DEVELOPMENT OF AN INTELLIGENT POULTRY BIRD INCUBATION

SYSTEM FOR SUSTAINABLE SUBSISTENCE FARMING.

BY

AGYO MICHAEL AGYO

2018/1/70901ET

DEPARTMENT OF MECHATRONICS ENGINEERING

FEDERAL UNIVERSITY OF TECHNOLOGY
MINNA, NIGER STATE

OCTOBER, 2024

1
DEVELOPMENT OF AN INTELLIGENT POULTRY BIRD INCUBATION
SYSTEM FOR SUSTAINABLE SUBSISTENCE FARMING.

BY

AGYO MICHAEL AGYO

2018/1/70901ET

PROJECT REPORT SUBMITTED TO THE DEPARTMENT OF

MECHATRONICS ENGINEERING, SCHOOL OF ELECTRICAL
ENGINEERING AND TECHNOLOGY, FEDERAL UNIVERSITY OF
TECHNOLOGY, MINNA

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE

AWARD OF THE DEGREE OF BACHELOR OF ENGINEERING IN
MECHATRONICS ENGINEERING

OCTOBER, 2024

2
 DECLARATION

I Agyo Michael Agyo herby attest that this work titled “Development of An Intelligent

Poultry Bird Incubation System for Sustainable Subsistence Farming” and its

contents is an original study, undertaken by me and has not been submitted by any other

person for a degree or any other qualification in fulfilment of any undergraduate award

at any other institution. All information gotten from relevant works have been cited and

referenced. I hereby relinquish copyright of this project to Federal University of

Technology, Minna.

AGYO MICHAEL AGYO SIGNATURE & DATE

2018/1/70901ET ------------------------------

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 ACKNOWLEDGEMENT

I would like to appreciate Dr Taliha A. Folorunso for his guidance and supervision from
my early years at the Federal University of Technology to the present. He has been a
mentor and guide to me.

Likewise, I wish to recognise the efforts, dedication, and contributions of Engr. Dr.
Jibril A. Bala to this study. Hisj meticulous and straightforward guidance reformed my
thinking and positively impacted the development of the artificial incubator system and
this project document.

Similarly, I would like to give my gratitude to my doting father, my brothers and my

ever-loving mothers, for love, motivation, discipline, refuge and for nurturing greatness
in me. To my family at large and all the names and faces behind my smile, I am grateful.

Additionally, to Engr.Dr. Kufre E. Jack I would like to show appreciation and

recognition for his endearing efforts in coordinating and supervising this work to
completion.

Furthermore, I would like to extend my regards Engr. Prof. Oluboji O.A and the
students of Mechanical Engineering, who aided in the development of the Intelligent
Incubator system.

Lastly, I extend my regards to the department of Mechatronics Engineering and its staff
for inducting the knowledge, skills, and values required of an engineer. I also want to
thank Acham Stephen, Khalid, and Ruel Kantiok for providing counsel and support for
the duration of the incubator construction.

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TABLE OF CONTENT

DECLARATION..........................................................................................................ii

CERTIFICATION .................................................... ivError! Bookmark not defined.

ACKNOWLEDGEMENT ........................................................................................... v

TABLE OF CONTENT.............................................................................................. vi

ABSTRACT .............................................................................................................. viii

CHAPTER ONE .......................................................................................................... 8

1.0 INTRODUCTION............................................................................................ 8

1.1 Background Of Study ........................................................................................ 8

1.2 Problem Statement ..................................................................................... 9

1.3 Aim And Objectives ................................................................................ 10

1.4 Significance Of Study .............................................................................. 10

1.5 Scope Of Study ........................................................................................ 11

1.6 Project Organisation................................................................................. 11

1.7 Definition Of Terms ................................................................................. 12

CHAPTER TWO ....................................................................................................... 13

2.0 LITERATURE REVIEW ............................................................................. 13

2.1 Chapter Introduction ................................................................................ 13

2.2 Egg Fertilization....................................................................................... 13

2.3 Incubator Temperature And Humidity..................................................... 14

2.3 Egg Orientation And Tilting .................................................................... 15

2.4 Review Of Literature ............................................................................... 16

2.5 Chapter Summary .................................................................................... 30

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CHAPTER THREE ................................................................................................... 31

3.0 METHODOLOGY ........................................................................................ 31

3.1 CHAPTER INTRODUCTION .................................................................... 31

3.2 System Overview ..................................................................................... 31

3.3 Design Implementation Of Control System ............................................. 35

3.4 Embedded Control System Development ................................................ 38

3.5 Performance Evaluation ........................................................................... 44

3.6 Chapter Summary .................................................................................... 45

CHAPTER FOUR ...................................................................................................... 46

4.1 CHAPTER INTRODUCTION ............................................................. 46

4.2 Design of Embedded Poultry Bird Egg Incubation System..................... 46

4.3 System Development And Implementation ............................................. 49

4.3 System Performance Evaluation .............................................................. 52

4.4 Chapter Summary .................................................................................... 55

CHAPTER FIVE ....................................................................................................... 57

5.0 CONCLUSION AND RECCOMENDATIONS .................................. 57

5.1 Conclusion ............................................................................................... 57

5.2 Recommendations .................................................................................... 57

REFERENCES........................................................................................................... 59

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 ABSTRACT

Poultry farming and consumption are widespread activities, which account for 30% of
the world’s protein consumption and serve as a lucrative source of income, practiced
at small and large scales. Due to high demands, artificial poultry egg incubation is
employed as a predictive measure to offset supply with demand. Artificial egg
incubation, however, is plagued with culpabilities, which include but are not limited to
inadequate monitoring of developing zygotes and inefficient implementation of control
systems for the purpose of egg embryo hatching. A responsive, controllable, intelligent
poultry bird egg incubator system that utilises hardware components such as a DHT11
sensor, DC fan, DC motor, heating elements, and an Arduino Uno and ESP 32
microcontrollers is developed to mitigate these effects. This system is governed by a
Mamdani Fuzzy Inference system, which maps out membership functions between the
humidity and temperature inputs of the system and establishes a rule base for governing
the behaviour of system actuator elements in response to real-time temperature and
humidity data collated by the DHT11 sensor. Through extensive testing, this study
achieved its aim of developing an intelligent artificial poultry bird egg incubation
system with a system logic accuracy of 70%, and achieved an average system response
time of 2.2 seconds, which improved the quality of poultry egg hatching for the duration
of poultry egg incubation. The developed incubator system, with an egg-carrying
capacity of 90 eggs, is capable of deployment for small and medium poultry farming
and serves to promote the industrialization of poultry farming.

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 CHAPTER ONE

1.0 INTRODUCTION

1.1 Background of Study

Farming as an activity, dates to the earliest years of human society and is under constant

development as the dietary needs of society increase on a large scale. Large-scale

poultry farming has roots in China and Egypt, who are credited with designing the

earliest artificial incubation procedures. Poultry accounts for over 30% of animal

protein consumption globally and is projected to rise with population increase (George

and George, 2023). Incubation in poultry birds which can be defined as the definitive

process by which poultry eggs and nurtured and developed to birth healthy offspring,

plays a key role in the success or failure of a poultry farm. Naturally, incubation of eggs

is undertaken by poultry birds and usually takes up to twenty-one (21) days to achieve

hatchling (Adegboyega et al., 2021).

Natural Incubation is necessary for the parental relationship between the chick and hen

and essential to the physical development of the chicks (Sunday et al., 2020). Due to

the various shortcomings of natural incubation methods which include high mortality

rate, high labour and time cost, unfavourable climate conditions and inadequate

monitoring and control actions over incubation period (Sunday et al., 2020).

Artificial incubation as it pertains to poultry egg development can be described as the

process of simulating natural egg incubation parameters, through the provision of

sensors, actuators, and control elements to stimulate the development of egg embryo.

Artificial incubation has become relevant and widespread for the mass production and

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continued sustenance of poultry bird farming. As such various research has been made

to identify determining factors that influence egg development, which guide the diverse

methods and control systems implemented for poultry bird incubation.

Artificial Incubation provides innumerable benefits to poultry farming and the

development of poultry birds but there are areas where it falls short. Problems such as

inadequate monitoring, and inadequate control system to properly regulate process

parameters (Seob et al., 2021; Adegboyega et al., 2021).The aim of this project is to

develop a control system for poultry incubation system with IoT communication to

provide a reliable incubator system that can be monitored remotely, by integrating a

microcontroller system with wireless network capabilities to monitor and control the

operating parameters and functions of the egg incubation system.

1.2 Problem Statement

An artificial incubation process is necessary for the commercial-scale production of

poultry birds. Significant challenges facing artificial incubation include the effect of the

interrupted incubation process on egg development caused by power outages, faulty

design, poor component selection and improper implementation of intelligent control

systems (George and George, 2023).

To improve the poultry farming standard, a control system is required to be developed

and integrated. The introduction of an intelligent system to poultry egg incubation is

for the sole purpose of providing a quantifiable and efficient means of measuring the

incubator microclimate and evaluating the effects these have on egg incubation, along

with the added benefits of system flexibility, backup power source and power

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management efficiency, remote monitoring capabilities and egg fertility preservation

(Raju et al., 2024; Cahyo et al., 2024).

1.3 Aim and Objectives

This project aims to develop an intelligent poultry bird incubation system for

sustainable subsistence farming of poultry birds. This aim was achieved through the

following objectives:

i. To design an embedded system to monitor and control parameters of poultry bird

incubation.

ii. To develop and integrate a control system for poultry bird incubators.

iii. To evaluate the performance of the control system.

1.4 Significance of Study

The significance of this study is to improve the quality and quantity of hatching among

poultry bird eggs by providing a feedback control system to monitor and regulate the

incubation conditions of poultry eggs in real time, to understand the optimal control

models and environment-system relationships that positively influence poultry egg

development and further improvement of artificial incubation systems, to research and

evaluate the strengths and shortcomings of previous poultry egg incubation works

carried out and to develop a marketable egg incubator prototype with the capacity of

catering to the needs of commercial poultry farmers on a small and medium scale.

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1.5 Scope of Study

This project takes into consideration the proper range of external variables, such as

temperature, humidity and egg orientation angle required for successful incubation of

fertilized chicken eggs and how these parameters are monitored and controlled by a

microcontroller system to ensure increase in poultry egg hatching for small and medium

scale poultry farmers. This project encompasses several technologies such as fuzzy

logic control models, implemented by the Arduino Uno and ESP 32 microcontrollers,

which monitors the incubation process and controls system elements. This system is

capable of monitoring and regulating temperature and humidity conditions, conduct egg

tilting for efficient egg incubation, communication with remote devices for control and

monitoring activities. The limitations of this project comprise of limited egg holding

capacity and the absence of egg candling operations.

1.6 Project Organisation

Chapter One introduces the concept of incubation, and the different methodologies and

types of incubation. Provide a brief review of the various shortcomings of incubation

methods and specify the criteria, aim and objectives of this project.

Chapter Two provides an in-depth analysis of literature and works, centred on the topic

of artificial autonomous incubation and the various parameters, to be taken into

consideration for adequate monitoring and control over the poultry incubation process.

Chapter Three proposes methodology and control actions considered for the

construction and monitoring of an autonomous artificial incubator. It draws conclusions

and data parameters from the literature reviews and inculcates innovative ideas for

improving and modifying the incubation system being developed.

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Chapter Four presents the results and findings of the designed incubator system.

Tabulating and quantifying the achievable performance of the system for analysis,

evaluation, and comparison. For the improvement and modification of the incubator

system, operational logic and data set used for the development of the intelligent poultry

egg incubator.

Chapter Five concludes the development, testing and evaluation of the incubator

systems performance with respect to the desired performance capabilities expected of

the system. It provides recommendations and referenceable data for the improvement

and modification of the designed system.

1.7 Definition of Terms

Terminologies and terms used are:

Zygote: This refers to a fertile poultry (chicken) egg required to undergo the incubation

process.

Candling: This refers to the systematic process which involves screening the eggs via

light illumination in a dark room to ensure the fertility of the egg embryo.

Incubation: The development stage by which, fertile eggs are nurtured into young

healthy chicks.

Hatchability: This refers to the percentage ratio of an incubator system, to develop

zygotes into healthy chicks.

Egg Tilting: Rotary or inclined motion of egg crate carrier to ensure the embryo mixes,

for proper development of zygotes and to prevent the chicks from sticking to the shells

after hatching.

Embryo: This refers to the early stage of development of a poultry egg, which occurs

regardless of egg fertility.

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 CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Chapter Introduction

This chapter aims to explore, analyse, and provide a contrast between existing

parameters and conditions which affect the success rate of artificial poultry egg

incubation from previous works and review and evaluate existing and related works

concerning poultry egg incubation. Thus, constructing an original working idea that

ensures the viability performance and safety of an automated embedded poultry egg

incubator system.

2.2 Egg Fertilization

Poultry egg for incubation is broadly classified into three categories which are

unfertilized, dead-in-shell, and fertilized eggs (Sheng-Yu et al., 2020). Poultry egg

incubation which is the systematic development of an egg embryo cannot be executed

without a zygote.

While fertilized eggs are favoured for incubation, it should be noted that not all zygotes

develop and hatch into healthy chicks. Various characteristic physiological parameters

such as corticosterone and thyroid hormone balances, metabolism, heat production and

gas exchange play a key role in the fertilization of poultry eggs (Tona et al., 2022).

During the incubation process, egg fertility is checked routinely to prevent wastage of

power and contamination of the incubation process. These routine checks are performed

before the 18th day of incubation, which is when the embryo is completely formed

(Sheng-Yu et al., 2020). Egg fertility is determined through the candling process. This

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process can be performed manually or automatically with the aid of image processing,

data analysis and digital cameras used for capturing image data (Lean et al., 2022).

To ensure poultry eggs selected for incubation meet the optimum level of viability, eggs

are stored no more than seven (7) days before incubation as this reduces the possibility

of egg hatching (Pedro et al., 2023). The relationship between long storage, weight loss,

fertility and hatchability have been observed and possesses a negative trend as the

increase in storage period leads to a progressive deterioration of the internal quality of

the egg, which plays a factor in early egg deterioration (Pedro et al., 2023).

2.3 Incubator Temperature and Humidity

Incubator temperature and humidity can be identified as a major measurable factor with

profound influence over the hatchability of developing egg embryo. Temperature and

humidity possess an inverse relationship with each other, where the increase of one

parameter results in a reduction in the other. Therefore, increasing the need to regulate

both parameters to achieve an even state to foster egg development.

Through years of research and experimentation, the optimal temperature and relative

humidity constraints for proper embryo development are marked at 350 C -370 C and

55%, respectively (Karlo et al.,2022). The response of the developing zygote to

deflection of the optimal range results in undesired development. Incubating at

temperature levels above 220 C, leads to premature development, while incubation at

temperature level higher than 370 C results in mortality of the developing zygote

(Adegboyega et al., 2021)

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It has been demonstrated that exposing incubated eggs to pre-incubating for the

duration of egg storage before incubation improves the viability of chicks hatched and

is especially useful when egg storage extends beyond the seven (7) days interval

(Bhavana et al., 2023). Several temperature and humidity sensors are available, but the

DHT11 sensor is highly favoured for monitoring activities in poultry incubation

systems due to its high precision which provides calibrated temperature and humidity

values, size, and real-time delivery of data (Ronaldo et al., 2023)

2.3 Egg Orientation and Tilting

Egg tilting can be identified as an essential procedure during egg incubation to ensure

the safety and viability of chicks. This activity is undertaken majorly by controlled

actuation of motors placed at the ends of the egg tray. An adequate turning angle of 450

speculatively improves the feed efficiency of birds during the growth period and

improves the quality of chicks produced through the incubation process (Tona et al.,

2022).

Although significant research has not been dedicated to establishing this as common

knowledge in the field of artificial poultry incubation, Tona et al., (2022) states that the

orientation of incubated eggs in a vertical direction, with the small end down leads to

an increase in hatch time as compared to when the egg is placed with the small end

facing up. Regardless of the time delay in hatching, the orientation of the incubated egg

in the vertical direction with the small end facing downwards leads to an improved

growth performance (Tona et al., 2022).

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2.4 Review of Related Works

Iraqi et al., (2024) proposed this study, with the aim of studying the effect of thermal

manipulation on the embryonic development of chicken breeder eggs for the duration

of egg incubation. The General Linear Model under the SAS program was employed,

to analyse the effect of thermal stress on the incubating eggs. For this experiment, six

hundred eggs were incubated with a hatch rate of 50%. The effects of thermal stress on

the incubated eggs show a significant improvement in the embryonic weight, yolk sac,

tibia, and heart weight. It also demonstrates that incubating embryos at slightly higher

temperatures than usual can accelerate embryonic development by altering embryo

growth, tissue metabolism, and respiration rate. The application of thermal stress to

incubating eggs should be controlled and applied moderately as it can stunt egg growth

if misapplied.

Raju et al., (2024), developed an infant incubator system, for infant monitoring and

system control. It utilises IoT communication to care for and observe premature and

critically ill new-borns. To achieve the project aim, components including a sound

sensor, heart rate monitor, MQ2 sensor, DHT 11 sensor, accelerometer, Peltier module,

LCD, humidifier, and GSM unit are integrated with an Arduino Wi-Fi R3 micro-

controller and Think Speak IoT storage, to provide adequate support and data

monitoring. The smart Infant Incubator reduces the mortality rate of infants through

timely communication and monitoring as well as optimizing the system's measurable

parameters to ensure the infants' safety. It fails to integrate real-time video data

coverage, oxygen, and respiration rates of the infant to the incubator system and only

has one point of data reception for real-time monitoring.

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Nugraha et al., (2024) designed and implemented an Internet of Things egg incubator

system, developed using a Wemos microcontroller, a DHT 22 temperature and

humidity sensor, a PIR motion sensor, a bulb, a ventilation fan, and rollers for egg tilting.

This system deploys a remote connection communication via the telegram application,

to facilitate communication between the incubator system and user. Using a bang-bang

control model, the system automates its’ tasks according to the temperature and

humidity conditions of the incubator, while identifying the hatching period of eggs in

the incubator utilising the PIR sensor. The results derived show that the system has a

hatch rate of 90%. The downside to this system lies in the fact that the egg capacity of

the system is not sufficient for commercial purposes and the lack of provision for proper

egg maintenance.

Wang et al., (2024) developed a new smoothing technique for the implementation of

bang-bang control, for optimal control problems. A bounded smoothing function,

normalized L2 -norm function is proposed to provide smoothing to a bang-bang control

function. The function approximates the switching function for the sign function which

results in a smooth optimal control profile. The proposed method can be readily adopted

into an indirect calculation method once the switching function has been derived.

In addition, Cahyo et al., (2024) demonstrated the performance capabilities of a

developed, automated egg incubator system that utilises Proportional Integral

Derivative (PID) control. Control instructions were stored in the ESP32 microcontroller

which also allows for wireless communication with the IoT module, responsible for

remote monitoring and control, DHT21 humidity and temperature sensor for incubator

climate monitoring, motors, ventilation fans and voltage protection system for safety

17
and maintenance of the incubator system with egg holding capacity of five hundred

eggs.

Furthermore, Zhou et al., (2023) developed a reliable, efficient and accurate alternative

system for the identification of sterile and fertile incubating duck eggs. The proposed

model is a lightweight detection architecture (LDA) based on the YOLOX-Tiny

framework. This method acquires duck egg images through an image acquisition device

and augments the dataset using rotation, symmetry, and contrast enhancement methods.

Model testing was conducted on 2111 duck eggs, with a dataset of 6488 images of

candled duck eggs. The Mean Average Precision (MAP) of the method in this paper

was 99.74%, which was significantly better than the YOLOX-Tiny network before

improvement which has a MAP of 94.92% and a reported prediction accuracy of

92.06%. The system demonstrates viability and readiness for commercial deployment.

Maaño et al., (2023) deployed an intelligent monitoring and control system, to improve

the welfare and sustainability of commercial poultry farming, utilising modular

programming of programmable logic controllers (PLCs) to automate control actions

and incubator climate per real-time data collated from the various sensors deployed.

This project deploys Edge gateways to mediate between sensors and cloud data to allow

for seamless programs running. It allows for user monitoring of system operation,

identification of issues and control over system behaviour. The deployment of this

system is costly and as such it adopts integration with old systems to serve as

augmentation, which reduces its effectiveness.

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George and George (2023), designed and developed an IoT-based poultry egg incubator

temperature and humidity monitoring system. Achieves its objective of monitoring and

controlling the designed IoT incubator system, utilising an Arduino MKR 1000

microcontroller, which interfaces with a DHT 11 temperature and humidity sensor to

monitor and control the system's behaviour. The microcontroller is interfaced with

Arduino IoT cloud for managing and monitoring the real-time status of the system to

prevent hatching failure, the hatchability of the system was evaluated at a 95.24%

success rate with thirty-eight eggs hatched out of an initial 42 fertile eggs present. The

incubator is evaluated using the Kano model to determine its readiness as a marketable

product. The limitation of the system includes its design to accommodate only chicken

eggs.

Allen et al., (2023) developed an alternative powered poultry egg incubator system,

geared at addressing the issue of irregular power supply to an incubator system. This

system proposes the addition of solar power to the egg incubator design to mitigate the

negative effects power shortage has on the continuous incubation of poultry eggs. The

incubator was designed with an egg holding capacity of one hundred and fifty (150), to

ensure successful hatching the eggs were incubated at a temperature of 38 0C and

relative humidity of 60%. Solar power was successfully integrated into the incubator

system, via the addition of solar panels and an inverter battery to store charge. The

systems’ performance in terms of hatch rate was determined at 70% for the initial test

and 80% for subsequent testing.

González-Redondo et al., (2023) examined the effects long-term storage of game-

farmed quail eggs has on the egg incubation process. To evaluate the percentage of

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weight loss of hatched eggs, chick weight is compared to the weight of the incubated

egg before incubation. The result is given as a ratio, where the performance evaluation

employs the use of Pearson's X2 test. The eggs were stored at 15.80 C and 80% relative

humidity. It was observed that game quail eggs stored for more than 28 days showed a

significant decrease in hatch rate and fertility. It was also observed that chicks hatched

after 28 days suffered from significant weight loss. This paper only focuses on a sub-

section of quails.

Ahmad et al., (2023) developed functional egg incubator system, which utilises light

bulbs, temperature sensors, a ventilation fan, and a microcontroller to perform control

actions. This system was designed to be lightweight and affordable for small-scale

poultry farmers. This system does not account for air ventilation between the incubator

system and the environment, power outages and automated or remote monitoring of

incubation activities.

Andri et al., (2023) investigated the effects of egg storage on the performance of Arab

chickens for the first week post-hatch, by evaluating the performance of hatched chicks

based on the following parameters: initial body weight, final body weight, body weight

gain, feed intake, water intake, feed conversion rate and water conversion rate. Two

hundred (200) eggs were separated into five (5) batches with each batch subjected to a

different egg storage duration, with the eggs stored under stable conditions of 24-26 0C

and relative humidity of 60-70% and incubated at a set temperature of 37.7-37.9 0C and

50-60% relative humidity. The results of this experiment identified that eggs stored for

seven (7) days and longer exhibited a significant reduction in initial body weight, final

20
body weight, body weight gain and an increase in feed conversion rate. Whereas egg

storage for four (4) days and less had no adverse negative effect on chick performance.

Dhotre et al., (2023) developed, studied and evaluated the effects of short periods of

incubation during egg storage (SPIDES). Storage conditions for the observed incubated

eggs were given at 160 – 810 C with a relative humidity of 65-70%. SPIDES treatment

inculcates short periods of egg warming under incubation conditions to mimic the

incubating pattern and behaviour of a poultry bird. This experiment divided five

hundred (500) eggs incubated with this method into five (5) groups and five (5)

subgroups and subjected them to a pre-incubating temperature of 37.500 C and relative

humidity of 55-60%. Results achieved from this experiment indicate that long-term

stored eggs exposed to extended periods of SPIDES treatment demonstrated high

hatchability, with no decline in fertility and weight of the hatched chicks.

Kabaradin, A. (2023) constructed and evaluated the performance of an electric-kerosine,

homemade egg incubator developed using a frame made of MDF and plywood. The

incubator system makes use of components such as a TS-C700 temperature and

humidity controller, mesh wires fabricated egg trays, a hygrometer, a kerosine lamp, a

cooling fan and a water pan to design an incubator system with an egg holding capacity

of one hundred and fifty (150). Heat supply was provided by four (4) fluorescence bulbs

and alternatively by the kerosine lamp in the event of a power outage. It consists of

three (3) ventilation vents and an electrical fan for cooling and air ventilation. The

measured hatchability of the homemade egg incubator system was 77.60% at a

temperature of 37.5 0C and 60% RH for the first 18 days and 36.6 0C and 65% RH for

21
the remaining duration of incubation. The disadvantages of the system include its

design for manual egg tilting, sanitation, and inspection of embryo development.

Al-Ansi et al., (2023), developed an imaging system for egg incubator systems and

studied the advantages they provide. It stresses that image identification of incubating

eggs provides users with information that enables them to gear their efforts towards

progress. Thereby increasing the probability of egg hatching and reducing the risk of

contamination. It also aids in identifying the growth rate of eggs, which can serve as an

analysis of the incubation control methodology. The result of integrating image vision

into an egg incubator system, shows an increase in the hatch rate and yield of healthy

eggs.

Adriaensen et al., (2023) conducted and experiment, which lasted for the duration of

seven (7) days. To study the difference in embryo development between artificially

incubated eggs and naturally incubated eggs. Embryo development was monitored

through candling operations, and it was discovered that there was no significant

difference in development between the two methods of incubation as they were

subjected to the same physical and environmental parameters.

Additionally, Maulana et al., (2023) presented this study, intended at exploring the

application and viability of fuzzy logic control on the incumbent temperature and

humidity of an incubator system. Fuzzy logic entails the deployment and determination

of membership between input constants. The Sugeno method, which outputs the result

of fuzzy logic as constants or linear equations is considered for evaluation. Fuzzy logic

is applied to an incubation system designed with a DHT22 sensor, Microcontroller,

22
ventilation fan and tilt motors for angular actuation. It was derived that the robustness

of the fuzzy logic controller system was acceptable and that the Sugeno method has a

faster temperature control time however humidity control poses a challenge during egg

incubation.

Tona et al., (2022) investigated the effect of varying incubation conditions on chick

quality and post-hatch performance, taking into consideration incubation parameters

such as relative humidity, temperature, turning requirements, ventilation, Ovo feeding

and delays in feed access. The effects of incubation parameters on chick development

are observed through a series of trial incubations, where each batch is exposed to a

varying degree of incubation conditions. It was discovered that a moderate negative

correlation exists between hatchability, egg weight loss and temperature, while a

positive correlation exists between hatchability and relative humidity. The use of

moderate to high ventilation is beneficial to chicken embryos during incubation as it

supports enhanced oxygen-carrying capacity of chicks. Whereas excessive ventilation

exposure to incubating eggs undergrowth rate in the initial phase of incubation.

Furthermore, Tolentino et al., (2022) developed an automated egg incubator with a

candling subsystem for egg maturity detection, for commercial duck eggs. An

automated incubator system consisting of four chambers, a heater, a LED bulb, a

camera module, a system fan and DHT 11 temperature and humidity sensors are

interfaced with a Raspberry Pi 4 microcontroller to the behaviour and parameters of the

incubator. The system with an egg capacity of 80 eggs performs incubation activities

along with candling at three intervals. The image data captured is analysed by a region-

based convolution neural network to determine egg maturity and differentiate between

23
the three different classifications of duck eggs. The measured candling identification

rate of the system is estimated at 80.5%. The system performs region-based

convolutions with a minimal dataset and cameras of low resolution.

Suhirman et al., (2022) developed an alternative method for image processing and

analysis over the embryonic development of incubating eggs. The proposed Otsu

Method involves dataset analysis, image grey scaling, image pre-processing and

adjustment, image enhancement through Histogram Equalization and Contrast Limited

Adaptive HE and image segmentation. This method aims to derive the maximum

possible threshold value of the captured image data. Compared with existing image data

analysis methods, the Otsu method demonstrates high Structure Similarity Index

Method and Accuracy (SSIM) evaluation distribution value, faster processing time and

increased accuracy.

Adriaensen et al., (2022) investigated the relationship between egg storage before

incubation and its effects on embryonic development. This study utilised imaging

technologies such as computed tomography and magnetic resonance imaging (MRI)

while taking classical measurement factors, such as egg weight, egg white Ph, eggshell

strength, Haugh units, yolk index and colour. There were three (3) subdivisions,

according to egg storage duration and a total of thirty (30) eggs were selected at random

for analysis. The eggs were incubated at a temperature of 37.8 0C and 55% RH. The

results of non-invasive imaging reveal that the eggs stored for ten days possessed a

higher mortality rate, from which the conclusion that prolonged egg storage is a crucial

parameter to synchronising embryonic stages, thereby reducing the hatching window

and it severely impairing embryonic development of incubating eggs.

24
Ostrenko and Galochkina (2021), evaluated the effects of water-soluble antioxidants on

egg incubation and offspring hatching. An experiment was performed on two groups of

egg-laying hens, selected at random consisting of a hundred (100) hens each. The

experiment lasted for forty-one (41) days with the experimental group (Group A) being

placed on a diet enriched with dihydroethoxychine (DGE), while the control group

(Group B) was kept on a basic diet. The effect of the water-soluble diet on the

experimental group is demonstrated in the high egg production exhibited by hens. It

was also noted that the hatchability of incubated eggs from the experimental group was

significantly increased when compared to chicks hatched from the control group.

Concluding that the inclusion of water-soluble antioxidants yields positive effects on

the productive and biological factors, influencing egg production and embryonic

growth.

Adegboyega et al., (2021), developed a cost-friendly poultry incubator system with a

system capacity of 30 eggs. The proposed system was constructed and interfaced with

a servo motor, a 60-watt bulb, DHT11 sensor, LCD, relay, RTC module, a 12-volt DC

fan for ventilation, 300 watts inverter and a water bowl for regulating the relative

humidity within the incubator. Control action and sensor integration were performed

on an AT Mega 328 microcontroller. The working efficiency of the incubator was

estimated to be 63 %. There was a malfunction with the temperature sensor which went

unnoticed. It eventually led to an interrupted incubation process and mortality of

incubating eggs.

Fredrick et al., (2021) designed and implemented a remotely monitored egg incubator

system. This system utilises a DHT 22 sensor for temperature and humidity reading, a

25
DC motor for egg tilting at an angle of 450, an LCD module for incubator system

monitoring and a GSM module for remote monitoring. The purpose of this is to enable

system flexibility, enhance productivity and provide a cheaper alternative to the egg

incubator system available commercially. The system provides accommodation for

solar power. However, the system suffers from operation limitations such as poor

connectivity between the GSM module and the incubator system, inability to detect egg

hatching and low battery capacity.

Additionally, Yadav et al., (2021) designed, constructed, and evaluated the

performance of an automatic egg incubator system. The device maintains an operating

temperature range of 37-38 0C and an operating humidity of 60-90%, eggs are placed

horizontally and tilted five (5) times a day. Control functions are designated by an

Arduino Uno microcontroller, egg heating by a filament bulb and heating rod, DHT22

sensor to measure incubator temperature and humidity, RTC DS1307 sensor for egg

incubation clock, LCD for parameter readings display, centrifugal fan for airflow within

the incubator. The employ of polyethene foam for incubator system insulation. After

initial testing of the egg incubator system, the hatchability of the system is given as

73%, where a total of twenty-six (26) eggs were hatched out of a total of thirty-six (36).

Mariani et al., (2021) reviewed and necessitated the need for modern design

modification of poultry egg incubators to achieve the aim of a cost-effective and power-

efficient system for poultry farmers. To achieve the objectives of this study,

microcontrollers are employed to provide automated control over temperature and

humidity conditions, tray switching and tilting. This system is developed for the

incubation of both duck and chicken eggs. With an average hatch rate of 86.5% and egg

26
holding capacity of one hundred and twenty (120) eggs, this incubator system provides

a feasible alternative to egg incubator systems designed for easy use, maintenance, and

purchase.

Soeb et al., (2021) developed and evaluated the hatch performance of a low-cost egg

incubator system, which consists of a microcontroller and an egg turner tray with an

egg holding capacity of one hundred and sixteen (116) eggs. To assess the performance

of the system, manual and automatic testing were conducted. The results indicate that

automatic turning, with a hatchability of 87.1% outweighs the performance of manual

turning with a hatch rate of 79.3%.

Furthermore, Mujčić and Drakulić (2021) developed and presented an automatic egg

incubator, which integrates Fuzzy logic control for monitoring and control of

incubation microclimate parameters. An automatic egg incubator with IoT

communication is designed with AT Mega 328 and ESP 8266 microcontrollers, DHT

22 temperature and humidity sensor, thermistors, heaters, fans, LCD and LED for

display and user notification. To implement fuzzy logic, input parameters such as error

rate change and error between the desired value and actual temperature and humidity

values are fed to the system. Fuzzification is performed on these parameters with the

aid of trapezoidal and triangular functions.

Nawaz et al., (2021),developed of a smart egg incubator system which inculcates IoT

technology. This system is designed with two incubating layers which incorporate still

and forced air incubation. Monitoring and control measures are taken by ESP 8266

NodeMCU. Control actions involving egg tilting, water level detection, temperature,

27
and humidity regulation with the aid of DS18B20 and DHT11, are performed by the

PIC 16F887 microcontroller. These study experiments with various temperature

conditions to determine the most suitable for feasible egg incubation. Out of three

hundred (300) eggs tested a hundred (100) per batch, the most suitable temperature for

egg incubation was given as 37.5 0C with a hatch rate of 92%.

Oussama et al., (2021) developed an intelligent egg incubation system to accommodate

and regulate heat and humidity levels, automatic egg turning, automatic water filling

and dispensing system in this study. This project implements Proportional Integral

Differential (PID) and Bang-bang control schemes, utilising several AT Mega

microcontrollers for control implementation and Liquid Crystal Display screens for

data relay to the system operator. The systems performance is evaluated in respect to

its hatchability rate which is given as greater than 80%.

In addition, Dutta and Anjum (2021), designed a Mamdani Fuzzy Logic Control

scheme for the optimization of Temperature and Humidity control in an automatic egg

incubator. Mamdani Fuzzy Inference system relays member relationships as fuzzy logic

and is notably useful in concluding a process. Input parameters such as temperature and

relative humidity are captured by the DHT22 sensor. Defining the shape of the

membership for the parameters captured, they are represented as Gaussian and

Triangular functions. The outputs of egg incubation are received as functions of fan

speed and heat.

Metwally (2020) developed this system, aimed at improving the performance of an egg

incubation by application of Pulse Repetition Frequency (PRF). To achieve this, an

28
electronic circuit designed to produce a pulse repetition frequency signal is integrated

into an incubator system and its effect is evaluated. Pulse repetition frequency can be

explained as a power management technique which cycles between the on-off phase of

a signal. Its advantages over analogue potentiometer control methods include the

minimal power loss generated in the system and precise control over voltage supply to

electronic and electrical components. A series of pulse intensities and frequency

durations were considered for improving the performance of egg incubation. The results

obtained show that the pulse intensity and frequency duration band most effective for

high hatchability are: 72 W.m2 and 25 min/hr. With a feasibility rate of 89%, and

incubator efficiency of 83%.

Sunday et al., (2020) developed and evaluated the performance of a solar-powered

poultry incubator with a capacity of 100 eggs. For the development of the incubator

system, a programmable logic controller was applied. Interfaced with the controller

were LCD, and LM358 temperature sensor, the evaluated hatchability of the eggs was

91.08% when operated within the specified range.

Koodtalang et al., (2020), developed a non-destructive poultry egg fertility detection

system, for incubator system deployment. This is achieved by passing image data

captured during egg candling performed by a LED light source and a digital camera, to

a trained convoluted neural network (CNN) designed with Python programming

language, supported by Open-CV and Keras. This study boasts an experimental

research result of 100%, in the ability of the CNN system to distinguish and locate the

placement of fertile eggs placed in the system from a batch study of 240 eggs. The

29
shortcoming of this paper lies in the inability of the system to identify the fertility of

incubated poultry eggs for the first six (6) days of incubation.

Additionally, Tsai et al., (2020) developed a system capable of classifying poultry eggs

according to incubation viability and analysing the absorption rate of fertile eggs, for

vaccine creation. This was achieved by employing fifty (50) egg candling LEDs to

provide illumination while digital images were captured by a colour camera. It

employed the use of receiver operating characteristics (ROC) to analyse image data and

obtain the image area under the curve to determine the screening.

2.5 Chapter Summary

This chapter investigated research papers and relevant bodies of work, with the sole

purpose of informing methodology choice, component selection and identifying key

concepts to be taken into consideration, for the monitoring and development of poultry

egg embryo. This was achieved by observing the different methods of approach,

employed for the purpose of artificial egg incubation, the outcome and the challenges

faced. With the aim of modelling of an intelligent incubator system, with control logic

capable of monitoring and manipulating system parameters based on established

relationships between input and factors governing egg incubation.

30
 CHAPTER THREE

3.0 METHODOLOGY

3.1 Chapter Introduction

This chapter aims to explore the design considerations, components and parameters

considered for the construction of a functional embedded control system for a poultry

bird incubator system, presents system layout and propose control logic and operations

to be conducted by the incubator system to achieve the goal of feasible and efficient

egg hatching.

3.2 System Overview

An intelligent control incubation system integrates hardware and software components

for monitoring and controlling poultry incubation operations. The components are

expected to retrieve data, automate work processes, above all be upgradeable to

accommodate evolving technologies that make monitoring and control simpler and

more streamlined. Sensors are required for monitoring temperature level, humidity, and

other production parameters for the incubator.

Microcontrollers automate complex tasks that affect the result of the poultry egg

incubation system through modular programming and enable real-time communication

and control for remote usage. Control hardware activates ventilation fans, heaters, and

motors in response to variable conditions. To achieve the aim of developing a functional

poultry bird incubator system, a schematic block diagram was designed, which

identifies the various components and sub systems required for hardware development.

31
Figure 3.1. Block Diagram of Incubator Structure

32
 Figure 3.2. Flowchart for the Operation of Intelligent Egg Incubator System.

The flowchart illustrated details of the operation schematic which starts with the system

and components activation. Sensor readings are derived and used to navigate the next

course of action, for the incubator system. The system evaluates the current incubator

temperature and humidity conditions according to the implemented Fuzzy Logic

control scheme, which actuates environment controlling actuators as required by the

33
system. In the event of failure during the incubators’ runtime, an emergency signal is

sent to the system operator.

34
3.3 Design of Control System

To design the circuitry and simulate control circuit operation, software design

technology such as Proteus and MATLAB were employed. The incubator is designed

to have a holding capacity of ninety (90) eggs, which are subdivided into three crates

with one crate per section. The incubator is designed to possess dual power units to

accommodate for the variability of power supply to the incubator from the power main

by utilising a rechargeable DC inverter battery, the system is designed for continuous

usage.

Fuzzy Logic Control entails integrating human reasoning and logic in the

implementation of control algorithms and functions. It performs this strenuous task with

the aid of fuzzy membership, which can be described as graphical representation of the

degree of correlation between members of a systems crisp input and fuzzy rules which

govern the system operation and output mapping of fuzzy output. The range of input

and out values, as well as the membership between the inputs and outputs of a system

are designed graphically and analysed through the aid of the MATLAB software.

Functional fuzzy logic implemented by the Arduino Uno microcontroller which

interfaces with the components in the incubator system to perform tasks efficiently.

35
 Figure 3.3 Fuzzy Inference Block Diagram

The Mamdani fuzzy inference block diagram maps the relationship between poultry

incubator inputs, given as temperature and humidity to actuator outputs, represents

them as block components and details the membership function of the range of values

allocated to each block system.

36
 Figure 3.4. Fuzzy Logic Rule Base System Governing Incubator Operation.

The rule base serves as the governing control logic that dictates system behaviour for

the duration of egg incubation. It accomplishes this task by generating a list of possible

events that can occur during egg incubation and allows for an appropriate user guided

outcome to map out output response.

Figure 3.5. Flowchart for egg tilting

37
To implement efficient and timed control over egg tilting operations, a functional

flowchart detailing the operation events was designed. Egg tilting is performed once

every six (6) hours, lasts for five (5) seconds. Its operation is dictated by the control

sequence from the microcontroller which keeps a record of the operating time of the

incubator. By utilising Bang-bang control logic, which is a feedback logic

controller that switches abruptly between two states of a system, the egg crate is made

to tilt the incubating eggs to ensure proper embryo mixture.

Egg pre-incubation operation is performed before egg incubation, this process

replicates egg storage conditions of naturally incubated eggs while preserving the

fertility and ensuring the viability of egg hatching. This delicate process is conducted

by regulating the temperature and humidity of the incubator at time intervals to mitigate

early embryo death. The incubator system is set to provide a regulated climate condition

of 37.5 0C and 65% relative humidity, the duration of pre-incubation varies according

to the duration of proposed egg storage.

3.4 Embedded Control System Development

For the development and construction of the embedded control system for a poultry

bird incubator, consisting of several actuating mechanism and sensors. Identifying and

summarily detailing the purpose of inclusion of each component utilised in the

development of the artificial egg incubator as shown below.

Table 3.1. Arduino Uno Pinout Table

COMPONENTS PIN OUTS SPECIFICATION

DHT 11 SENSOR D8 To collate humidity and temperature

38
 values of the incubator system

LCD SDA, SCL For display of the incubator’s

current, environment parameters.

DC FAN D11 To circulate heat in the incubator and

regulate humidity

DC TILT MOTOR D7 To provide tilt motion, for the egg

crate.

AC BULB A2 Serves as heat source, for egg

incubation.

DC BULBS A3 Serves to provide heat in the

incubator.

Derived from the circuit connections of the intelligent control system, clearly defining

the position of each component, in accordance to the circuit diagram connected to the

Arduino Uno microcontroller, which houses majority of the components utilised in

the systemin the incubator.

Table 3.2 ESP Pinout Table

SYSTEM PIN OUT SPECIFICATION

ELEMENT

DHT 11 SENSOR GPIO 5 To collate humidity and temperature

values of the incubator system, for

data transmission.

39
Derived from the circuit connections of the intelligent control system and it illustrates

how the intelligent system is able to collect and transmit sensor readings to the targeted,

IoT cloud platform utilising an ESP microcontroller.

a) DHT Temperature and Humidity Sensor

Required to capture relevant incubator system parameters for monitoring and control

process. Temperature and humidity sensors are utilised. Mum

Figure 3.6 DHT 11 Temperature and Humidity sensor (Raju et al., 2024)

b) DC Fan

This actuator is required for humidity and air flow regulation in the incubator system.

Its activity in the incubator system is controlled by a microcontroller.

Figure 3.7 System Fan (Ahmad et al., 2023)

40
 c) DC Bulbs

This component is required for the supply of heat for the duration of egg incubation.

Controlled by the microcontroller its activity is regulated, monitored and controlled

Figure 3.8 Heating Element (Ahmad et al., 2023)

d) Arduino Uno

Responsible for monitoring and controlling the process of the incubator system. The

microcontroller is interfaced with all components responsible for proper system

function.

41
 Figure 3.9. Arduino Uno (Raju et al., 2024)

e) ESP 32 Microcontroller

The ESP32 microcontroller receives temperature and humidity readings and

communicates between the incubator system and a remote IoT platform.

Figure 3.10. ESP 32 microcontroller (Cayo et al., 2024)

f) DC Stepper Motor

Egg tilting is heavily dependent on the functionality and operation of the stepper motor,

which is required as the driving force to ensure proper egg mixing during incubation.

Figure 3.11 Stepper Motors (Tilt motor) (Allen et al., 2023)

42
 g) Liquid Crystal Display Module

This is a communication module that presents system parameters in alphanumeric

values for ease of access and easy understanding of sensor values.

Figure 3.12. Liquid Crystal Display (Raju et al., 2024)

Designed to provide reference and guide during component installation. The circuit

diagram designed with the aid of Proteus circuit simulation software, displays the

component wiring required for powering and integrating hardware components for the

intelligent artificial poultry bird egg incubator, as shown below.

43
 Figure 3.13 Circuit Diagram

3.5 Performance Evaluation

System performance is evaluated by calculating the percentage accuracy of the fuzzy

system and the system’s response time to change input parameters. This is

accomplished by comparing the accuracy of measured system output for given variables

of input parameters, to the estimated fuzzy logic system output for the given input

parameters and dividing the summation of accurate system and estimated response to

the total number of recorded test samples. System response time is averaged from the

summation of measured response time for change in input reading.

∑𝑇
System Response Time = (3.1)
𝑛

44
Where ∑T, is given as the summation of measured response time for change in input

readings.

Where n, is given as the total number of test samples recorded within a given time

period.

∑𝐶
Accuracy of Fuzzy Logic Control Scheme (%) = 100 × (3.2)
𝑛

Where ∑C, is given as the summation of accurate test results recorded between input

behaviour and estimated fuzzy logic response.

Where n, is given as the total number of test samples recorded within a given time

period.

3.6 Chapter Summary

Through the implementation of hardware and software components, as listed in this

chapter. The aim of developing a controlled environment for the artificial incubation of

poultry eggs is achieved, and the system's performance is evaluated, for further research

into parameter and system adjustment to achieve better results and ensure the

practicality and safety of the proposed embedded control system.

45
 CHAPTER FOUR

4.0 RESULTS AND DISCUSSION

4.1 Chapter Introduction

This chapter provides insight into the results derived from the system’s operation,

evaluation of system design and performance and its conformity to design and

performance expectations. By providing detailed procedures and tasks undertaken

during the design, development and testing of the fuzzy-controlled system.

4.2 Design of Embedded Poultry Bird Egg Incubation System

To achieve a suitable embedded control system, capable of performing the desired

control and monitoring actions, a microcontroller circuit was designed and interfaced

with the DHT humidity and temperature sensor. The system was able to acquire the

incubator’s ambient temperature and humidity in real-time, the collation of data

occurred within three (3) seconds and was displayed on the mounted liquid crystal

display unit.

Governed by a Fuzzy Logic Rule Base and implemented as system code to detect and

report system activity, the intelligent poultry system is designed to have an appropriate

response to environmental conditions, relevant to the development of egg embryos.

Designed by utilising the MATLAB software, system response to a given permutation

of input parameters, governed by the fuzzy rule base is established and presented in

figure 4.4 and 4.5

46
The graph represents the simulated behaviour of the incubator heating bulbs over a

given range of incubator temperature and humidity for the duration of egg incubation.

The graph shows the corresponding response of the incubator heater to the increase or

decrease in incubator inputs. The peaks of the plotted control surface graph highlighted

in yellow, represent the regions of high activity for the incubator heater and regions of

inactivity highlighted as blue.

Figure 4.1. Control Surface for Heater

It can be deduced from the graph the incubator heater will remain active for a majority

of the egg incubation process and inactive during periods of high incubator temperature.

The system graph illustrated below, represents the simulated air gate actuator behaviour

across given instances of temperature and humidity readings in the incubator. The graph

shows the corresponding response of the incubator fans to the increase or decrease in

incubator inputs. The peaks of the plotted control surface graph highlighted in yellow,

47
represent the regions of high activity for the incubator fan and regions of inactivity

highlighted as blue.

Figure 4.2. Control Surface for Air Gate

It can be deduced from the graph that the incubator fan will remain dormant for the

duration of egg incubation, with the exception of prolonged increase in temperature or

decrease in humidity and temperature.

System output which is derived as an aggregate, which represents the outputs of each

rule set that governs the fuzzy system, for a set of given input parameters. The rule

inference viewer allows for evaluation and training of the fuzzy logic rule base and

membership mapping to ensure that the desired outcome is achieved.

48
 Figure 4.3. Rule Inference for Intelligent Poultry System

The rule inference viewer allows for the modification of system behaviour which is reflected

in the control surface graphs. By training the fuzzy logic with the rule inference viewer,

system accuracy and performance can be measured and evaluated.

4.3 System Development and Implementation

By integrating various components and encasing the embedded system in a plastic frame,

the incubator system is connected to the system actuators and sensors. The incubator system

is powered by a 12-volt rechargeable DC inverter battery, which connects directly to the DC

fan, DC bulbs and Tilt motor with the aid of a relay block. Stepping down 12-volts from the

DC battery to 5-volts the bulk converter provides power for the microcontroller units. The

LCD and DHT sensors are mounted on the Arduino Uno microcontroller and are connected

49
to the 5-volt and 3.3-volt power supply respectively. The system reads the temperature and

humidity of the incubator and displays it accordingly on the LCD.

The Fuzzy Inference System governs the operation of the system, which regulates the

activity of the heater bulbs and DC Fan, with the DC fan set high from start-up. Connected

to the internet via Wi-Fi or mobile hotspot, the ESP microcontroller receives DHT 11 sensor

readings from the GPIO 5 pin of the ESP 32 microcontroller. It transmits the data through

WriteApiKey to the ThingSpeak channels. The assembled, intelligent poultry egg incubator

is shown below.

Figure 4.4. Intelligent Artificial Poultry Egg Incubator System

50
Figure 4.4. Intelligent Artificial Poultry Egg Incubator System

51
4.3 System Performance Evaluation

Acquired during the live testing of the incubator system, this graph shows the captured

incubator temperature from start-up, with a data capture interval of fifteen (15) minutes.

Figure 4.5 DHT Temperature Graph

The graph reveals the steady increase of the incumbent temperature of the incubator,

which certifies the ability of the control system to generate and regulate heat within the

incubator.

System humidity data captured at an interval of fifteen (15) minutes, is plotted against

time for the evaluation of the artificial incubator system’s condition and regulation of

humidity for the development of zygotes.

52
 Figure 4.6. DHT Humidity Graph

The incubator’s relative humidity rises and decreases with respect to the controlled

system temperature. It can be deduced from the graph that the incubator system

demonstrates the ability to regulate humidity levels within the incubator.

To evaluate the operation and functionality of the Fuzzy Logic Controller system, the

temperature and humidity readings are measured from start-up and the ability of the

system to generate, regulate and respond to the environment conditions required for

proper egg incubation are observed and recorded.

Table 4.1 System Time Response

DHT Temperature DHT Humidity Readings System Response Time (s)

Readings (0 C) (RH)

33.20 62.8 1.6

33.20 62.9 1.4

33.30 62.9 1.7

53
 33.40 63.0 2.3

33.47 63.0 1.8

33.50 63.1 2.2

33.50 62.3 4.2

∑𝑇
System Response Time =
𝑛

15.2
System Response Time =
7

System Response Time = 2.2 seconds

Average incubator system response time, evaluated at 2.2 seconds

Table 4.2. Fuzzy Logic Accuracy Test

DHT Sensor DHT Sensor System Response Estimated Fuzzy Evaluation

Temperature Humidity System Response

Readings (0C) Readings (RH)

33.20 62.8 Heater On, Fan Heater On, Fan On Correct

On

33.20 62.8 Heater On, Fan Heater On, Fan On Correct

On

33.20 62.9 Heater On, Fan Heater On, Fan On Correct

On

33.30 62.9 Heater On, Fan Heater On, Fan On Correct

On

33.40 63.0 Heater On, Fan Heater On, Fan On Incorrect

54
 Off

33.40 63.0 Heater On, Fan Heater On, Fan On Correct

Off

33.47 63.0 Heater On, Fan Heater On, Fan Incorrect

On Off

33.50 63.1 Heater On, Fan Heater On, Fan On Incorrect

Off

33.50 62.3 Heater On, Fan Heater On, Fan On Correct

On

33.50 62.3 Heater On, Fan Heater On, Fan On Correct

On

∑𝐶
Accuracy of Fuzzy Logic Control Scheme (%) = 100 ×
𝑛

7
Accuracy of Fuzzy Logic System = 100 × 10

Accuracy of Fuzzy Logic System = 100 × 0.7 = 70%

Fuzzy system accuracy evaluated at 70%.

4.4 Chapter Summary

This chapter presents the results derived from the implemented intelligent artificial

incubation system. It highlights the design, build, implementation and evaluation

metrics of the intelligent artificial poultry bird incubator, as detailed in the methodology.

This study showcases the ability of the system to meet the required design and

performance objectives, which are: to collate and display DHT sensor data, adapt Fuzzy

Inference Logic for the regulation of heat and humidity in the incubator, actuate tilting

55
mechanism for the purpose of egg embryo development and remote communication of

sensor data to an IoT cloud platform.

56
 CHAPTER FIVE

5.0 CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion

The developed intelligent artificial poultry bird incubation system is capable of

monitoring and regulating the temperature and humidity conditions of the incubator,

conduct egg tilting for efficient egg incubation, with a provision made for remote

monitoring of incubator parameters, utilising IoT communication.

This study aimed at improving the quality and quantity of hatching among poultry bird

eggs for small and medium scale poultry farming, feasibly performed this by providing

a feedback control system, which utilises artificial intelligence for the monitoring and

regulation of incubator conditions in real time, with an understanding of the required

environmental factors which influence poultry egg development. Thus, achieving the

aim of developing a marketable intelligent egg incubator prototype with the capacity

for commercial or domestic poultry farming, with the inclusion of remote monitoring

functionality and egg pre-incubation storage capabilities. This project delivers a low-

cost alternative which aids the ease of poultry farming and serves as a case study for

inculcating control techniques into poultry farming.

5.2 Recommendations

The following recommendations are suggested for future works, based on the findings

and limitations of this study.

57
 i. For the development of a robust system for artificial poultry egg

incubation, the inclusion of an ammonia gas sensor and candling

subsystem for the detection of dead-in-shell eggs and egg candling is

recommended.

ii. Integration of an automated humidifier system to properly regulate

humidity circulation in the incubator.

By addressing this study’s recommendation, exploring the use of different components

and control systems. The feasibility of developing an intelligent artificial poultry egg

incubator, capable of optimal operation and response is achievable.

58
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