. Cost of Materials ...................................................................................... 22
7. Project Timeline ....................................................................................... 23
8. Dataset model ......................................................................................... 24
9. Hardware Calibration and Test Result ..................................................... 25
I.
Development Process ...................................................................... 25
II.
Calibration ........................................................................................ 26
10. Process Flow on Testing ....................................................................... 29
Chapter V ....................................................................................................... 30
RESULTS AND DISCUSSION ....................................................................... 30
1.
Whole Prototype Machine ..................................................................... 30
2.
Receptacles .......................................................................................... 31
3.
Enclosure and Electronics .................................................................... 32
4.
Garbage Level Detector Module ........................................................... 33
5.
Notification Module ............................................................................... 34
6.
Separator Flap ...................................................................................... 35
7.
Camera Sensor..................................................................................... 36
8. Process of activating the machine using the command line of Linux in
Raspberry pi operating system. ................................................................... 37
9.
Confusion Matrix ................................................................................... 40
10. Discussion ............................................................................................ 44
Chapter VI ...................................................................................................... 46
CONCLUSION AND RECOMMENDATIONS ................................................. 46
1.
Conclusion ............................................................................................ 46
2.
Recommendations ................................................................................ 47
References ..................................................................................................... 48
Appendices………………………………………………………………………....52
vii
List of Figures
FIGURE
TITLE
PAGE
1
Conceptual Diagram of Garbage Sorting Machine
14
2
Conceptual Diagram of Notification Module
15
3
Process Block Diagram of the Machine
16
4
Process Block Diagram of the Notification Module
17
5
Architectural diagram of the machine
18
6
Architectural Diagram of the Enclosure
19
7
Schematic Diagram of the Motor and Sensor machine
20
8
Process Flow on Testing
29
9
Whole System Prototype Machine
30
10
Receptacles for the garbage container.
31
11
System Enclosure and Electronics
32
12
Level Detector System
33
13
Buzzer module and RGB Light Emitting Diode (LED)
34
14
Servo Motor
35
15
Separator flap powered by motor.
35
16.
Raspberry Pi Camera Module
36
17
Opening the tflite1 folder
37
18
Opening the Python Virtual Environment
38
19
Activation of General Pin Input Output (GPIO) library
38
20
Activation of classification of waste program
39
21
The confusion matrix of ResNet152 and the incorrectly classified
garbage materials both in false positive and negative
22
The confusion matrix of VGG16 and the incorrectly classified
garbage materials both in false positive and negative
23
41
The confusion matrix of MobileNet and the incorrectly classified
garbage materials both in false positive and negative
24
40
42
The confusion matrix of Inceptionv3 and the incorrectly classified
garbage materials both in false positive and negative
43
25
Raspberry Pi Module
53
26
Raspberry Pi Camera
54
viii
FIGURE
TITLE
PAGE
27
Ultrasonic Distance Sensor
55
28
Jumping Wires
56
29
Resistor
57
30
12 Volts Fan
58
31
12 volts Power Supply
59
32
Breadboard
60
33
Buzzer Module
61
34
Servo Motor
62
35
RGB LED
63
36
Plywood
64
37
Building the Box Frame
66
38
Electronics Installations
67
ix
List of Tables
TABLE
TITLE
PAGE
1
The tabular form of Cost of the Materials
22
2
Gantt Chart of the Capstone Project
23
x
List of Appendices
APPENDIX
A
TITLE
PAGE
List of Materials for the Intelligent Garbage Segregation
Sorting Machine
52
B
Building process of the prototype machine
65
C
Relevant Codes
68
D
Grammarian Certificate
76
E
Plagiarism Evaluation Result
78
xi
Chapter I
INTRODUCTION
1. Background of the project
Garbage is described as materials that had been disposed of or were no
longer required (Maritime Safety Queensland). According to the UN, the
modern economy's rising amount of garbage posed a severe threat to
ecosystems and human health. An estimated 11.2 billion tons of solid trash
were collected globally each year, UN (2022). Poor garbage disposal had
several harmful impacts on our health and the environment, according to the
Metropolitan Transfer Station. The negative effects of improper garbage
management included soil contamination, water contamination, and air
contamination. This was due to numerous dissolved chemicals from the
garbage which could harm our health by inhaling toxic chemicals from the air,
as well as generate extreme weather as a result of climate change because
hazardous greenhouse gases were produced by decaying waste. These rose
to the surface of the earth and trapped heat. Extreme weather reactions, such
as storms and typhoons, were a result of this, Metropolitan Transfer Station
(2017).
Several challenges led to improper garbage disposal, according to
Greenbank. Four factors led to poor garbage management, including lack of
public awareness, particularly lack of understanding within enterprises, and
poor attitudes, which were among the first reasons for inadequate waste
management, Greenbank (2020). When something reached the end of its
useful life, it was frequently disposed of carelessly, and the lack of proper types
of machinery such as balers and compactors made it difficult to implement a
truly efficient waste management strategy, Greenbank (2020). In summary, the
garbage problem in our society rapidly rose due to many productions in the
current period. Poor garbage management could result in health and
environmental issues because most people did not care about proper disposal.
Even though it had a lot of technology that was applied in society, it lacked the
machinery to control and manage the trash in its category.
1
Some technologies were fully automatic and segregated garbage according to
its classification. Some of these technologies used the internet of things to
address this type of problem by inserting a system into the trashcan and
monitoring the state of the trashcan in real-time. Some systems incorporated
an image classification program and machine learning to identify garbage
components. To address this issue, the community needed a machine that
automatically separated garbage into categories, checked the amount of waste
in the trashcan, and alerted the user if the trashcan could no longer contain the
waste. The project was carried out to create a machine capable of sorting
various garbage materials, as well as to create a system that notified the user
if the trash bin could no longer be accommodated.
2. Statement of the Problem
Poor garbage management is a well-known and common problem not
just to society but also to our planet. Although the concept of a garbage sorting
machine was popular, it was mostly utilized by industries that recycled
resources for other purposes, and aftermarket trash bins were very expensive
for their minimum features. Most individuals did not care about dumping their
garbage in the trash can, resulting in the mixing of biodegradable garbage
materials and non-biodegradable garbage materials, which could lead to
improper waste management. However, there was no suitable equipment in the
community that was capable of separating different garbage materials, and
there was no system that notified the user when the garbage bin was full.
2
3. Objectives of the Project
The general objective of the project was to develop a garbage sorting
machine prototype.
Specifically, this project aims to:
i.
implement an image-based classification model on the sorting
machine;
ii.
develop a machine that can identify biodegradable garbage
materials and non-biodegradable garbage materials;
iii.
detect trashcan levels, and alert the user about the state of the
machine, and
evaluate the overall satisfaction of the machine’s performance.
iv.
4. Scopes and Limitations
This project was conducted to develop a machine that could put garbage
into categories: biodegradable materials such as Fruit waste, Paper Waste, and
Vegetable waste, as well as non-biodegradable materials such as Glass Bottle
Waste, Metal Can Waste, and Plastic Waste. It detected trashcan levels and
alerted the user about the state of the machine. The system could only detect
one type of garbage in each load and would not accommodate several types of
garbage simultaneously. The system was only composed of four major
components: sensor components that identified garbage materials, motor
components for the separator machine, a microprocessor that processed
sensor data and sent commands to the motors, and a notification module that
sent users the system's status. The machine would not function if there was no
power source because it had no backup power. The garbage sorting machine's
sole purpose was to classify garbage and inform and alert the user when the
garbage can no longer be accommodated. The machine only tested its
capabilities in garbage materials and did not test any materials unless they were
garbage components.
3
5. Significance of the Project
The use of this machine was important in significantly reducing the
current problem of insufficient garbage disposal methods, which was primarily
leading to alarming amounts of air and water pollution. By putting this machine
into the local garbage disposal system, it provided numerous benefits to the
community as a whole. One major advantage was its ability to raise awareness
among individuals by alerting them when the garbage container was full, so
encouraging proper waste disposal habits. Furthermore, this system was
capable of autonomously categorizing sorts of garbage, biodegradable garbage
and non-biodegradable garbage, significantly reducing the need for manual
monitoring and oversight by community members.
4
CHAPTER II
REVIEW OF RELATED LITERATURE/SYSTEMS
1. Review of related literature about smart dust bin technology
Several researchers have worked in recent years to build and implement
smart dustbin technology in real-world applications. Rajiv Kumar Gurjwar
developed a system called Smart Trash Can Using IoT, which is focused on
segregating different garbage materials, it is made up of an Arduino based
system that includes an Arduino R3 as a micro-controller, a Bluetooth module
that acts as a connection or gateway, an ultrasonic sensor that monitors the
moisture of the objects, DC motors attached to the conveyor belt to perform,
and finally a 9 volts battery to power the system (Gurjwar, n.d.). This is like the
research of Kavipriya et al (2020) entitled Intelligent Trashcan Monitoring
System Using IoT. The system uses Arduino-based technology as well, but the
primary distinction is that this system focuses on checking the dustbin level.
Both systems are effective in terms of their functions, and both systems include
an application to display the data acquired by the systems (Kavipriya et al.,
2020).
Some of the researchers include the high-level feature in their system,
in the study of Ayush et al., (2020) entitled Voice Controlled Automatic Dustbin
with Garbage Level Sensing, it is also using the Arduino based technology, the
main feature of this device is that it is voice-controlled that you can give the
command, trash level detection as well as the detection of trash. In 2018, Saha
et al., (2018) proposed a study titled IoT Based Garbage Monitoring and
Clearance Alert System. The system uses normal bins (metal or plastic),
ultrasonic sensors, DHT-11 temperature, and humidity sensors, Arduino, GSM
module, WiFi module, RGB led lights Breadboards, Jumper wires, and a power
supply that is a battery connected to a solar panel. The main feature of this
feature is that it is powered by solar energy and has a specialized application
that alerts the user when the garbage cans are full.
5
In 2019 Raaju et al., (2019) developed a system entitled IoT Based
Smart Garbage Monitoring System, using Zigbee, the system is composed of
Arduino Pro Mini and Arduino -nano as micro-controllers, it has Ultrasonic
sensor for detecting the objects, and for the gateway they use the ESP 8266
Node MCU, The ultrasonic sensors are installed on the sides of the bins that
are used to collect the trash level and frequent monitoring can be done given
with the power supply to NODE MCU linked through the solar panel as a power
source terminal. Through the GPS module, the position is determined, and the
frequency is determined. The nearest municipal workplace is kept informed of
the position, and the drivers are notified. The device gathers the waste level of
the environment. Dustbins levels are collected using an ultrasonic sensor and
sent to the next node. ZigBee is used to transport data from the child node to
the parent node. The NodeMCU in the parent node collects the data and sends
it to Firebase.
It is different from the study of Chinmay, et al., (2018) E-dustbin, the
system is included with Esp 8266 for the micro-controller, for a sensor it has a
proximity sensor and ultrasonic sensor, the difference is the data from this
device is shown in the Liquid Crystal Display and also it only focuses on sensing
the level of trash in the dustbin. The height of the rubbish within the dustbin is
detected using one ultrasonic sensor. The rubbish level is measured in
centimeters. Whenever the height of the rubbish is less than 10cm, the esp8266
is interrupted. The Wi-Fi module will be turned on. ESP uses the secured dns
connection to load the data into the web server. The buzzer is also activated,
as is the alert message on the LCD. A moveable platform is suggested for the
dustbin, which is connected to motors and controlled by the motor driver L293D.
The front proximity sensor attached to the trashcan generates an interruption,
which is used to regulate the motor driver. Both systems are effective according
to rules and provide efficient performance.
Researchers from India create a system entitled IoT Based Smart Waste
Management System: India prospective, the systems The project makes use of
the Internet of Things (IoT) and GSM/GPRS technology, connecting the
transmitter and receiver It provides real-time data on trash status, It calculates
the percentage of the trash that has been filled and the amount of toxicity. It
sends data to a website so that it may be seen afterward. It keeps track of the
6
time and date, as well as the dustbin's percent filling and toxicity level, in a
database. When the trash is full or the toxicity level is high, it sends a notice to
the person in charge. The Arduino is coupled to an ultrasonic sensor and a gas
sensor, which provide data to the municipal corporation through the GSM
module, Singhvi, et al., (2019). This study is similar to Dr. Sathish Kumar and
his co-workers' IOT Based Smart Garbage Alert System Using Arduino Uno,
but it focuses more on dustbin level monitoring. The system has its web
application with an embedded system, and it can track the level of the dustbin
using an ultrasonic sensor and send the data via Wifi Module on the web. The
main difference is that it has an RFID system to monitor the garbage truck when
it starts collecting the trash in the bin (Kumar, N. et al., 2016).
This research entitled The Design and Implementation of Smart Trash
Bin provides a low-cost concept for an intelligent trash container for small-scale
applications from the study of Fady E. F. Samann. This system uses an Arduino
Nano board and an ultrasonic sensor to monitor the container's fullness level
and send SMS notifications using a GSM module. The system is fueled by a
lithium battery power bank that is backed up by a solar cell panel. The solution
allows you to charge external portable gadgets with the power bank.
Furthermore, when the bin is in use, the system will record usage and fullness
events on a memory card, which is also utilized to deliver an audio message
through a speaker (Fady E. F. Samann, 2017). Also, in the study of Henita
Rahmayanti and his co-workers entitled the implementation of smart trash as
smart environment concept, The focus of this research was to implement the
smart garbage bin idea in schools. This smart trash can identify garbage
depending on garbage kind (plastic and metal). Furthermore, smart garbage
can serve as an educational tool for pupils. It includes explanations about
knowledge and how to utilize the garbage can so that children may understand
to maintain environmental cleanliness and attractiveness The research
employed the Borg and Gall model's Research and Development approach.
This study's focus was on elementary school kids.
The Internet of Things (IoT) is a fast-evolving technology that has been
extensively researched in order to increase the efficiency of services such as
waste management. Recently, research has concentrated on the creation of
Internet of Things-based trash machines, which are sophisticated sensors and
7
systems that monitor waste levels in containers and warn operators when such
levels reach a given threshold. This paper investigates the present state of the
art in the development of IoT-based trash machines, describing several
research studies and initiatives linked to this technology.
One of the primary advantages of utilizing an IoT-based garbage
machine is its capacity to improve waste management efficiency and minimize
expenses. For instance, Zou et al., (2020) investigated the possibilities of an
IoT-based garbage machine for urban waste management. The authors
created a trash machine prototype that could detect and report waste levels in
a bin and then activate garbage pickup when the bin reached a specified
threshold. According to the authors, the prototype was able to minimize
collection expenses by up to 10% while also providing precise data on garbage
collection activities.
In addition to cost savings, IoT-based waste machines may improve
safety and the environment. For example, Wang et al. (2020) created an IoTenabled waste management system capable of detecting the presence of
hazardous items in garbage containers. The system was able to identify
hazardous compounds and inform operators, lowering the chances of
dangerous substance exposure. Furthermore, data acquired by the garbage
machine was utilized to give insights into waste management operations, such
as detecting locations with higher-than-normal waste levels and providing
recommendations on how to minimize waste.
Furthermore, waste machines powered by IoT may be utilized to improve
customer service. For example, Zhang et al. (2020) created a smart trash
machine that could recognize consumers using face recognition and give
personalized services depending on their preferences. According to the
authors, the approach improved customer satisfaction and reduced the time
required for garbage collection operations.
Overall, IoT-based garbage machines have the potential to transform
waste management operations by offering considerable cost savings, safety,
environmental protection, and customer service benefits. As this technology
advances, the possibilities for its application in the waste management
business will grow. The majority of smart trash bin monitoring systems are
based on Arduino technology, with the majority of the systems focusing on
8
waste level monitoring. All of the systems are primarily focused on notification
systems and utilize ultrasonic sensors to measure garbage levels. Some of the
systems have their application to monitor data from the device, however, no
device with advanced technology such as computer vision and deep learning
is incorporated.
2. Review of related literature about Garbage Sensing machines
The rapid increase in urbanization and population has posed a major
challenge to waste management globally. Smart waste management has been
proposed as a solution to this challenge. One of the components of smart waste
management is the development of smart garbage-level sensing systems. The
purpose of this review is to examine the existing literature on smart garbagelevel sensing systems.
A smart garbage level sensing system refers to a system that is designed
to monitor the level of waste in garbage bins in order to optimize waste
management processes. This system is made up of sensors, microcontrollers,
and wireless communication systems. The sensors are used to detect the level
of waste in the bin and send the data to the microcontroller which then sends
the information wirelessly to the waste management authority. Several studies
have been conducted on smart garbage-level sensing systems. Gomez
Corona, et al. (2016) proposed a smart garbage system that uses an ultrasonic
sensor to detect the level of waste in a garbage bin. The system was able to
detect the level of waste in the bin with an accuracy of 96%. The system was
also able to optimize waste collection routes.
Another study by Huang, et al. (2016) proposed a smart waste
management system that uses a combination of sensors including ultrasonic
sensors, infrared sensors, and temperature sensors to monitor the level of
waste in a garbage bin. The system was able to detect the level of waste in the
bin with an accuracy of 98% and was able to optimize waste collection
schedules.
Similarly, Garg et al. (2019) proposed a smart waste management
system that uses a combination of technologies including Internet of Things
9
(IoT), cloud computing, and sensors to optimize waste management processes.
The system was able to detect the level of waste in the bin accurately and was
able to optimize waste collection schedules.
In conclusion, smart garbage level sensing systems have been
proposed as a solution to waste management challenges caused by
urbanization and population growth. Several studies have been conducted on
smart garbage level sensing systems, and the studies have shown that the
systems are effective in optimizing waste management processes. The
reviewed studies can serve as a basis for the development of smart garbage
level sensing systems to implement effective waste management in urban
areas.
3. Review of related literature about Confusion Matrix
A confusion matrix is a popular tool in the field of machine learning used
to evaluate the performance of classification algorithms. It is a powerful metric
that helps machine learning practitioners to understand the accuracy of their
model, identify the types of errors made and improve the model based on the
insights gained from the matrix.
The use of the confusion matrix has been widely studied in the machine
learning literature. In their study of Fawcett (2006), analyzed the effect of
varying class imbalance on the performance of the confusion matrix. They
showed that when class imbalance is high, metrics like accuracy and precision
can be misleading and the confusion matrix can provide deeper insights into
the model's performance.
Another study by Guyon and Elisseeff (2003) investigated the impact of
feature selection on the performance of different classification algorithms. They
found that the confusion matrix can be used to compare the performance of
different feature selection techniques and help machine learning practitioners
to choose the best technique for a particular problem.
In addition, Phua, C. et al. (2010) conducted a comparative study of
different algorithms based on the confusion matrix in the context of identifying
fraudulent financial transactions. They found that the confusion matrix is a
10
powerful tool to assess the performance of fraud detection algorithms and
evaluate the trade-off between false positives and false negatives.
Overall, the confusion matrix is a powerful and widely used tool in
machine learning to evaluate the performance of classification algorithms. Its
application has been widely studied and it has been shown to provide valuable
insights into the performance of different models. As a result, it is recommended
that it be used by machine learning practitioners to evaluate their models and
gain insights to improve performance.
11
Chapter III
TECHNICAL BACKGROUNDS
1. Raspberry Pi
The Raspberry Pi is a microprocessor computer that is capable of
processing data sets and could also install software packages such as
TensorFlow. The Raspberry Pi served as the system's brain, responsible for
processing the received data from sensors and giving commands to motors.
2. Raspberry Pi Camera Module
The Raspberry Pi camera module is responsible for object detection and
sensing the material components of garbage. It provided data to the Raspberry
Pi for data processing.
3. Servo Motor
The servo motor is in charge of removing dropped garbage from the
system's detecting region. The motorized separator flap could rotate from 0
degrees to a full 360 degrees, allowing the flap mechanism to move up and
down.
4. Tensor Flow Lite
TensorFlow Lite is a tool that enabled running a model on mobile,
embedded, and edge devices, allowing for on-device machine learning. The
TensorFlow Lite software was loaded on the system's microprocessor and ran
the trained model via image classification. The TensorFlow Lite program was
responsible for running the data sets model of different garbage on the
Raspberry Pi to perform image classification, enabling the system to detect the
garbage components.
12
5. Ultrasonic Sensor
The ultrasonic sensor is responsible for detecting the level of the waste
receptacle by emitting ultrasonic sound waves. The sensor sent data to the
microprocessor for continued monitoring, and it monitored the level of the
garbage inside the container.
6. Buzzer
The buzzer acts as an alerting module. The passive buzzer was
connected to the microcontroller, and if triggered, it emitted a sound to notify
the potential user that the garbage containers of the system were full.
7. Open CV
OpenCV is a Python package that enabled image processing and
computer vision applications. It was intended to provide a shared infrastructure
for computer vision applications and accelerate the usage of machine
perception.
8. RGB Light Emitting Diode (LED)
The RGB Light Emitting Diode (LED) is a type of LED that produced light
in three different colors: red, green, and blue. It was used in garbage machines
to indicate when the bin was full. The LED would light up when the bin was full,
alerting the user that it needed to be emptied. Additionally, RGB LEDs used to
indicate different types of waste, such as non-bio and bio waste.
13
Chapter IV
METHODOLOGY
1. Conceptual Diagram
During the sorting process, the garbage was analyzed using computer
vision to determine whether it was biodegradable or non-biodegradable. Once
this was determined, the machine's separator flap dropped the garbage into its
categories by rotating the motor from 0 degrees to a full 180 degrees. Figure 1
showed the conceptual diagram of the garbage sorting machine.
Figure 1. Conceptual Diagram of Garbage Sorting Machine
14
The machine assessed the trash can level using an ultrasonic sensor.
The ultrasonic sensor module collected data and delivered it to the computer,
while the buzzer module served as an alerting system. Figure 2 showed the
conceptual diagram of the notification module.
Figure 2. Conceptual Diagram of Notification Module
15
2. Process Block Diagram
For the process block diagram of the system, the garbage sorting
machine began the process by determining whether the garbage was
biodegradable or non-biodegradable. The process then proceeded to the
confirmation process, where the system determined the classification. If the
garbage was biodegradable, it was placed in the biodegradable trashcan, and
if it was nonbiodegradable, it was placed in the nonbiodegradable trashcan.
Figure 3. Process Block Diagram of the Machine
The sensor evaluated the system's trash level. If it was at its maximum,
the system will send a notification to the designated user via a buzzer, which
16
alerted the intended user. If the user cleaned up the garbage container, the
system will return to a normal state. However, if the user did not clean up the
garbage container, the buzzer will continue to alert. Figure 4 showed the
Process Block Diagram of the Notification Module.
Figure 4. Process Block Diagram of the Notification Module
17
3. Architectural Diagram
For the architectural diagram of the project, the main component of the
system was a Raspberry Pi used as the microprocessor. It was accompanied
by a camera module for visualization purposes. Additionally, two types of
motors, namely the servo motor and the stepper motor, were incorporated. The
computer system was powered by the power supply, and the sensors and
actuators were directly connected to the microprocessor for data input and
output.
Figure 5. Architectural diagram of the machine
The system's enclosure measured 76.2 centimeters in height and 30.48
centimeters in width. The upper part of the enclosure provided access to where
the garbage fell into the sensing area, with the camera module on top
responsible for detecting the material components of the garbage. Once the
material component of the garbage was identified, the garbage was dropped
and segregated using servo motors. The servo motor moved the garbage to
one of the containers located at the bottom of the enclosure. The ultrasonic
sensor measured the level of garbage in the receptacles, and the buzzer
module alerted if the garbage reached its full capacity and could no longer be
accommodated. Figure 5 showed the architectural design of the garbage
enclosure.
18
Figure 6. Architectural Diagram of the Enclosure
19
4. Schematic diagram
For the schematic diagram of the system, the Raspberry Pi
microprocessor was connected to the servo motor via GPIO 25. The
microprocessor controlled the servo using analog output, while the remaining
VCC and GND of the servo were connected to the 3.3 volts power supply. The
two ultrasonic distance sensors were connected to the microprocessor via trig
into GPIO 27 and GPIO 23, and echo into GPIO 22 and 24. The microprocessor
received data from the ultrasonic distance sensors through analog input. For
the buzzer module, a 1K resistor was connected to GPIO 17 of the
microprocessor and then connected to the transistor. The VCC of the transistor
was connected to the GND of the buzzer, and the VCC of the buzzer was
connected to the 3.3 volts power supply module. The fan motor was powered
by a 12 volts power supply, while the two LED lights were connected to the 3.3
volts
power
supply
module.
Figure 7. Schematic Diagram of the Motor and Sensor machine
20
5. List of Materials
Hardware Materials
●
Raspberry Pi
●
Motor Driver
●
Raspberry Pi Camera Module
●
Servo Motor
●
Jumping Wires
●
Plywood for enclosure
●
Plastic Box
●
12v ac dc charger
●
MB102 Solderless Breadboard Power Supply Module
●
Ultrasonic Sensor Distance Measuring Module HC-SR04
●
Buzzer module (passive)
●
LED
●
Transistor
●
Resistor
Software Materials
●
Tensor Flow Lite
●
Python
●
Open CV
21
6. Cost of Materials
Table 1. The tabular form of Cost of the Materials
Materials
Price
Raspberry Pi Model b
₱ 4,049
Raspberry Pi Camera Module
₱ 192
Servo Motor
₱ 127
9 volts Power Supply
₱ 125
Jumping wires
₱ 109
MB102 Solderless Breadboard Power
₱ 68
Supply Module
Plastic Box
₱ 30
Plywood for enclosure and Lid
₱ 200
Resistor
₱5
Transistor
₱5
12v ac dc charger
₱ 90
Ultrasonic Sensor Distance
₱ 98
Measuring Module HC-SR04
Buzzer module (passive)
₱ 21
Total
₱ 5,119
22
7. Project Timeline
Table 2. Gantt Chart of the Capstone Project
2022
Apr
May
Jun
Apr
May
Jul
May
Apr
Apr
Take Name
04
y
n
05
06
n
07
Aug
Sep
Oct
Nov
Dec
2023
2023
Jan Feb Mar
Dec
Jul
May
Apr
08
09
10
11
12
01 02 03
Project
Consultation
Project Proposal
Project
Development
Project
Consultation
Project
Finalization
Revision
Project
Development
Project
Finalization
Final Defense
The Project consultation occurred throughout the entire month of April,
and the project proposal took place in May. The project development spanned
four months from June to September. In October, the project consultation
started, and the project finalization occurred in September. However, there was
23
a revision on the project in December. The project development and finalization
continued in January and February, and finally, in March, the project was
defended.
8. Dataset model
The project's dataset model was titled "IMAGE CLASSIFICATION OF
BIODEGRADABLE AND NON-BIODEGRADABLE WASTE MATERIALS
USING CONVOLUTIONAL NEURAL NETWORKS (CNN)", and it was trained
by Daphne G. Salomon. The study focused on developing an image classifier
that could categorize waste materials into two groups: Biodegradable and NonBiodegradable, utilizing Convolutional Neural Networks (CNN).
The CNN model employed in the study processed input images through
a sequence of convolutional, pooling, and fully connected layers to generate
the output. The performance of the image classifier was evaluated using
various metrics and a confusion matrix.
The image classification model encompassed six waste classifications,
including three biodegradable materials: Fruit Waste, Paper Waste, and
Vegetable Waste, as well as three non-biodegradable materials: Glass Bottle
Waste, Metal Can Waste, and Plastic Waste.
To optimize the model's deployment, four CNN models were converted
into TensorFlow Lite models. These models included:
- model_ResNet152_v2_epoch50.tflite with a file size of 226 megabytes.
- model_VGG16_v3_epoch50.tflite with a file size of 57.1 megabytes.
- model_MobileNet_v1_epoch50.tflite with a file size of 14.2 megabytes.
- model_inception_v2_epoch50.tflite with a file size of 87.1 megabytes.
24
9. Hardware Calibration and Test Result
I. Development Process
The next stage of development involved creating a solution process that
included both software and hardware components required for system
development. This encompassed development tools, programming languages,
materials, and apparatus.
For the configuration of the microprocessor, which is the Raspberry Pi
computer, additional devices such as a monitor, keyboard, and mouse were
needed to access and successfully configure the computer. The first step
involved installing the latest operating system, which is the Raspberry Pi
Operating System based on Debian version 11 (bullseye). Setting up the
graphical user interface (GUI) and performing installation updates were
essential to ensure the system had the latest package versions. Python
package updates were also necessary as the system heavily relied on the
Python programming language for its scripts. The camera library was updated
last since it was required to support the embedded camera in the
microprocessor.
The installation of a Python virtual environment was carried out to
manage Python packages for different projects. Utilizing a Python virtual
environment helped avoid installing Python packages globally, which could
potentially conflict with system tools or other projects. Once the virtual
environment was set up, TensorFlow and OpenCV were installed. These tools
enabled on-device machine learning, allowing the system to run trained models
on the Raspberry Pi. The dataset used was obtained from the TensorFlow
website's repositories and consisted of an object detection model capable of
detecting specific objects. Since the system controlled a motor, the installation
of the General-Purpose Input and Output (GPIO) library was necessary, along
with the PIGPIO Factory Library, which specialized in controlling the servo
motor, within the virtual environment.
The hardware component of the system body was constructed using 4x4
wood posts that formed a box frame and were covered with plywood. The
25
bottom part of the system had two drawers for containers of non-biodegradable
and biodegradable components. The installation of the separator flap, along
with the servo motor, enabled the machine to separate the biodegradable and
non-biodegradable components. Ultrasonic distance sensors were attached to
the supports in the receptacles to measure the level of garbage. The camera
sensor and buzzer were installed in the upper part of the system. The
component wires were organized using zip ties and cable clips, while PVC
clamps were used for cable management. The system's electronics were
placed inside a plastic enclosure to ensure proper protection from heat and
moisture.
II. Calibration
Preparation:
•
Ensured that the sorting machine was placed in a suitable location with
proper lighting and a stable power supply.
•
Verified that all the components, including the microprocessor, sensor,
motors, and notification module, were properly connected and
functioning.
Initial Configuration:
•
Set up the image-based classification model on the microprocessor,
which was responsible for processing the collected data.
•
Configured the model to distinguish between biodegradable and nonbiodegradable materials based on visual characteristics.
•
Established the classification thresholds or parameters to differentiate
the two types of materials.
Biodegradable Material Calibration:
•
Gathered a set of known biodegradable materials, such as food waste
or plant-based products.
•
Introduced each biodegradable item, one at a time, into the sorting
machine's input area.
26
•
Allowed the sensor to capture images of the biodegradable materials
and processed them using the image classification model.
•
Verified that the machine correctly identified and classified each
biodegradable item as expected.
•
Adjusted the classification thresholds or parameters in the model to
improve accuracy if there were any misclassifications.
Non-biodegradable Material Calibration:
•
Collected a set of known non-biodegradable materials, such as plastic,
glass, or metal objects.
•
Repeated the process described in step 3, but this time with the nonbiodegradable materials.
•
Ensured that the machine accurately classified the non-biodegradable
items as intended.
•
Made necessary adjustments to the classification thresholds or
parameters if any misclassifications occurred.
Integration Calibration (Garbage Level):
•
Integrated the ultrasonic sensor into the system to measure the garbage
level in the trash can.
•
Determined the appropriate ultrasonic sensor range and positioned it
correctly to obtain accurate measurements.
•
Tested the sensor by filling the trash can with different levels of garbage
and confirmed that the readings corresponded to the actual levels.
•
Adjusted any settings or thresholds related to garbage level detection to
ensure reliable and accurate notifications.
Validation and Finalization:
•
Conducted comprehensive tests using a variety of real-world garbage
items, including both biodegradable and non-biodegradable materials.
•
Evaluated the machine's performance by introducing these items and
verifying that it correctly classified them based on the established
criteria.
27
•
Monitored the garbage level measurements and ensured that the
notification module provided accurate alerts to the user.
Documentation:
•
Maintained detailed records of the calibration process, including the
materials used, adjustments made, and the machine's performance
during validation tests.
•
Documented the final configuration settings, classification thresholds,
and any specific instructions or guidelines for future calibration or
troubleshooting.
28
10. Process Flow on Testing
The first step in process flow testing is to prepare the test plan and the
test environment. This ensures that there are no unnecessary failures when
executing the test. If additional testing is required, the process will return to the
preparation stage. If not, the test results will be analyzed. If the tester is not
satisfied with the results, the process will return to the execution stage, and
testing will continue until satisfaction is achieved. Once the tester is satisfied
with the results, the system will be considered complete and will proceed to the
deployment stage. Figure 8 illustrates the process flow in the testing figure.
Figure 8. Process Flow on Testing
29
Chapter V
RESULTS AND DISCUSSION
1. Whole Prototype Machine
The Prototype machine consists of two distinct processes. The first
process is the sorting process, which is responsible for segregating the
biodegradable and non-biodegradable components of the garbage. This
process analyzes the garbage using computer vision to determine its
classification and then employs mechanisms such as servo motors to
separate and drop the garbage into the appropriate categories. The second
process is the monitoring process, which continuously monitors the garbage
level inside the receptacles. It utilizes sensors, such as ultrasonic sensors, to
measure the level of garbage present. If the garbage reaches a maximum
capacity, the system triggers a notification to alert the intended user about the
state of the machine. This notification can be in the form of a buzzer or other
alerting system. These two processes work together to enable efficient
garbage sorting and monitoring in the Prototype machine.
Figure 9. Whole System Prototype Machine
30
The System prototype machine is composed of microprocessor, servo
motor, camera sensor, power supply, power distributor, jumping wires, buzzers,
transistor, resistor, and LED.
2. Receptacles
The receptacle of the system is located at the bottom part of the machine
and serves as a container for holding the garbage. It is designed with two
receptacle, one specifically designated for the biodegradable components and
the other for the non-biodegradable components. This segregation allows for
efficient sorting and disposal of the garbage. To enhance convenience and
ease of maintenance, the receptacles can be installed with trash bags. The
trash bags can be placed inside the drawers, providing a practical solution for
collecting and disposing of the garbage. This way, when the drawers are full or
need to be emptied, the trash bags can be easily removed and replaced,
ensuring a hygienic and organized waste management system.
Figure 10. Receptacles for the garbage container.
31
3. Enclosure and Electronics
The enclosure plays a crucial role in safeguarding the electronic components
of the system from potential damage caused by heat and moisture. It is
designed with specific features to ensure optimal protection and functionality.
To address heat-related concerns, the enclosure is equipped with a built-in fan.
The fan helps in reducing the temperature inside the enclosure by promoting
air circulation and dissipating heat generated by the electronic components.
This cooling mechanism prevents overheating and ensures the components
operate within safe temperature ranges. The choice of plastic material for the
enclosure is deliberate as plastic is non-conductive and provides insulation.
This insulation property helps protect the electronic components from moisture,
dust, and other environmental factors that could potentially cause damage.
Inside the enclosure, various electronic components are present, including the
microprocessor (such as the Raspberry Pi), power supply, power distributor,
transistor, resistor, and jumping wires. These components are carefully
arranged and connected to ensure proper functioning of the system.
Figure 11. System Enclosure and Electronics
32
4. Garbage Level Detector Module
The garbage level detection system worked by using two ultrasonic
distance sensors facing downward to the receptacles to measure the distance
of the contents of the receptacles. In this method, the ultrasonic distance
sensor would send signals to the microprocessor to measure the level of
garbage.
Figure 12. Level Detector System
33
5. Notification Module
The system's notification system was made up of two primary
components: the Buzzer module and the RGB Light Emitting Diode (LED).
When the receptacles were full of garbage, the buzzer would warn the
designated user while also blinking the red light. Additionally, when the intended
user threw biodegradable garbage, the LED would light up green, and nonbiodegradable garbage would light up blue, to notify and alert the user of the
type of garbage they threw.
Figure 13. Buzzer module and RGB Light Emitting Diode (LED)
34
6. Separator Flap
The Separator flap was powered by a Servo motor. This flap worked
for the separation of biodegradable garbage and non-biodegradable garbage.
When the garbage was biodegradable, the flap would turn clockwise, and it
would turn counterclockwise for the non-biodegradable garbage.
Figure 14. Servo Motor
Figure 15. Separator flap powered by motor.
35
7. Camera Sensor
The system's camera sensor was a Raspberry Pi camera module that
would take a picture of the garbage. The optical sensor provided data to the
microcontroller for the segregation process.
Figure 16. Raspberry Pi Camera Module
36
8. Process of activating the machine using the command line of
Linux in Raspberry pi operating system.
Figure 17 shows the opening of the "tflite1" folder which contained all the data,
including the Python Virtual Environment, Models, and Python Libraries.
Figure 17. Opening the tflite1 folder
37
Figure 18 shows the activation of the Python virtual environment, which
was done to manage Python packages inside the "tflite1" folder.
Figure 18. Opening the Python Virtual Environment
Figure 19 shows the activation of the General Pin Input Output (GPIO)
library, which was done to enable communication between programs and
scripts with the GPIO pins.
Figure 19. Activation of General Pin Input Output (GPIO) library
38
Figure 20 shows the activation of the garbage classification program by
running the python script "classify.py." The program predicts the top prediction
class index, which is a value between 0 and 1, and the top prediction score.
These results are then printed on the command line. The frame displayed is
composed of 30 by 30 pixels and shows the categorization of the picture.
Figure 20. Activation of classification of waste program
39
9. Confusion Matrix
For the testing and evaluation of the machine's performance using four
CNN models, a confusion matrix is used to assess and test the machine's
capabilities. In Figure 21, the confusion matrix result is depicted for the
ResNet152 model. Most of the garbage was correctly classified, with only a
few instances of incorrect classifications. The ResNet152 model
successfully categorized 596 pieces of garbage. Additionally, the model had
three false positives, indicating that three biodegradable garbage items
were mistakenly classified as non-biodegradable. It also had one false
negative, meaning that one non-biodegradable garbage item was predicted
as biodegradable.
Figure 21. The confusion matrix of ResNet152 and the incorrectly classified
garbage materials both in false positive and negative
40
Figure 22 depicts the result of the confusion matrix in the VGG16 model.
Most of the garbage was correctly identified, with only a few exceptions. The
VGG16 model accurately classified 581 items of garbage. However, it had
the highest number of false positive and false negative values among the
models. Approximately 14 biodegradable garbage items were projected as
non-biodegradable, and 5 non-biodegradable garbage items were predicted
as biodegradable.
Figure 22. The confusion matrix of VGG16 and the incorrectly classified
garbage materials both in false positive and negative
41
Figure 23 depicts the result of the confusion matrix in the MobileNet
model. Most of the garbage was correctly identified, with only a few
exceptions. The MobileNet model accurately classified 598 items of
garbage. It had a low number of false positive and false negative values.
Approximately 2 biodegradable garbage items were projected as nonbiodegradable, and no non-biodegradable garbage items were predicted as
biodegradable.
Figure 23. The confusion matrix of MobileNet and the incorrectly classified
garbage materials both in false positive and negative
42
The confusion matrix of the Inceptionv3 model is shown in Figure 24.
The Inceptionv3 model correctly categorized 598 pieces of garbage, with
around 300 true positives and 298 true negatives. It also has the fewest
false positive and false negative results, with about 0 biodegradable waste
predicted as non-biodegradable garbage and 2 non-biodegradable garbage
forecasted as biodegradable garbage.
Figure 24. The confusion matrix of Inceptionv3 and the incorrectly classified
garbage materials both in false positive and negative
In summary, the evaluation of the four models revealed varying degrees
of performance in classifying garbage. The VGG16 model had a relatively high
number of misclassifications. The ResNet152 models showed lower rates of
false positives and false negatives compared to the VGG16 model. The
Inceptionv3 model and MobileNet model exhibited the highest overall accuracy
and the fewest misclassifications. Based on these evaluation metrics, both the
Inceptionv3 model and MobileNet model proved to be the most reliable and
accurate models for garbage classification.
43
10. Discussion
The machine consists of two main processes: sorting and monitoring.
The sorting process segregates the garbage into biodegradable and nonbiodegradable components, while the monitoring process keeps track of the
garbage level inside the receptacles and notifies the user about the machine's
status. The prototype machine includes various components such as a
microprocessor, servo motor, camera sensor, power supply, power distributor,
jumping wires, buzzers, transistors, resistors, and LEDs. The receptacle,
located at the bottom of the system, contains two drawers—one for
biodegradable components and the other for non-biodegradable components.
Trash bags can be installed in the receptacles for convenience. To protect the
electronic components from heat and moisture, the machine has a plastic
enclosure with a built-in fan for heat reduction. Inside the enclosure, there are
electronic components including a microprocessor, power supply, power
distributor, transistors, resistors, and jumping wires that connect to the sensors
and motors.
The garbage level detection system utilizes two downward-facing ultrasonic
distance sensors to measure the distance of the contents inside the
receptacles. These sensors send signals to the microprocessor, which
measures the garbage level based on the received signals. The notification
system of the machine consists of a Buzzer module and an RGB LED. When
the receptacles are full of garbage, the buzzer sounds an alarm, and the red
light on the LED blinks. Additionally, the LED lights up green when
biodegradable garbage is thrown, and it lights up blue for non-biodegradable
garbage, providing notification and alerting the user about the type of garbage
being thrown. The Separator flap, powered by a servo motor, is responsible for
separating
biodegradable
and
non-biodegradable
garbage.
When
biodegradable garbage is detected, the flap turns clockwise, and when nonbiodegradable garbage is detected, it turns counterclockwise.
44
In the evaluation of the machine's performance, four different models
(ResNet152, VGG16, MobileNet, and Inceptionv3) were analyzed using
confusion matrices. These matrices provided insights into the models'
performance in classifying garbage as either biodegradable or nonbiodegradable.The ResNet152 model, as shown in Figure 30, exhibited a high
level of accuracy, correctly categorizing the majority of the garbage samples.
Out of the total garbage items, 596 were successfully classified. The model had
three false positives, where three biodegradable garbage items were wrongly
identified as non-biodegradable. Additionally, there was one false negative,
indicating one non-biodegradable garbage item was mistakenly predicted as
biodegradable.
The VGG16 model, depicted in Figure 31, demonstrated good overall
performance, accurately classifying 581 items of garbage. However, it had the
highest number of false positives and false negatives among the evaluated
models. Approximately 14 biodegradable garbage items were misclassified as
non-biodegradable, and 5 non-biodegradable garbage items were incorrectly
identified as biodegradable. The MobileNet model, shown in Figure 32,
achieved satisfactory performance by accurately classifying 598 items of
garbage. It had a low number of false positives and false negatives, with only
around 2 biodegradable garbage items being projected as non-biodegradable.
There were no instances of non-biodegradable garbage being predicted as
biodegradable.The Inceptionv3 model's confusion matrix, illustrated in Figure
33, exhibited the best performance among the evaluated models. It correctly
categorized 598 pieces of garbage, achieving a high number of true positives
and true negatives, with approximately 300 instances of each. Notably, this
model had the fewest false positives and false negatives. There were no
instances of biodegradable waste being predicted as non-biodegradable.
45
Chapter VI
CONCLUSION AND RECOMMENDATIONS
1. Conclusion
The project "Intelligent Garbage Segregation Sorting Machine"
concluded that the development of a prototype of an automatic garbage sorting
machine was possible by using the technology of the Raspberry Pi, which was
the system's microcomputer, and motors that classified the garbage. By using
the image classification model, the computer could categorize nonbiodegradable and biodegradable components. It was also determined that all
four models performed reasonably well in classifying garbage, with varying
degrees of accuracy. Misclassifications were relatively common in the VGG16
model. When compared to the VGG16 model, the ResNet152 models
demonstrated lower false positive and false negative rates. Based on the
evaluation metrics provided, both the Inceptionv3 model and the MobileNet
model displayed the best overall accuracy and the fewest misclassifications,
making them the most dependable and accurate models for garbage
categorization. It was also determined that the machine could use the ultrasonic
sensor to determine the level of rubbish in the bin and successfully inform the
user using the buzzer module.
46
2. Recommendations
For future studies aimed at enhancing the mentioned project, it would be
preferable to utilize an object detection model specialized in recognizing
biodegradable and non-biodegradable garbage. Additionally, incorporating the
Google Coral USB Accelerator would be a valuable addition. This small device
provides hardware acceleration specifically designed for TensorFlow Lite
models, making it ideal for machine learning applications. When used in
conjunction with a Raspberry Pi, the Coral USB Accelerator significantly boosts
the speed of object detection tasks by offloading computationally intensive
operations.
The Google Coral USB Accelerator can greatly enhance the
performance of object detection tasks on a Raspberry Pi, resulting in faster and
more accurate object detection, even for real-time video streams or highresolution images. To improve the machine's functionality, it would be beneficial
to increase the height and width of the machine frame and consider using a
larger flap to accommodate larger-sized garbage items. Moreover, employing
a larger servo motor would generate sufficient power to effectively separate
heavy garbage.
Since the machine is embedded, it would be advantageous to
incorporate Internet of Things (IoT) technology, particularly in the machine's
notification module. Furthermore, developing a dedicated application for the
machine would enable remote communication, allowing users to issue
commands and retrieve data such as the current fill level of the bin and the
history of garbage disposed of in the machine.
47
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smart trash as the smart environment concept. Journal of Physics:
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51
APPENDIX A
List of Materials for the Intelligent Garbage Segregation Sorting Machine
52
Figure 25. Raspberry Pi Module
53
Figure 26. Raspberry Pi Camera
54
Figure 27. Ultrasonic Distance Sensor
55
Figure 28. Jumping Wires
56
Figure 29. Resistor
57
Figure 30. 12 Volts Fan
58
Figure 31. 12 volts Power Supply
59
Figure 32. Breadboard
60
Figure 33. Buzzer Module
61
Figure 34. Servo Motor
62
Figure 35. RGB LED
63
Figure 36. Plywood
64
APPENDIX B
Building process of the prototype machine
65
Figure 37. Building the Box Frame
66
Figure 38. Electronics Installations
67
APPENDIX C
Relevant Codes
68
I.
Code for the Classifying Biodegradable Garbage and Non
Biodegradable Garbage
import cv2
import time
import RPi.GPIO as GPIO
import tflite_runtime.interpreter as tflite
import numpy as np
from tflite_support.task import core
from tflite_support.task import processor
from tflite_support.task import vision
import RPi.GPIO as GPIO
from gpiozero import Servo
from time import sleep
import pigpio
from gpiozero import Servo
from time import sleep
from gpiozero.pins.pigpio import PiGPIOFactory
factory = PiGPIOFactory()
servo = Servo(25, pin_factory=factory)
GPIO.setmode(GPIO.BCM)
GPin=6
BPin=5
GPIO.setup(BPin,GPIO.OUT)
GPIO.setup(GPin,GPIO.OUT)
cap = cv2.VideoCapture(0)
69
cap.set(cv2.CAP_PROP_FPS, 30)
model_path = '/home/pi/Downloads/model-DenseNet201-epoch67.tflite'
interpreter = tflite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
while True:
ret, img = cap.read()
img = cv2.resize(img, (224, 224))
cv2.imshow('Frame', img)
key = cv2.waitKey(1) & 0xFF
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
input_shape = input_details[0]['shape']
preprocessed_image = cv2.resize(img, (input_shape[2], input_shape[1]))
preprocessed_image = preprocessed_image.reshape(input_shape)
preprocessed_image = preprocessed_image.astype('float32') / 255.0
interpreter.set_tensor(input_details[0]['index'], preprocessed_image)
interpreter.invoke()
output_tensor = interpreter.get_tensor(output_details[0]['index'])
# Decode the output tensor
a = top_index = output_tensor.argmax()
b = top_score = output_tensor[0][top_index]
# Print the top prediction class index and score
print("Top prediction class index: ", a)
print("Top prediction score: ", b)
70
if a == 0:
GPIO.output(GPin,1)
servo.min()
time.sleep(2)
GPIO.output(GPin,0)
servo.mid()
time.sleep(2)
elif a == 1:
GPIO.output(BPin,1)
servo.max()
time.sleep(2)
GPIO.output(BPin,0)
servo.mid()
time.sleep(2)
cap.release()
cv2.destroyAllWindows()
71
II.
Code for Identifying the level of Biodegradable Garbage and NonBiodegradable Garbage in the bin.
import cv2
import time
import RPi.GPIO as GPIO
import tflite_runtime.interpreter as tflite
import numpy as np
from tflite_support.task import core
from tflite_support.task import processor
from tflite_support.task import vision
import RPi.GPIO as GPIO
from gpiozero import Servo
from time import sleep
import pigpio
from gpiozero import Servo
from time import sleep
from gpiozero.pins.pigpio import PiGPIOFactory
factory = PiGPIOFactory()
servo = Servo(25, pin_factory=factory)
GPIO.setmode(GPIO.BCM)
GPin=6
BPin=5
GPIO.setup(BPin,GPIO.OUT)
GPIO.setup(GPin,GPIO.OUT)
import RPi.GPIO as GPIO
72
import time
GPIO.setmode(GPIO.BCM)
buzzPin=17
trigPin=24
echoPin=23
trigPin1=27
echoPin1=22
RPin=16
GPIO.setup(RPin,GPIO.OUT)
GPIO.setup(buzzPin,GPIO.OUT)
GPIO.setup(trigPin,GPIO.OUT)
GPIO.setup(echoPin,GPIO.IN)
GPIO.setup(trigPin1,GPIO.OUT)
GPIO.setup(echoPin1,GPIO.IN)
try:
while True:
GPIO.output(trigPin,0)
time.sleep(2E-6)
GPIO.output(trigPin,1)
time.sleep(10E-6)
GPIO.output(trigPin,0)
while GPIO.input(echoPin)==0:
pass
echoStartTime=time.time()
while GPIO.input(echoPin)==1:
pass
echoStopTime=time.time()
pingTravelTime=echoStopTime-echoStartTime
result2=int(pingTravelTime*1E6)
GPIO.output(trigPin1,0)
time.sleep(2E-6)
GPIO.output(trigPin1,1)
time.sleep(10E-6)
73
GPIO.output(trigPin1,0)
while GPIO.input(echoPin1)==0:
pass
echoStartTime1=time.time()
while GPIO.input(echoPin1)==1:
pass
echoStopTime1=time.time()
pingTravelTime1=echoStopTime1-echoStartTime1
result1=int(pingTravelTime1*1E6)
print(result1)
if result1<600:
GPIO.output(buzzPin,GPIO.HIGH)
GPIO.output(RPin,1)
time.sleep(1)
GPIO.output(buzzPin,GPIO.LOW)
GPIO.output(RPin,0)
time.sleep(1)
if result2<600:
GPIO.output(buzzPin,GPIO.HIGH)
GPIO.output(RPin,1)
time.sleep(1)
GPIO.output(buzzPin,GPIO.LOW)
GPIO.output(RPin,0)
time.sleep(1)
if result2>600:
time.sleep(.1)
if result1>600:
time.sleep(.1)
except KeyboardInterrupt:
GPIO.cleanup()
print('goo')
74
cap.release()
cv2.destroyAllWindows()
75
APPENDIX D
Grammarian Certificate
76
77
APPENDIX E
Plagiarism Evaluation Result
78
79