Performance evaluation of low-cost IoT based chlorophyll meter

Bulletin of Electrical Engineering and Informatics
Vol. 9, No. 3, June 2020, pp. 956~963
ISSN: 2302-9285, DOI: 10.11591/eei.v9i3.2014  956
Journal homepage: http://beei.org
Performance evaluation of low-cost IoT based
chlorophyll meter
Heri Andrianto1, Suhardi2, Ahmad Faizal3
1,2
School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia
3
School of Life Sciences and Technology, Institut Teknologi Bandung, Indonesia
Article Info ABSTRACT
Article history:
Received Aug 4, 2019
Revised Oct 23, 2019
Accepted Feb 4, 2020
Nutrient deficiencies in plants can be identified using a chlorophyll meter.
However, current chlorophyll meters are still expensive and have many
disadvantages. In this paper, a low-cost IoT-based chlorophyll meter has
been developed. The performance of a low-cost IoT-based chlorophyll meter
has been compared with the performance of a spectrophotometer
(SP-3000nano) and a commercial chlorophyll meter (SPAD-502). A low-cost
IoT-based chlorophyll meter has been functioning properly which is able to
measure the chlorophyll content of plants in the field, get positions based
on GPS satellites, store data in a memory module, and send data to
the service system platform. The test results showed the coefficient
of determination (R2
) between SPAD-502 values and low-cost IoT-based
chlorophyll meter values is 0.9705, this shows a significant correlation.
An IoT-based chlorophyll meter can be used as a cheap alternative to
the SPAD-502 chlorophyll meter.
Keywords:
Chlorophyll meter
Internet of Things (IoT)
Plant nutritional deficiencies
This is an open access article under the CC BY-SA license.
Corresponding Author:
Heri Andrianto,
School of Electrical Engineering and Informatics,
Institut Teknologi Bandung,
Jl. Ganesha No.10, Bandung, Indonesia.
Email: heri.andrianto@students.itb.ac.id
1. INTRODUCTION
Communication and information technology will facilitate farmers to explore innovative ways to
minimize risks and to increase production [1]. Recently, Internet of Things (IoT) as one part
of communication and information technology is developing rapidly and has been widely applied in various
fields, such as in agriculture. The IoT is a combination of all the "things" by using the internet as
the backbone of a communication system to build intelligent interactions between people and objects around
them [2, 3]. Most IoT technology solutions in the field of agro-industry and the environment focus on
monitoring (62%), controls (25%), logistics (7%), and predictions (6%) [4]
In field of agriculture, the nitrogen content in the soil can be determined through the chlorophyll
content of plant leaves, because nitrogen content is strongly related to chlorophyll content [5, 6].
Sensor technology can be used to detect the chlorophyll content of plant leaves based on the ability of the
leaves to absorb, transmit and reflect light [7]. This technology has been applied to spectrophotometer [8, 9]
and chlorophyll meter [10-14]. Spectrophotometer can be used to analyze chlorophyll pigments in extracted
plant leaves, but this method has disadvantages such as destructive, tiring and time consuming [13].
This device is not only expensive but also has a large size therefore it is only used in laboratories.
Meanwhile, between fields and the laboratories are generally separated by long distances [15].
Therefore, carrying the leaves of plants from field to the laboratory becomes ineffective and inefficient.

Bulletin of Electr Eng & Inf ISSN: 2302-9285 
Performance evaluation oflow-cost IoT based chlorophyll meter (Heri Andrianto)
957
A chlorophyll meter allows non-destructive measurement of chlorophyll content in the field,
however chlorophyll meters available on the market are still expensive. Van Den Berg and Perkins [16] have
reported that a portable chlorophyll meter (CCM-200) is an effective, rapid, and non-destructive devices for
estimating the chlorophyll content in sugar maple leaves during the growing season. Another chlorophyll
meter is SPAD-502, it is the most widely used chlorophyll meter [7]. Several studies have been conducted to
measure the chlorophyll content of various plants using SPAD-502 [10, 17-21]. The chlorophyll content in
leaves is expressed in Chlorophyll Content Index (CCI) unit or Soil Plant Analysis Development (SPAD)
unit. Chlorophyll content of plant leaves in the CCI unit is calculated using [22]:
CCI =
% transmission IR
% transmission RED
(1)
Conversion from CCI to SPAD is calculated using [22]:
SPAD = k log(CCI) + c (2)
Where:
k: Confidential slope coefficient
c: Confidential offset value
As an example, computational processes for calculating the amount of Nitrogen (N) fertilizer that
must be given to plants (corn and wheat) consists of calculating the average value of chlorophyll content
from the field and calculating chlorophyll content from the reference area where N levels are high.
N recommendation for plants (corn and wheat) is calculated using [23-25]:
N = 6 + (7 x D) (3)
Where:
N = N (lb/A) needed at Feekes 5 for optimal growth
D = The difference between the average chlorophyll measurements from the agricultural land and the
reference areas that have high N levels (in a SPAD unit)
2. RELATED WORKS
Several studies on the design and realization of a low-cost chlorophyll meter have been carried out.
Takeuchi et al [26] reported the development of a simple PAM chlorophyll fluorometer for monitoring
vegetation conditions using a Programmable Logic Controller (PLC) and a computer. Maleki et al [27]
reported the development of a device to measure nitrogen content in lettuce plants using the TCS230 sensor.
Lamb et al [28] reported the development of a fluorometers based on LED for quantification of chlorophyll
in the field and in the laboratory. Sookchalearn and Abdullakasim [29] reported the development of a tool to
measure the chlorophyll content of cassava plants using TCS230 sensor and added a Global Position System
(GPS) module to get a location when measuring chlorophyll content of plant leaves in the field.
Limitations of previous studies, namely requires expensive costs [26], destructive [28], no data sending
facility to the server for agricultural data management [26-29], no data storage facility in non volatile
memory therefore data will be lost if the power is turned off [26-29], no data location based on GPS
satellites [26-28], and no interface to external devices [26-28].
New innovations are needed on chlorophyll meter devices to meet user needs and provide solutions
to the current limitations of chlorophyll meters, namely by applying IoT technology, storage facility,
location based on GPS satellites and data sending facility to the service systemplatformfor agricultural data
management. This study is primarily interested in design and realization of a low-cost IoT-based chlorophyll
meter for collecting chlorophyll content and location data from the field and send it to the service system
platform. The performance of a low-cost IoT-based chlorophyll meter is also compared to more expensive
chlorophyll meter (SPAD-502) and spectrophotometer(SP-3000nano).
3. RESEARCH METHOD
The research method consists of four stages, namely designing IoT-based service system
architecture for monitoring plant nutrition deficiencies (Figure 1), developing low cost IoT-based chlorophyll
meter, testing the low cost IoT-based chlorophyll meter and evaluating the performance of a low cost

 ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 9, No. 3, June 2020 : 956 – 963
958
IoT-based chlorophyll meter. The first stage is designing IoT-based service system architecture for
monitoring plant nutrition deficiencies. The IoT-based service system architecture for monitoring plant
nutrition deficiencies consists of IoT-based chlorophyll meters, wireless access points, service system
platforms and remote monitoring devices. In this system, farmers can measure the chlorophyll content
of plant leaves, mark a location based on GPS satellites at the time of measurement in the field and send data
to the service system platform using an IoT-based chlorophyll meter. The low-cost IoT-based chlorophyll
meter uses the API Key as an ID to enter and communicate with the service systemplatform. This systemcan
be used for effective and efficient agriculture because it only requires one IoT-based chlorophyll meter
device to collect data rather than installing multiple sensors in the field, such as in the Wireless Sensor
Network system(WSN) [30-31].
Figure 1. An IoT-based service systemarchitecture for monitoring plant nutrition deficiencies
3.1. Developing a low-cost IoT-based chlorophyll meter
The second stage is developing a low cost IoT-based chlorophyll meter. Low cost IoT based
chlorophyll meter devices consist of NodeMCU, red and infrared LEDs, spectral sensor (TSL2561),
GPS module (GPS6MV2), LCD module, pushbutton, and memory module. Table 1 shows connection
of NodeMCU pins with other components. Figure 2 shows an IoT based chlorophyll meter device
that has been developed.
Table 1. Connection of NodeMCU pins with othercomponents
NodeMCU Pins LCD Module TSL2561Sensor Memory Module GPS6MV2 PushButton
D0 D0
D1 SCL SCL
D2 SDA SDA
D5 SCK
D6 MISO
D7 MOSI
D8 CS
D9 TX
D10 RX
Figure 2. IoT based chlorophyll meter realization

959
3.2. IoT-based chlorophyll meter flowchart
Main program flowchart (Figure 3), starting with the process of initializing the components,
then selecting the online or offline mode. If offline mode is selected, then the data will be stored
in the memory module when the button is pressed. If online mode is selected, then the data will be sent
to the database server when the button is pressed. Sub program flowchart (Figure 4) to obtain chlorophyll
content and location based on GPS satellites starting with the process of obtaining positions based on GPS
satellites (Long and Lat), reading the intensity of red and infrared light transmitted by the leaf on the spectral
sensor,and calculating the CCI values. Finaly, CCI, Long and Lat are displayed on the LCD.
Figure 3. Flowchart of main program Figure 4. Flowchart of process
CCI and GPS
4. RESULTS AND DISCUSSION
A low-cost IoT-based chlorophyll meter testing has been conducted by comparing the performance
of a low-cost IoT-based chlorophyll meter with a chlorophyll meter (SPAD-502) and a spectrophotometer
(SP-3000nano). Measurements of chlorophyll content were conducted on the leaves of maniltoa grandiflora
(young leaves, adult leaves and old leaves). The leaf chlorophyll content was measured using a low-cost
IoT-based chlorophyll meter and a SPAD-502 at four different positions (Figure 5) and repeated five times
which were then taken the average value. We have also measured the leaf chlorophyll content using
a spectrophotometer (Figure 6). As shown in Figure 7 to Figure 9, the measuring chlorophyll content results
using a spectrophotometer, a SPAD-502 and a low-cost IoT-based chlorophyll meter are strongly correlated.
It can be concluded that young leaves have less chlorophyll content than mature leaves and old leaves.
The leaves that contain the most chlorophyll are old leaves. As chlorophyll is linearly correlated with

 ISSN: 2302-9285
960
nitrogen, young leaves have less nitrogen content than mature leaves and old leaves. Young leaves need more
nitrogen than mature leaves and old leaves.
Figure 5. Measurement of chlorophyll content in four
positions
Figure 6. Measurement of chlorophyll content
through Leaf extract
Figure 7. Measurement of chlorophyll content using
Spectrophotometer
Figure 8. Measurement of chlorophyll content using
SPAD-502
Figure 9. Measurement of chlorophyll content using low-cost IoT-based chlorophyll meter
The strength of the correlation between chlorophyll content measurement using a low-cost
IoT-based chlorophyll meter and a SPAD-502 is assessed using the coefficient of determination (R2).
From the test results, we obtained an R2 of 0.9705 (Figure 10), this shows a very strong correlation between
low-cost IoT-based chlorophyll meter values and SPAD-502 values. A low-cost IoT-based chlorophyll
meter has been used successfully to measure chlorophyll content, these results in accordance with other
reports [13, 27, 29, 32].
0
10
20
1 2 3 4 5
Measurement Results Using
Spectrophotometer (Unit: mg/L)
Young leaves Mature Leaves Old leaves
0
50
100
1 2 3 4 5
Measurement Results UsingSPAD-
502
(Unit: SPAD)
0
5
10
15
1 2 3 4 5
Measurement Results UsingIoT
Based Chlorophyll Meter (Unit: CCI)

961
Figure 10. Correlations between SPAD values and IoT-based Chlorophyll meter in the leaves
of maniltoa grandiflora
Testing of a sensor module, a memory module, and an LCD module is conducted by inserting leaf in
a low-cost IoT-based chlorophyll meter and pressing a button to save data into memory and send data to
the server. Test results show the device has worked well, chlorophyll content of plant leaves and location
based on GPS satellites can be displayed on the LCD, stored in memory and sent to the server.
Figure 11 shows the data displayed on the LCD and data in the form of text files stored in memory.
Figure 11. Memory module and LCD test results
Testing of communication between IoT-based chlorophyll meter device and service system
platforms has been conducted by sending chlorophyll content and location based on GPS satellites data from
a low-cost IoT-based chlorophyll meter to the service system platform. Figure 12 shows data received by
the service systemplatform is displayed on monitor page (http://e-agriculture.net/).
Figure 12. Data from a low-cost IoT-based chlorophyll meter is displayed on the monitor page
y = 5.171x + 5.3551
R² = 0.9705
0
20
40
60
80
0 5 10 15
Chlorophyll
meter
SPAD-
502
(SPAD) IoT Based Chlorophyll meter
(Chlorophyll Content Index, CCI)

 ISSN: 2302-9285
962
5. CONCLUSION
The results have shown that low-cost IoT-based chlorophyll meter are functioning properly,
which is able to measure chlorophyll content, get location, display data on the LCD, store data in memory,
and send data to the service system platform. A strong correlation was obtained between measurements of
chlorophyll content using IoT-based chlorophyll meters, SPAD-502 and spectrophotometer. The test results
showed a significant relationship between SPAD-502 values and low-cost IoT-based chlorophyll meter
values. Therefore, IoT-based chlorophyll meter can be used as a cheap alternative to SPAD-502 chlorophyll
meter. In our future work, the data collected in the database server will be processed for the purpose
of providing fertilizer recommendations to plants. In addition, the performance of a service system platform
to monitor the chlorophyll content of plants (http://e-agriculture.net/) will be evaluated.
ACKNOWLEDGEMENTS
Authors would like to thank Institut Teknologi Bandung for the financial support that has been
provided (Grants No. 684b/I1. B04.2/SK/2018). Thanks also to Universitas Kristen Maranatha for funding
scholarships.
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BIOGRAPHIES OF AUTHORS
Heri Andrianto is currently a Ph.D. student in the School of Electrical Engineering and
Informatics, Institut Teknologi Bandung, Indonesia. He received his bachelor’s degree in
electrical engineering from Universitas Kristen Maranatha, Bandung, Indonesia, in 2004, and his
master’s degree in information technology from Institut Teknologi Bandung (ITB), Bandung,
Indonesia, in 2007. He is interested in the field of embedded system, microcontroller, service
computing, and internet of things.
Suhardi received the B.E., M.E. in Electrical Engineering from Institut Teknologi Bandung
(ITB), Indonesia, in 1988 and 1992, respectively. He received Doktor der Ingeneuer (Dr.-Ing.) in
Electrical Engineering from Technical University of Berlin, Germany in 1997. In 1990, he joints
the School of Electrical Engineering and Informatics ITB as a lecturer and affiliated with
Information Technology Research Group. He is a member of IEEE. He is a professor in services
computing area. His research interests are in the areas of services computing, service systems
engineering, digital forensics and cyber-crime, and IT value engineering.
Ahmad Faizal received the B.Sc., M.Sc. in the School of Life Sciences and Technology, Institut
Teknologi Bandung (ITB), Bandung, Indonesia, in 2004, and 2006. He obtained his PhD. in
Applied Biological Sciences from Ghent University, Belgium in 2013. Currently, he is a faculty
member of School of Life Sciences and Technology ITB. He is interested in the field of plant
sciences and biotechnology.

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