# Optimization of Temperature and Relative Humidity in an Automatic Egg
Incubator Using Mamdani Fuzzy Inference System

Pramit Dutta1 and Nafisa Anjum2 Department of Electronics and
Telecommunication Engineering
Chittagong University of Engineering and Technology
Chittagong, Bangladesh
<EMAIL_ADDRESS>and<EMAIL_ADDRESS>
###### Abstract

Temperature and humidity are two of the rudimentary factors that must be
controlled during egg incubation. Improper temperature and humidity levels
during the incubation period often result in unwanted conditions. This paper
proposes the design of an efficient Mamdani fuzzy inference system instead of
the widely used Takagi-Sugeno system in this field for controlling the
temperature and humidity levels of an egg incubator. Though the optimum
incubation temperature and humidity levels used here are that of chicken egg,
the proposed methodology is applicable to other avian species as well. The
input functions have been used here as per estimated values for safe hatching
using Mamdani whereas defuzzification method, Center of Area (COA), has been
applied for output. From the model output, a stabilized heat from temperature
level and fan speed to control the humidity level of an egg incubator can be
obtained. This maximizes the hatching rate of healthy chicks under any
conditions in the field.

###### Index Terms:

artificial intelligence, control system, fuzzy logic, humidity, incubator,
Mamdani, Takagi-Sugeno, temperature, sensor.

##  I Introduction

The importance of egg incubators in achieving the ultimate goal of poultry
industry, that is, to increase the hatching rate and produce healthy hatched
birds is undeniable. If temperature is too high or too low during incubation,
problems like dead embryos at early stage, underdeveloped or crippled chicks,
early hatching and so on may arise. On the other hand, if humidity level is
not maintained properly, embryos adhering to shells, dead embryos before
pipping, early broiler mortality and many other difficulties arise.

Over the past decade, rapid growth has been noticed in consumer demand in the
developing countries for livestock products. Such rise of demand is being met
by corresponding rise in poultry meat and egg production. Comparative case
studies have shown that in countries like India, China, Thailand, Brazil and
Egypt, the average per capita income plays a vital role in determining the
rate of consumption of meat. World egg production has increased by more than
150 percent in the last three decades. This growth has been more noticeable in
Asia where production has increased almost fourfold. World poultry meat
production shot up from 9 to 122 million tonnes between 1961 and 2017 whereas
egg production soared from 15 to 87 million tonnes [1]. By 2025, the global
poultry production is expected to experience a rise of 24% reaching 1,44,682
thousand tonnes approximately [2].

In this context, the obvious burning question that needs to be addressed is
how effective are the current physical conditions of incubation? Are they
effective enough to promote greater hatchability and better quality chicks?
Are they effective enough such that under adverse conditions, they can
demonstrate better performance in the field and ensure higher survival rate?
As a new and future prospect for incubation, an efficient system has been
proposed in this paper that stabilises the output of heater and controls the
fan speed of an automatic egg incubator. In most of the cases of incubation,
temperature is controlled manually whereas humidity level is managed by adding
water to the built-in water trays at the bottom of an incubator. The system
here takes inputs from a sensor (DHT22) and artificial intelligence based
Mamdani fuzzy inference system designed here compares it with the required
optimum values. The output heat is adjusted according to the input temperature
and humidifier fan speed is maintained throughout the process for correct
humidity. As such embryos do not lose excess water and mortality rate is
considerably reduced.

##  II RELATED WORK

Many researchers have presented their work on incubators trying to develop
more efficient models throughout time. One of them includes the optimization
of temperature in incubator using fuzzy inference and IoT [3]. Another paper
discusses the design of incubator using STEM approach [4].In addition, a
micro-controller based electrical incubator system has been designed [5]. A
temperature and humidity controller has been implemented through FPGA [6]. A
quail egg incubator has been designed using Arduino micro-controller and IoT
[7]. Fuzzy logic control has been used in designing a private home heating
system [8].In this paper, comparative study has been conducted on the
management of temperature inside egg hatching incubator from implementation of
on/off and fuzzy logic controller using a low cost micro-controller board
interfaced with the integrated circuit based temperature sensor and LabVIEW
2015 software [9]. Most of the works in this field has been done using the
Takagi-Sugeno fuzzy inference system but the method proposed here uses Mamdani
in which the output is not maintained constant or linear. This is much more
efficient as it takes linguistic inputs of temperature and humidity which do
not vary linearly in nature.

##  III METHODOLOGY

Figure 1: Design Procedure

###  III-A Input

The inputs are taken by the sensor DHT22. This sensor has temperature
measuring range from -40 to +125 degrees Celsius with $\pm 0.5$ degree
accuracy. Moreover, the sensor has much efficient humidity measuring range
from 0 to 100% with 2-5% accuracy. The operating voltage of DHT22 ranges from
3 to 5 volts while the maximum current used when measuring is 25mA.

Figure 2: DHT22 Sensor

####  III-A1 Temperature

In a chicken egg incubator it is mandatory to maintain the temperature
throughout the entire incubation process. The optimal temperature is
38°C(100.5°F)but should be kept in a range between 37-39°C.[3]

####  III-A2 Relative Humidity

Usually for chicken egg, the incubation process is completed in 21 days. The
relative humidity should be 50-55% in first 17 days and it should be 60-65%
for day 18-21.

###  III-B Fuzzification

In the fuzzification process all the parameters have been defined as variables
according to their value. For the parameters defined, the shape of the
membership functions are Triangular and Gaussian function respectively. As per
requirement Triangular functions for temperature, humidity and fan speed have
been used. Triangular function can be represented as:

$\mu\textsubscript{A}(x)=\begin{cases}0,\hskip 19.91684ptx\leq 0\\\
\frac{x-a}{m-a},\hskip 5.69046pta<x\leq m\\\ \frac{b-a}{b-m},\hskip
5.69046ptm<x\leq b\\\ 0,\hskip 19.91684ptx\geq b\end{cases}$

Where,
a=lower limit;
b=upper limit;
m=a value
The heat change in an incubator should be smooth for stable hatching. So, for
heat, Gaussian function has been used. A Gaussian function with central value
m and standard deviation k>0 can be represented as:

$\mu\textsubscript{A}(X)=e^{-\frac{(x-m)^{2}}{2k^{2}}}$

For temperature there are three membership functions defined as follows:

Figure 3: Membership Function Plot of Temperature

But in case of another input parameter, that is, relative humidity, the
condition for membership function has to be defined twice. The first condition
is set for first 17 days of incubation process then as per the changes in
humidity requirement for day 18-21, changes have been made in the functions as
follows:

Figure 4: Membership Function Plot of Humidity for first 17 days

For proper incubation the relative humidity should be increased after 17 days
by 10-15 % which can be defined as:

Figure 5: Membership Function Plot of Humidity for day 18-21

After that, the outputs of incubation process have been taken as heat and fan
speed. The membership function of the estimated heat is defined in a range
between 1-10. The heat change in an incubator should be smooth for stable
hatching. For that reason the Gaussian function has been used as membership
function. The design for this is given below:

Figure 6: Membership Function Plot of Heat

The output of fan speed has also been categorized into three membership
functions defined in a range of 1-10. The figure can be observed below:

Figure 7: Membership Function Plot of Fan Speed

###  III-C Fuzzy Inference system

There are two methods of FIS. They are:

1.Mamdani Fuzzy Inference System;

2.Takagi-Sugeno Fuzzy Model (TS Method);

In this application, the membership function and relation between them is not
linear or constant to gain an optimal temperature and humidity. By considering
this characteristic, Mamdani Fuzzy Inference System has been applied.

###  III-D Fuzzy Rules Base

The rules for the optimization of temperature and humidity in the incubation
process have been set for maximum possible conditions that may prevail in the
process:

TABLE I: Rules for fuzzy model Sl No. | Temperature |  Relative Humidity | Heat | Fan Speed
---|---|---|---|---
01. | Cold | None | High | Medium
02. | Optimal | None | Optimal | Medium
03. | Hot | None | Low | Medium
04. | None | Dry | Optimal | High
05. | None | Optimal | Optimal | Medium
06. | None | Wet | Optimal | Low
07. | Cold | Dry | High | High
08. | Cold | Optimal | High | Medium
09. | Cold | Wet | High | Low
10. | Optimal | Dry | Optimal | High
11. | Optimal | Optimal | Optimal | Medium
12. | Optimal | Wet | Optimal | Low
13. | Hot | Dry | Low | High
14. | Hot | Optimal | Low | Medium
15. | Hot | Wet | Low | Low

###  III-E Output

Here, for defuzzification process, Centroid of Area (COA) Method has been used
which can be represented as:

x*=$\frac{\sum_{i=1}^{k}A\textsubscript{i}\times\bar{x}\textsubscript{i}}{\sum_{i=1}^{k}A\textsubscript{i}}$
where,
Ai=firing area of ith rules;
k=total number of rules fired;
$\bar{x}\textsubscript{i}$=center of area

####  III-E1 Heat

Relating to the temperature and relative humidity, the heat has been measured
here considering estimated value but the actual heat is going to vary
depending on the number of eggs and structure of the incubator.

Figure 8: Block diagram for heat control

####  III-E2 Fan Speed

The humidifer fan evaporates the water and brings it into the air. this
humidifier fan is used with the water channel. When the relative humidity is
under the optimal level, the fan speed will be high and vice-versa.

Figure 9: Block diagram for fan-speed control

###  III-F Simulation Model

The simulink model of this control system can be represented as:

Figure 10: Simulation model for the inference system

###  III-G System Diagram

In automatic egg incubators, the temperature and humidity within the
incubation chamber is measured by the DHT22 sensor. The fuzzy logic inference
system takes these parameters as inputs, compares the data with the stored
optimal levels and the generated output controls the heater and humidifier fan
as a closed loop feedback system. With the rise of temperature above optimal
level, the heat level is adjusted by reducing the heat and vice versa. The
humidifier fan speed decreases proportionally to the increase in relative
humidity so with decrease in humidity level the ventilation increases whereas
when relative humidity is low, the fan speed increases.

Figure 11: Block Diagram

##  IV RESULT

After defuzzification, the result can be analysed through the two outputs. For
the first 17 days, it can be observed that when the temperature and humidity
were 38°C and 53.60%, the estimate of stabilized heat and fan-speed were found
to be 5.00 and 5.00 respectively (considered in a range of 10).

TABLE II: Output for first 17 days Temperature (in °C) |  Relative Humidity(in %) |  Heat(in scale of 0 to 10) |  Fan Spped(in scale of 0 to 10)
---|---|---|---
0.00 | 34.30 | 7.02 | 6.06
24.70 | 45.20 | 6.38 | 5.79
34.30 | 48.80 | 5.68 | 5.37
35.50 | 51.20 | 5.33 | 5.00
38.00 | 53.60 | 5.00 | 5.00
41.60 | 58.40 | 4.80 | 4.40
50.00 | 17.50 | 4.25 | 7.20
58.40 | 62.00 | 3.43 | 3.92
64.50 | 69.30 | 3.19 | 3.65
76.50 | 75.30 | 3.09 | 3.64

For day 18-21, the criteria for relative humidity was changed as humidity
level increased. It was seen that when the temperature and humidity were
38.2°C and 64.10%,the estimate of stabilized heat and fan-speed were found to
be 5.00 and 5.00 respectively.

TABLE III: Output for day 18-21 Temperature (in °C) |  Relative Humidity(in %) |  Heat(in scale of 0 to 10) |  Fan Spped(in scale of 0 to 10)
---|---|---|---
0.00 | 34.10 | 6.98 | 6.28
22.30 | 40.40 | 5.80 | 6.00
33.10 | 52.20 | 5.26 | 5.47
35.50 | 59.20 | 5.06 | 5.20
38.20 | 62.60 | 5.00 | 5.00
40.40 | 64.10 | 4.86 | 4.17
51.20 | 19.90 | 4.25 | 7.60
57.20 | 80.10 | 3.23 | 3.52
68.10 | 85.70 | 3.19 | 3.25
76.50 | 87.30 | 3.06 | 3.04

The following graph represents the relation between input and output in the
fuzzy logic inference system. By observing the graph, it can be said that when
the temperature rises, the heat distribution falls but such changes vary in
non-linear fashion. As the humidity within incubation chamber increases, the
humidifier fan speed decreases considerably.

Figure 12: Graph representing changes in heat and fan speed for 1-17 days

For day 18-21, the incubation procedure requires additional humidity. During
this period, the optimal temperature level remains unchanged and hence, so
does the heat. The relation between input and output has been depicted in the
following graph:

Figure 13: Graph representing changes in heat and fan speed for 18-21 days

In an egg incubator, additional 1-2 degree Celsius in temperature can cause
early hatching with high mortality rate.On the contrary, reduction of 1-2
degree Celsius in temperature can cause late hatching with reduced hatching
rate. Considering such cases, the Mamdani fuzzy inference system proposed in
this paper serves as a far better choice than Takagi-Sugeno inference system.
This is because Takegi-Sugeno system gives the output of the system as linear
or constant in nature. So, the optimization of such systems are not accurate
and not up-to the mark. On the other hand, the heat and fan speed changes in
this proposed system is not only accurate but also considerably smooth.

##  V CONCLUSION

In conclusion, it can be said that the Mamdani fuzzy inference system used in
designing this model is much suitable for parameter controlling in Multiple
Input Multiple Output (MIMO) systems. Though other works have been conducted
in this field, most of them have implemented the TS method which does not
provide membership functions for the output so the transition of output
parameters due to unstable conditions in the field cannot be observed clearly.
Temperature and humidity are factors that vary unpredictably and non-linearly
in nature and considering such cases, the system presented here produces
stabilized output levels by controlling the heater and humidifier fan of an
egg incubator. The model proposed here can be applied in the egg incubation
process of other avian species as well by changing the crisp values of input
parameters. The model can also be modified for use in large scale systems as
well.

##

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