Hydroponic Nutrition Water Quality Identification System on
Cayenne Pepper Using Fuzzy Method Based on IoT
Mohammad Ainun Ardiansyah
1
, I Gede Wiryawan
1 a
, Khafidurrohman Agustianto
1 b
,
Prawidya Destarianto
1 c
, Shabrina Choirunnisa
1 d
and Fitri Krismiratsih
2
1
Department of Informatics Technology, Politeknik Negeri Jember, Jember, Indonesia
2
Department of Agribusiness Management, Politeknik Negeri Jember, Jember, Indonesia
Keywords: Internet of Things, Hydroponic, Fuzzy, Cayenne Pepper.
Abstract: This study aims for an Internet of Things system for determining the nutritional water quality of hydroponic
cayenne pepper plants based on fuzzy logic. Variable values were obtained from the readings of three sensors,
water temperature, TDS, and pH. Hydroponic cayenne pepper plants can grow optimally at a water
temperature from 18 to 28 °C, with a pH of 5 - 7. Nutrient levels in cayenne pepper will increase according
to the age of the chili. The IoT system can provide corrections to any variables outside the specified range by
giving commands from Arduino to the actuator. The actuators that run the water pump turn on and off, the
addition of nutrients and water levels, and the process of neutralizing the pH with KOH and HCl compounds.
The results of testing the IoT system send data to the website successfully, and then it is processed using fuzzy
logic. This study found that the average accuracy of the three sensors was 93.61%.
1 INTRODUCTION
Hydroponics is a plant cultivation technique that
utilizes water-containing nutrients as a growing
medium and no longer needs to use soil media. Water
is mixed with fertilizer to meet the nutrients required
by plants (Swastika et al., 2017). The level of
nutrients dissolved in water or the concentration of
the mixture is expressed by TDS (Total Dissolved
Solid). The TDS value is an essential indicator in
hydroponic cultivation systems. Plants have normal
levels to absorb nutrients for plant growth
(Rahmadhani et al., 2020). If the TDS value is too
high or too low, it will interfere with the absorption
of nutrients in plants. The effect of a TDS value that
is too high is that the plant leaves will turn yellow or
burn due to excess nutrients. The degree of acidity
(pH) and water temperature also affect the growth
rate of hydroponic plants. Temperatures that are too
high can cause plants to wither. So it needs good
regulation of nutrient levels, pH, and temperature in
hydroponic water.
a
https://orcid.org/0000-0002-8528-5011
b
https://orcid.org/0000-0002-0494-3124
c
https://orcid.org/0000-0001-6228-4967
d
https://orcid.org/0000-0001-8581-2136
Cayenne pepper is a leading commodity in
Indonesia. Cayenne pepper is commonly used to add
a spicy taste and natural red coloring to food. Based
on data from the Central Statistics Agency and the
Directorate General of Horticulture, the productivity
of cayenne pepper increased by 13.07% from 2017 to
2018, with a productivity of 7.78 tons/ha. Farmers are
increasingly carrying out the cultivation of cayenne
pepper with a hydroponic system. The advantage of
growing cayenne pepper using hydroponics is that it
does not require a large area of land and can be done
on a household scale.
Problems that arise and are still experienced by
many farmers are regulating and monitoring levels of
TDS values, pH, and hydroponic water temperatures
continuously. If nutrient water is not monitored
regularly and carefully, it can disrupt plant growth
(Aprillia & Myori, 2020). Improper nutrient water
regulation will affect the growth rate of cayenne
pepper plants. When monitoring nutrient water
conditions, Hydroponic farmers mostly do it
manually using a TDS meter to measure TDS value
Ardiansyah, M., Wiryawan, I., Agustianto, K., Destarianto, P., Choirunnisa, S. and Krismiratsih, F.
Hydroponic Nutrition Water Quality Identification System on Cayenne Pepper Using Fuzzy Method Based on IoT.
DOI: 10.5220/0012056600003575
In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2022), pages 1043-1051
ISBN: 978-989-758-619-4; ISSN: 2975-8246
Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
1043
and temperature; and a pH meter to measure the pH
value of nutrient water.
This study was preceded by using the linear
regression method to design a nutrient monitoring
information system for hydroponic plants (Wibowo et
al., 2022). In applying hydroponics, nutrition is a
need that must always be met for plant development,
where each plant requires different nutrients. The
Nutrient Film Technique (NFT) is a technique that is
often used in hydroponic cultivation. Because in this
method, the circulation of nutrients in the water will
constantly flow through the plant at any time. So plant
growth is faster because plants get oxygen and
nutrients all the time. The NFT technique is said to be
an energy-intensive technique because the water
pump will run continuously and still use human
power. Previously some studies designed the design
of the same system (Setyo Wibowo et al., 2022). This
study aims to design a nutrient monitoring
information system for hydroponic water spinach
plants with an NFT system to increase the
productivity of hydroponic farmers by automatically
regulating nutrition and monitoring nutrients in
hydroponic plants using the Linear Regression
method. This Linear Regression method can
determine the nutritional valve opening the next day
so that the system can monitor nutrition.
Previously, the internet of things had also been
carried out on oyster mushroom cultivation
(Agustianto et al., 2021). In this study, the fuzzy logic
method was applied to monitor oyster mushroom
cultivation. The research then proceeds to the
adaptive application of temperature and humidity
using a fuzzy neural network algorithm (Hartadi et
al., 2022). The use of modern technology can be a
solution to the above problems. One solution is to use
an Arduino microcontroller and several sensors such
as TDS, pH, and temperature sensors as readings for
hydroponic nutrient water conditions. Real-time
monitoring through the website can also help farmers
to know firsthand the necessity of nutrient water.
Then Farmers can access the website via a
smartphone or laptop. The microcontroller that can be
integrated by Arduino and used to send nutrient water
condition data to the website is the MCU node. The
MCU node sends data using the internet network.
Then the decision determination from the input is
processed using the Sugeno fuzzy logic method—this
study's purpose using it because of the suitability of
the problems described above. Sugeno's fuzzy logic
has a rule in the form of IF-THEN to determine the
desired result in using nutrient water conditions for
hydroponic cayenne pepper. This research aims to
monitor the quality of hydroponic water in real-time
and to produce a tool in the form of an IoT-based
hydroponic system. The decision results from fuzzy
logic calculations of water temperature are used for:
Automating on/off the water pump; Automating the
administration of nutrient concentrations; And
automating the administration of compounds to
neutralize the pH of the water.
2 LITERATURE REVIEW
2.1 Hydroponics
According to Sardare in (Dudwadkar et al., 2020)
hydroponics is the process of cultivating plants that
do not use soil media but water. Hydroponic plants
can be grown on a household scale as a hobby or a
large scale for commercial purposes. Cultivating this
plant does not require a large area of land. It can also
be done in the yard, on the house's terrace, or in a
greenhouse. Some hydroponic experts suggest
several advantages of the hydroponic system
compared to conventional farming, including more
efficient land use, plants growing without using soil,
no risk for continuous planting throughout the year,
higher and cleaner production quantity and quality,
and fewer human resource requirements. , the use of
fertilizers and water is more efficient, pest and disease
control is more manageable, and the selling price of
crop products is higher than non-hydroponic
products.
2.2 Hydroponic Nutrient Water
Hydroponic nutrient water is a mixture of water with
soluble inorganic minerals used as a nutrient provider
by plants. Plants need 16 nutrients/nutrients for
growth from air, water, and fertilizer. These elements
are carbon (C), hydrogen (H), oxygen (O), nitrogen
(N), phosphorus (P), potassium (K), sulfur (S),
calcium (Ca), iron (Fe), magnesium (Mg), boron (B),
manganese (Mn), copper (Cu), zinc (Zn),
molybdenum (Mo) and chlorine (Cl) (Agricultural
Research and Development Agency, 2019). Two
crucial factors in a nutrient solution formula are the
composition of the solution and the concentration of
the solution. These two factors greatly determine crop
production. Each type of plant requires a balance of
the amount and composition of the nutrient solution
and different concentration levels (Swastika et al.,
2017).
Every liquid has TDS (Total Dissolved Solid),
acidity (pH), and temperature. Total Dissolved Solid
(TDS) shows the number of solids dissolved in
iCAST-ES 2022 - International Conference on Applied Science and Technology on Engineering Science
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nutrients as measured from the TDS meter. Liquid
objects also have a degree of acidity written pH
(power of hydrogen). pH is used to express a
solution's acidity or alkalinity level. The pH of the
nutrient water is kept in the range of 5 to 7 to get good
results and tends to be acidic (Swastika et al., 2017).
Indicators of good nutrient water can also be seen in
the temperature of the water. The ideal temperature
for good nutrient water for plants is 18 – 28°C.
Temperatures too high will make the plants wither
quickly (Firdausyah et al., 2022).
2.3 Cayenne Pepper
Chili is a plant classified as a member of the genus
Capsicum. Chili is a food ingredient that is needed by
the community. Chili is commonly used as a cooking
ingredient and as a healthy plant. Chili also contains
nutrients that are indispensable for human health
(Afrilia, 2017). The cultivation of chili plants in
Indonesia is very diverse. Not only planted on
extensive land but also a narrow land, such as in the
yard of the house planted in pots and polybags or
planted in a hydroponic system. Cayenne pepper can
be grown hydroponically using the Dutch bucket
system. Here are the parameters/average temperature,
nutrient levels, and pH in the growth of hydroponic
chili plants.
Table 1: Parameters of Hydroponic Chili Plant Cultivation
Needs.
Parameters
Normal level
Temperature
18 – 28oC
Nutrition
500 – 2500 ppm
pH
5 - 7
2.4 Fuzzy Logic
Fuzzy logic was first introduced by a researcher at the
University of California, Barkley, in the field of
computer science named Prof. Lutfi A. Zadeh in 1965
(Adiguna, 2017). The difference between digital logic
and fuzzy logic is the degree of membership which
has a value of zero to one, while digital logic only has
a value of 0 or 1 which means "yes" or "no". Fuzzy
logic can explain an uncertainty phenomenon in a
mathematical model (Hariyadi, n.d.). Fuzzy divides
the degree of membership and truth on the interval
[0,1], which is something that can be partially true
and partially false at the same time. Fuzzy logic
translates a quantity described using language
(linguistics). For example, the magnitude of the TDS
value in ppm is expressed as very low, low, normal,
high, and very high. The advantages of fuzzy logic
are its ability to model very complex nonlinear
functions, easy-to-understand logic concepts, and
flexible use.
2.5 Internet of Things
Internet of Things is a modern and latest technology
that allows every electronic device to be controlled,
communicate, and exchange information between
other devices through the internet network (Ciptadi &
Hardyanto, 2018). Every device connected to the
internet can exchange data and information. IoT can
also virtualize anything tangible onto the internet.
Another advantage of IoT is that it can monitor a
device that works remotely by connecting it to the
internet and then using other devices such as
smartphones. Not only can it be used to watch, but
IoT also acts as a medium for remote control of
working devices. The function of IoT is to facilitate
human work, increase work efficiency, and increase
work productivity.
2.6 Website
A website is an application stored on a server
computer that is accessed via the internet. According
to Yeni Susilowati, a website has several pages with
interrelated topics between one page and another.
Websites are generally built using HTML and CSS
programming languages to beautify the appearance of
a page (Reizandi, 2019). The contents of the website
pages vary and can contain text or text, images,
videos, animations, or a combination of one another.
Data from the website is stored in a structured
database. Someone who wants to access a web page
must type the URL in a browser.
3 DESIGN METHODOLOGY
3.1 Fuzzy Membership Function
This study uses a fuzzy logic method to determine the
nutritional water quality of hydroponic chili plants,
whose value is obtained from the readings of three
sensors. The variables of this study were water
temperature, nutrient content (ppm), and acidity (pH).
Water temperature has a fuzzy set: cold, average, and
hot. Nutrient levels have a group: low, sufficient, and
high. The degree of acidity or pH has a fuzzy set:
acidic, neutral, and essential. The collection of each
variable described above is presented as a graphic
diagram of the group of fuzzy membership functions.
Hydroponic Nutrition Water Quality Identification System on Cayenne Pepper Using Fuzzy Method Based on IoT
1045
Figure 1: Water Temperature Chart.
If the temperature is too hot or more than 29 °C,
the nutrient water pump from the reservoir will work
to cool the water in the pot. The best average water
temperature as nutrient water is in the range of 18 –
29 °C. However, if the temperature is too cold or
below 18 °C, the system does not perform a
command, and the nutrient water heating process
does not occur in the reservoir.
Figure 2: Water Ppm Chart.
The water ppm graph is obtained from the data
regarding the relationship between water ppm and the
addition of ab mix nutrient water or the addition of
water. The picture above shows that the ppm level
was entirely from 1200 to 1400. The ppm is adjusted
according to the age of the chili plant. If the ppm is
too high or more than specified, the water pump will
work to lower the nutrient levels. However, if the ppm
is too low or below the set, the system will add a mix
of nutrients to the nutrient water bath.
Figure 3: Water pH Chart.
The pH graph of the water is obtained from the
data on the relationship between the pH of the water
and the neutralization of the acidity of the nutrient
water. The neutral pH of the nutrient water is in the
range of 5 – 7. The system will add alkaline
compounds to neutralize the nutrient water if the pH
is too acidic. However, the system will add a sour
mixture if the pH is too alkaline or above 7.
Figure 4: Water Quality Output Chart.
Water quality is declared less good if it is in values
0-50. From this value, various actions are taken
according to predetermined rules. Nutrient water
conditions are announced well if the fuzzy calculation
results show a value range of more than 50 to 100.
Based on interviews with experts, the author's
observations showed that there are two kinds of
nutritional water quality for hydroponic chili plants,
namely water quality with good conditions and water
quality with poor conditions. Good water conditions
are temperatures in the normal range and ppm in an
acceptable range. While an acid or alkaline state can
tolerate it, the IoT system still gives orders to the
actuator to neutralize it.
3.2 Determination of Nutritional Water
Quality
Table 2: Determination of Nutritional Water Quality
Conditions.
Rule
Temp
Ppm
Ph
Quality
[R1]
Cold
Low
Acid
Not Good
[R2]
Cold
Low
Netral
Not Good
[R3]
Cold
Low
Alkali
Not Good
[R4]
Cold
Fair
Acid
Not Good
[R5]
Cold
Fair
Netral
Not Good
[R6]
Cold
Fair
Alkali
Not Good
[R7]
Cold
High
Acid
Not Good
[R8]
Cold
High
Netral
Not Good
[R9]
Cold
High
Alkali
Not Good
[R10]
Normal
Low
Acid
Not Good
[R11]
Normal
Low
Netral
Not Good
[R12]
Normal
Low
Alkali
Not Good
[R13]
Normal
Fair
Acid
Good
[R14]
Normal
Fair
Netral
Good
[R15]
Normal
Fair
Alkali
Good
[R16]
Normal
High
Acid
Not Good
[R17]
Normal
High
Netral
Not Good
[R18]
Normal
High
Alkali
Not Good
[R19]
Hot
Low
Acid
Not Good
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Table 2: Determination of Nutritional Water Quality
Conditions.(cont.)
Rule
Temp
Ppm
Ph
Quality
[R20]
Hot
Low
Netral
Not Good
[R21]
Hot
Low
Alkali
Not Good
[R22]
Hot
Fair
Acid
Not Good
[R23]
Hot
Fair
Netral
Not Good
[R24]
Hot
Fair
Alkali
Not Good
[R25]
Hot
High
Acid
Not Good
[R26]
Hot
High
Netral
Not Good
[R27]
Hot
High
Alkali
Not Good
3.3 Actuator Motion Determination
On Arduino, each variable's fuzzy logic calculation
process is carried out without relating to other
variables. The calculation is done to determine the
motion of the actuator so that the quality of nutrient
water is always in good condition. The following is a
table for deciding actuator motion in an IoT system.
Table 3: Actuator Motion Determination.
Temperature
Cold
Normal
Hot
PPM
&
PH
Low
& Acid
Add
nutrition,
add KOH
Add
nutrition,
add KOH
Add
nutrition,
add KOH,
turn on the
nutrition
water pump
Low &
Netral
Add nutrition
Add nutrition
Add nutrition,
turn on the
nutrition
water pump
Low
& Alkali
Add nutrition,
Add HCl
Add nutrition,
Add HCl
Add nutrition,
Add HCl, turn
on the
nutrition
water pump
Fair
& Acid
Add KOH
Add KOH
Add KOH,
turn on the
nutrition
water pump
Fair
& Netral
No action
No action
Turn on the
nutrition
water pump
Fair
& Alkali
Add HCl
Add HCl
Add HCl, turn
on the
nutrition
water pump
High
& Acid
Add water,
add KOH
Add water,
add KOH
Add water,
add KOH,
turn on the
nutrition
water pump
High
& Netral
Add water
Add water
Add air, turn
on the
nutrition
water pump
High
& Alkali
Add water,
Add HCl
Add water,
Add HCl
Add water,
add HCl, turn
on the
nutrition
water pump
3.4 Plant Nutrient Needs
The nutritional needs of hydroponic plants are
directly proportional to the age of the plant. The older
the chili plant, the more dietary needs are. The
following is a table of the nutritional needs of
hydroponic cayenne pepper plants. The following is a
table of the dietary needs of hydroponic cayenne
pepper plants, along with the age of the plant.
Table 4: Hydroponic Chili Nutrition Needs.
Plant Age (HST)
Nutritional needs
(ppm)
8 – 14 HST
500 – 700
15 – 21 HST
700 – 1000
22 – 28 HST
1000 – 1200
29 – 35 HST
1200 – 1400
36 – 42 HST
1200 – 1400
43 – 49 HST
1400 – 1600
50 – 56 HST
1600 – 1800
57 – 63 HST
1800 – 2000
64 – and so on
2000 – 2200
3.5 Research Stages
The following is a flow chart of the system's work,
illustrated in the image below. The diagram explains
the input flow in the form of sensor readings, part of
the process, namely calculations with fuzzy logic, and
output commands to the actuator and sending sensor
reading values to the website.
Figure 5: How the System Works.
Hydroponic Nutrition Water Quality Identification System on Cayenne Pepper Using Fuzzy Method Based on IoT
1047
The system's first part is the reading of
temperature sensor values, TDS, and pH on
hydroponic nutrients. The units for each data are:
TDS has units of ppm (parts per million); PH has
units of pH (Power of Hydrogen); The author takes
units of degrees Celsius (°C) for temperature data.
The three sensors are immersed in the nutrient
water in the sensor reading container. The sensor
placement should be protected from direct sunlight so
that the sensor usage time becomes longer. The sensor
readings are received by Arduino and then processed
using fuzzy logic.
The calculation process is carried out starting
from the fuzzification process. Fuzzification is
converting actual values into a fuzzy form using a
membership function. The three sensor values are
searched for the membership function value or its
Miu(). Then the inference process is carried out;
namely, the application of the IF THEN ELSE rule
based on the knowledge base of the expert. After that,
defuzzification changes back from the fuzzy value to
a firm value that will be used as a determinant of the
actuator's work. Temperature data is also sent to the
MCU Node, which is then sent to the website.
The output of the Fuzzy calculation is in the form
of nutritional water quality conditions on the website.
On this website, farmers can monitor whether the
quality of nutrient water is in good condition or not.
Arduino also processes sensor data into fuzzy logic in
response to actuators. The calculation results
determine if, for example, the temperature is included
in the too high category. Arduino will give an order
to pump nutrient water to drain water with a lower
temperature in the reservoir. Or if the TDS sensor
readings find that the nutrient level is too low, the
system will drain the nutrients to the pool.
3.6 IoT Devices Flowchart
The first IoT system workflow is the reading of
temperature, TDS, and pH sensors in nutrient water.
The results of the sensor readings are sent to Arduino,
and then the calculation process is carried out with
fuzzy logic. Each sensor value is calculated fuzzy to
determine the actuator output to be run. Arduino will
make corrections to stabilize the condition of the
nutrient water so that it is always in good condition.
Figure 6: IoT Devices Flowchart.
3.7 Website System Flowchart
The system's workflow on the first website is admin
or farmer logging in on the login page by filling in the
username and password. If appropriate, it will enter
the dashboard page. Admins or farmers can see the
current condition of nutrient water quality and graph
each sensor reading sent to the website. The admin
can process (add, change, delete) the ppm data on the
ppm processing data page. The fuzzy calculation page
displays the results of calculations with fuzzy logic
for determining nutrient water quality.
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Figure 7: Website System Flowchart.
3.8 Design of Iot Devices
The design of IoT tools will be described in this
section. Electronic components consist of Arduino as
a microcontroller for sensor readings and a command
center for the actuator.
Figure 8: Desain Alat IoT.
4 RESULTS AND DISCUSSIONS
The following is a website page to monitor the quality
of hydroponic nutrient water for cayenne pepper
plants. There are three graphs of each sensor reading,
and the results of the last sensor readings and the
nutritional water quality are also displayed.
Figure 9: Dashboard Page.
This ppm data processing page is helpful for
processing ppm data that is adjusted to the planting
age of chili. Admins or farmers can process the data,
such as adding, changing, and deleting data. This
page contains the age range of chili peppers and the
minimum – maximum levels of nutrients that plants
need. Farmers can activate it via a button, and the
fuzzy logic calculation process will automatically
follow the selection of the active ppm range.
Figure 10: Ppm Processed Data Page.
Hydroponic Nutrition Water Quality Identification System on Cayenne Pepper Using Fuzzy Method Based on IoT
1049
Figure 11: IoT Device System.
Figure 11 is a ready-made IoT tool. Electronic
components such as Arduino, ESP32 MCU nodes,
and how many relays are in the white case. Between
electronic components are connected with jumper
cables. The solenoid valve connected to the relay is
also located in a white container, whose water flows
from a line of pipes. The brown case has a 12V DC
ADAPTER and a 5V DC ADAPTER 2 USB ports.
The 12V DC adapter is used to supply power to two
pumps and four solenoid valves. ADAPTER DC 5 V
2 USB ports control the Arduino and the ESP32 MCU
node. All ADAPTERS are connected to 220V AC
home power.
Table 5: Pengujian Akurasi Alat.
Var.
Man.
Sensors
Dev.
Dev. (%)
Accu.
(%)
Temp
29,9
28,06
1,84
6,55
93,45
Ppm
506
497
9
1,81
98,19
PH
6
6.65
0,65
10,83
89,17
Av.
3,83
6,39
93,61
The table above results from testing the accuracy
of IoT tools compared to manual measuring tools.
The most significant difference between reading the
sensor and reading from a manual measuring
instrument is in ppm, which is 9. However, in this
case, it is in the range of 500, much higher than the
other variables. The temperature and pH variables are
in the field of tens and units values (temperature in
the range of 28 - 29, pH in the range of 6 - 7). So if it
is calculated based on proportions, reading the ppm
value has the smallest value of 1.81% and has the
highest accuracy of 98.19%. The accuracy of reading
the pH sensor on the measurement of the measuring
instrument showed the lowest result, 89.17%, with a
difference of 10.83%. It happens because the sensor
has been used for a long time and may be damaged,
or it could be from reading a manual measuring
instrument that uses litmus paper, not a pH meter, to
make it more accurate. At the IoT accuracy testing
stage, the accuracy value is 93.61%, so the IoT tool
runs quite accurately.
5 CONCLUSIONS
This research has succeeded in developing a
Hydroponic Nutrition Water Quality Identification
System. Determination of nutritional water quality of
hydroponic using fuzzy logic method. The nutrition
content was increased successfully by giving
commands via Arduino to the actuator. The actuator
activates and deactivates the water pump that already
contains the required nutrients. The results of testing
the IoT system send data to the website successfully,
and then it is processed using fuzzy logic. This study
found that the average accuracy of the three sensors
was 93.61%. This research can be extended to the
other plant case for further work. So the variable can
be variated.
ACKNOWLEDGEMENTS
We did all stages of this research with the funding
support of the research and community service center
of Politeknik Negeri Jember. This research take place
at KSI Laboratory, so thanks to the Information
Technology Department of Politeknik Negeri Jember
for providing all the facilities.
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Hydroponic Nutrition Water Quality Identification System on Cayenne Pepper Using Fuzzy Method Based on IoT
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