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Designing an IoT-Cloud Gateway for the Internet of Living Things

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Designing an IoT-Cloud Gateway for the Internet of Living Things

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Cloud Computing and the Internet of Things (IoT) have started to revolutionize traditional systems to be smart. Smart farming is an example of this process, that aims to respond to predictions and provisions of population growth by providing smart solutions in agriculture to improve productivity and reduce waste. Plant phenotyping is an important research field related to smart farming by providing means for complex monitoring of development and stress responses of plants. The current phenotyping platforms for greenhouses are very expensive limiting their widepread use. The recent advances in ICT technologies with the appearance of low cost sensors and computing solutions have led to affordable phenotyping solutions, which can be applied in standard greenhouse conditions. In this paper we propose a low cost plant phenotyping platform for small sized plants called the IoLT Smart Pot. It is capable of monitoring environmental parameters by sensors connected to a Raspberry Pi board of the smart pot. We developed an IoT-Cloud gateway for receiving, storing and visualizing the monitored environmental parameters sent by the pot devices. It is also able to perform image processing on the pictures of the plants to track plant growth. We have performed a detailed evaluation of our proposed platform by means of simulation, and exemplified real world utilization.
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Designing an IoT-Cloud Gateway
for the Internet of Living Things?
Tamas Pflanzner1, Miklos Hovari2, Imre Vass2, and
Attila Kertesz1[0000000294572928]
1Software Engineering Department, University of Szeged, 6720 Szeged, Dugonics ter
13, Hungary, {tampfla,keratt}@inf.u-szeged.hu
2Institute of Plant Biology, Biological Research Centre, Hungary,
{hovari.miklos,vass.imre}@brc.mta.hu
Abstract. Cloud Computing and the Internet of Things (IoT) have
started to revolutionize traditional systems to be smart. Smart farming
is an example of this process, that aims to respond to predictions and
provisions of population growth by providing smart solutions in agricul-
ture to improve productivity and reduce waste. Plant phenotyping is an
important research field related to smart farming by providing means
for complex monitoring of development and stress responses of plants.
The current phenotyping platforms for greenhouses are very expensive
limiting their widepread use. The recent advances in ICT technologies
with the appearance of low cost sensors and computing solutions have
led to affordable phenotyping solutions, which can be applied in standard
greenhouse conditions. In this paper we propose a low cost plant phe-
notyping platform for small sized plants called the IoLT Smart Pot. It
is capable of monitoring environmental parameters by sensors connected
to a Raspberry Pi board of the smart pot. We developed an IoT-Cloud
gateway for receiving, storing and visualizing the monitored environmen-
tal parameters sent by the pot devices. It is also able to perform image
processing on the pictures of the plants to track plant growth. We have
performed a detailed evaluation of our proposed platform by means of
simulation, and exemplified real world utilization.
Keywords: Cloud Computing ·Internet of Things ·Plant Phenotyping
·Gateway
1 Introduction
According to recent reports in the field of the Internet of Things (IoT) (e.g.
[1]), there will be 25 billion connected things by 2021. These estimations call for
smart solutions that provide means to connect, manage and control these de-
vices efficiently. IoT can be envisioned as a dynamic network with self-configuring
?The research leading to these results was supported by the Hungarian Government
and the European Regional Development Fund under the grant number GINOP-
2.3.2-15-2016-00037 (”Internet of Living Things”). This paper is a revised and ex-
tended version of the conference paper presented in [28].
capabilities, in which devices (that are called as things) can interact and commu-
nicate among themselves and with the environment by exchanging sensor data.
Such systems can be utilized in many application areas, thus they may have very
different properties.
Smart farming is also a rapidly growing area within smart systems, that
need to respond to great challenges of the near future. By 2050, it is expected
that global population will grow to 9.6 billion as the United Nations Food and
Agriculture Organisation predicts. A recent Beecham Research report [2] also
states that food production have to respond to this growth to increase it with
70% till 2050. This report also states that agriculture is responsible for a fifth
of greenhouse gas emissions and for 70 percent of the world’s fresh water usage,
which strives for a reform. IoT supported by cloud services has the potential to
implement the required changes [3].
Plant phenotyping [4] also evolves rapidly and provides high throughput ap-
proaches for monitoring the growth, physiological parameters, and stress re-
sponses of plants with high spatial and temporal resolution. Recent advances
use the combination of various remote sensing methods that can exploit IoT and
cloud technologies. In the past typical plant phenotyping platforms used very
expensive instrumentation to monitor several hundreds, even few thousands of
plants. Although these large infrastructures are very powerful, they have high
cost ranging to a few mEUR per platform, which limits their widespread, ev-
eryday use. Due to recent ICT developments we can apply novel sensor and IoT
technologies to provide a promising alternative, called affordable phenotyping.
Our research goals also point to this direction, and in this paper we propose a
low cost plant phenotyping platform for small sized plants, which enables the
remote monitoring of plant growth in a standard greenhouse environment. In an
earlier work we introduced the first prototype of our IoLT Smart Pot [28]. In this
work we discuss its extension for leaf area calculations, and present a detailed
evaluation of it.
The main contributions of this paper are the design and implementation of
the IoLT Smart Pot Gateway for managing smart pot clusters by monitoring
their environmental parameters. This IoT-Cloud platform is capable of collect-
ing, storing and visualizing sensor data, as well as performing leaf area calcula-
tions with image processing to allow plant growth tracking. We also evaluate the
proposed solution with scalable simulations, and exemplify real world utilization.
The remainder of this paper is as follows: Section 2 introduces related ap-
proaches for smart farming and plant monitoring, and Section 3 highlights our
research aims and discusses the proposed smart pot solution. Section 4 presents
a detailed evaluation of our gateway framework by means of simulation, and Sec-
tion 5 shows real world utilization. Finally, we conclude the paper in Section 6.
Table 1. Comparison of commercial smart pot solutions
Product Main features No. of plants Price (EUR)
Xiaomi Flora indicator board, salt content mon. 1 40
Parrot pot self-watering system, 4 sensors 1 50
sPlant light supplement 4 90
PlantRay soil moisture, color change, beep 1 19
Tregren control light, water and nutrients 3-6-12 90
Odyseed automatic irrigation and lighting 2 70
Click & Grow water plants automatically, LEDs 3-9 100-200
CitySens self-watering, app 3-5 176
LeGrow modular 1+ 40
AeroGarden LEDs, Amazon Echo 2-24 40-450
SmartPot many sensor prototype 1 NA
Lua 15 different universal animated emotions 1 100
TOKQI pet plant to play music, bluetooth speaker 1 12
HEXA AI robot moving to sunshine 1 950
2 Related work
Smart system design and development have started to flourish. Smart farming
is also getting very popular, there are many commercial solutions and products
in household areas.
Concerning indoor plant monitoring, many tools are available for monitoring
temperature, humidity, light, water level and salt content of the plant soil, and
are able to communicate with nearby devices. Some advanced systems are capa-
ble of automatic watering or provide notifications or even remote control through
mobile applications. Table 1 shows a comparison of the available solutions, and
we briefly introduce them in the following.
PlantRay [8] is a really basic smart pot, it has a soil moisture sensor, and it
changes color and beeps if watering is required. The battery may last for a year.
AeroGarden [9] has many different size products, able to hold from two to 24
plants. They have LED lights, but only the bigger ones have automatic LED con-
trol. The most advanced one can be connected with Amazon Echo. Xiaomi Flora
[5] has an indicator board for providing information coming from the sensors,
and it has a dedicated application for remote controlling the management of the
pot. It is able to monitor the moisture and salt content of the soil. GAIAA [7] is
a solution from sPlant, which is able to manage four plants at a time. It also has
a remote app control, and provides automatic watering, light supplement, and
WiFi communication with a cloud server. Tregren [11] produces 3 different size
products, they can handle 3, 6 or even 12 plants. The watering and the light con-
trol are automatic, usually it can be leaved alone for 21 days. The SmartPot [15]
is just a prototype, but it has temperature, humidity, light, soil moisture sensors
and a water pump. It can be controlled by a mobile application. The Parrot pot
[6] includes a self-watering system and four built-in sensors. Unfortunately, its
production has been suspended. Click & Grow [12] has two similar products,
they only differ in size. The smaller one is for three plants, the bigger one can
handle 9 plants. CitySens [13] is a vertical pot system with auto-watering and
variable pot numbers with an option to communicate with a mobile application
via wifi. LeGrow [14] creates modules for a smart pot system. Currently they sell
lamp, humidifier, power and pot modules. Odyseed [10] is a smart pot solution
that uses time schedules for automatic irrigation and lighting.
There are some other interesting products, like Lua [16] the flowerpot with
15 different animated emotions. The emotions representing the status of the
plant based on the moisture, light and temperature sensors. The Vincross [18]
company has an AI robot called HEXA, and it can move the plant to get enough
sunshine. The TOKQI [17] smart pot can detect if the user pets the plant and
starts playing music. It is a decorative item with RGB lighting and it can be
used as a regular bluetooth speaker too.
For professional usage, there are only very few commercially available plat-
forms for affordable phenotyping (e.g. PhenoBox [19]).
Concerning generic IoT gateways, Kang et al. [20] introduced the main types
and features of IoT gateways in a detailed study, which presents the state-of-
the-art and research directions in this field. This solution is also too generic for
our needs.
Focusing on the development of a smart farming environment, Dagar et al.
[21] proposed a model of a simple smart farming architecture of IoT sensors
capable of collecting information on environmental data and sending them to
a server using wireless connection. There are also generic solutions to monitor
agriculture applications using IoT systems, such as the Kaa IoT Platform [22].
It is a commercial product that is able to perform sensor-based field and remote
crop monitoring. It also has an open source version called the Kaa Community
Edition. Such generic toolkits are quite complex and heavy-weight, so they are
not well suited to specific needs.
In contrast to these solutions, our approach aims to provide a low-cost solu-
tion using the latest IoT and Cloud techniques to enable a robust and scalable
solution to be used for groups of plants with user friendly management.
3 The design of a Smart Pot for the IoLT project
The Internet of Living Things (IoLT) project was started in 2017 with the aims to
integrate IoT technological research with applied biological research, and to de-
velop IoT applications for three target fields: complex plant phenotyping, actig-
raphy for psychosocial treatments, and Lab-on-a-chip systems for microfluidic
diagnostics. IoLT is also forming a Network of Excellence of researchers of cor-
responding disciplines working at the University of Szeged and the Biological
Research Centre of the Hungarian Academy of Sciences. An opensource IoLT
platform is under development to enable the execution of applications on cheap,
Fig. 1. IoLT Smart Pot prototype (left), and one pot of the cluster (right)
low capacity IoT devices providing easy to use programming interfaces based on
Javascript.
In the research field of plant phenotyping we planned to design and develop a
scalable, low-cost automation system called IoLT Smart Pot using IoT and cloud
technologies, to monitor the effect of various stress factors of plants (drought,
nutrition, salt, heavy metals, etc.), as well as behavior of various mutant lines.
For the first prototype depicted in Figure 1, the biologists designed a hardware
for hosting 12 small sized plant pots (for Arabidopsis plants) organized in a 4x3
matrix. To monitor plant growth an RGB camera and a LED-based illumination
system for additional lighting are installed above the plant cluster. The relevant
environmental parameters are light intensity, air and leaf temperature, relative
air and soil humidity, which are monitored by sensors placed above and into the
pots. To govern the monitoring processes, a Raspberry Pi board is placed beside
the cluster. The monitored sensor data is stored locally on the board, and acces-
sible through a wired connection on the same network. The initial configuration
Fig. 2. The architecture of the IoLT Smart Pot Gateway as shown in [28]
for performing periodical monitoring was set to 5 minutes concerning the sensor
readings, and 1 hour to take pictures of the cluster of pots.
3.1 Implementation of the IoLT Smart Pot Gateway
The architecture of our initially proposed IoLT Smart Pot Gateway [28] can be
seen in Figure 2. It has a modular setup, it consists of three microservices, and
its source code can be found on GitHub [27]. The microservices are realized by
Docker containers [25], which are composed together to form the gateway appli-
cation deployable to a virtual machine (VM) of a cloud provider. Special moni-
toring scripts are used to track and log the resource utilization of the containers
for performance measurements. The users can access the gateway through a web
interface provided by a Node.js portal application. It can be used to group and
manage pots and users with projects created by administrators. Projects need
to have start and finish dates, associated users and a short description. Pots can
also be registered by them and linked to projects. In this way, registered and
connected smart pots can send sensor data to them, which can be visualized in
the portal. A sample view of such a portal web interface can be seen in Figure 3.
It displays a project named Real BRC Smartpot test (1 week) managing a smart
pot registered as BRC Smartpot 1. The chart depicts values (y axis) of seven
sensor types with timestamps (X axis) for a week of utilization. On the chart
interface a user can tick or untick certain sensors, and change the time interval
Fig. 3. Historical sensor data visualization in the IoLT Smart Pot Gateway as shown
in [28]
below the chart using a sliding bar. Once a setup is done, the depicted sensor
datasets can be downloaded (in CSV format) by clicking on the ”Download”
button.
The Node.js portal application is built upon two other microservices. In the
middle of the architecture in Figure 2 we can find the Mosquitto MQTT Broker
service, which is built on the open-source Mosquitto tool [23] to store the received
sensor values of the pots using a MongoDB [24] database. The monitored seven
sensor types of a pot are described by a JSON document (see later in Figure 9),
which should be regularly updated and sent in a message by an MQTT client of
a smart pot to the MQTT broker running in this service. The sensor readings
on the Raspberry Pi board are performed by a python script using an MQTT
client package configured with a pot identifier, sensor value sampling frequencies
and picture taking frequencies. The third microservice on the bottom is called
the Apache Web Server, which is responsible to save the pictures of the plants
of the pots. The python scripts of the boards use SFTP file transfers to send the
pictures stored by this service.
3.2 A solutions for monitoring and analyzing plant growth over
time
After the initial version of the gateway portal was released, the biologists started
to use it for monitoring Arabidopsis plants. As mentioned in the previous sec-
tion, the gateway stores regularly updated sensor values, and periodically taken
Fig. 4. Real and segmented pictures of the Smart Pot cluster taken at 2019.01.01
{
Time:"2019-06-01 15:46:30",Slot_1:8.37,Slot_2:11.76,Slot_3:9.57,
Slot_4:11.54,Slot_5:10.73,Slot_6:16.39,Slot_7:23.87,Slot_8:22.39,
Slot_9:1.88,Slot_10:21.33,Slot_11:20.02,Slot_12:20.69
}
Fig. 5. Projected leaf area values to be stored in the database of the gateway
Fig. 6. The gateway screen for querying detailed leaf area values
pictures of the smart pot cluster. The portal can be used to query, visualize and
download a set of sensor values for a certain period, and the created pictures.
Besides viewing these monitored results, the biologists had to perform post-
processing tasks of the monitored data by downloading them from the gateway
portal. One of these tasks is to calculate the growth speed of the plants, which
is generally performed by calculating the projected leaf area visible on the taken
pictures, and filing them to a time series document, later depicting them in
Fig. 7. Selection of a pot from the cluster
Fig. 8. Detailed leaf area values over time for a pot
diagrams. Such a task had to be done manually, taking valuable time from the
researchers.
By responding to their need, we extended the gateway with a new functional-
ity. After a new picture is uploaded to the gateway, a python script is triggered to
perform the segmentation of the picture. The segmented pictures are also stored
in the gateway server in a subfolder to allow verification from the researchers.
After segmentation the projected leaf area is calculated for all 12 plants visible
on the segmented picture (also with image-processing algorithms in a python
script), then saved to the database of the gateway in the format shown in Figure
5 (in cm2).
In order to access the results, a researcher should log in to the portal web
interface of the gateway, and select a registered project with a time interval, as
shown in Figure 6. For the next step, one can select one of the plants (represented
with a slot id) of the smart pot cluster associated to the given project (as depicted
by Figure 7). Finally, as Figure 8 shows, we can see the chart of the calculated
values that represent a time course of the projected leaf area of the selected plant
in a pot of the cluster. The curve nicely reveals a cirkadian oscillation pattern
due to periodic leaf movement (flattening in the dark and erection in the light
period).
{
"Project": "SampleProject",
"Soil-sensor II": "434.437",
"Full light intensity [lux]": 16901.38,
"Time": "2019-01-14 14:02:56",
"Humidity [%]": "41.1",
"Soil-sensor I": "594.940",
"IR light intensity [lux]": 15865.80,
"Temperature [C]": "21.8",
"Visible light intensity [lux]": 1035.58
}
Fig. 9. Sample JSON message of seven sensor values of a pot as shown in [28]
Fig. 10. CPU and memory usage measurement results for 250 pots
Fig. 11. Network and I/O usage measurement results for 250 pots
4 Evaluation of the Smart Pot Gateway
In order to evaluate our proposed solution, we instantiated an IoLT Smart Pot
Gateway service in the MTA Cloud [26] with a small VM flavor with a single
virtual CPU core and two GB memory. The MTA Cloud is an OpenStack-based,
national community cloud financed by the Hungarian Academy of Sciences for
providing cloud infrastructure services for scientists from the academy.
4.1 Simulations with a python tool
First, we performed a throughout evaluation by means of simulation. After exe-
cuting some initial measurements, we found out the exact, real data value ranges
for the installed sensors of the smart pot. Based on these values, we designed a
simulated smart pot represented by python scripts capable of sending generated
sensor data via the MQTT protocol. Figure 9 depicts a generated sample JSON
file for the revealed sensor types.
First, we created 250 simulated pots with scripts that sent generated sensor
data to our IoLT Smart Pot Gateway service (runing at MTA Cloud) for 30
minutes. We divided the total experiment time-frame to the following periods:
in the first 10 minutes we applied sensor data generation frequency of 30
seconds (which means that each pot sent a message of 7 sensor values every
30 seconds);
in the second 10 minutes we applied sensor data generation frequency of 10
seconds;
in the following 5 minutes we applied sensor data generation frequency of 2
seconds;
and in the last 5 minutes we applied sensor data generation frequency of 10
seconds, again.
Fig. 12. CPU and memory usage measurement results for 50 pot clusters
We also developed a special monitoring script for the gateway (as shown
in Figure 2) to track its resource consumption. The resource usage sampling
of the script was set to 10 seconds. They queried CPU, memory, network and
input/output (I/O) resource utilization for all containers, and we summed these
values to get the total resource consumption of the composed service (running
in a VM). We can see the measurement results for this initial round simulating
250 pots in Figure 10 and Figure 11. The x axis denotes the timestamps of
resource usage monitoring, while the y axis denotes the resource usage values
(in percentage or in kB or MB). We can see that there are some spikes in the
resource usage percentages after the first 10 minutes, when we start to send
more messages, and from the 20th minute the utilization has an increasing trend.
Nevertheless, we have to mention that the resource using sampling is less frequent
than the arrival rate of the messages, which results in an incomplete curve (the
resource utilization is not tracked between the sampling intervals). The network
and I/O utilization was visible only for the first 10 minutes, possibly due to the
initialization phase of the script.
Next, we set the simulation parameters in a way to mimic future, real world
utilization. Our proposed IoLT Smart Pot is basically a cluster of 12 pots, as
shown in Figure 1. To evaluate the scalability of our gateway solution, we per-
formed three simulation measurements with 50, 100 and 250 clusters (composed
of 600, 1200 and 3000 pots respectively). In all cases we performed the measure-
ments for 30 minutes, and the simulated smart pot platform sent sensor values
with the following setup:
in the first 10 minutes we applied sensor data generation frequency of 5
minutes (which means that each pot sent a message of 7 sensor values every
5 minutes: resulting 2 messages in this period per pot);
in the second 10 minutes we applied sensor data generation frequency of 1
minute;
Fig. 13. Network and I/O usage measurement results for 50 pot clusters
and in the last 10 minutes we applied sensor data generation frequency of 5
minutes, again.
Table 2. Comparison of the four evaluation rounds
No. of pots 250 600 1200 3000
CPU AVG 6.22 1.19 7.06 8.65
CPU MAX 26.64 13.42 30.91 39.29
MEM AVG 14.28 17.79 15.55 21.72
MEM MAX 16.14 18.94 15.95 24.73
In the first simulation for 50 clusters we set the sampling of resource usage
(processor and memory usage) in every 10 seconds, while for the second and
the third one (100 and 250 clusters) we set it to 2 seconds (to have a better
resolution of resource loads).
We can see the measurement results for the first round simulating 50 clusters
with 600 pots in Figure 12 and Figure 13 for 30 minutes. Here we can see
that the average CPU load varies between 1 and 2 percent, and the memory
usage fluctuates between 15 and 19 percent. The network and I/O utilization
are slowly, but constantly growing. In this experiment we also observed that the
time of an actual data processing (receiving a message and writing its contents
to the database) and the time of the resource usage sampling are rarely matched.
One matching example can be seen right after the 3rd minute in Figure 12, which
shows a spike with almost 14 percent of CPU utilization.
For the second round we doubled the number of clusters to 100, and per-
formed the simulation only for 5 minutes with detailed resource usage sampling
Fig. 14. CPU and memory usage measurement results for 100 pot clusters
Table 3. Comparison of the 30 min. evaluation rounds
No. of pots 600 3000
CPU AVG 0:00 - 0:10 1.37 6.65
0:10 - 0:20 1.09 14.11
0:20 - 0:30 1.11 5.01
CPU MAX 0:00 - 0:10 13.42 39.29
0:10 - 0:20 1.25 36.43
0:20 - 0:30 1.27 31.78
MEM AVG 0:00 - 0:10 17.44 22.67
0:10 - 0:20 17.88 22.60
0:20 - 0:30 18.05 19.81
MEM MAX 0:00 - 0:10 18.94 24.47
0:10 - 0:20 18.00 24.73
0:20 - 0:30 18.11 20.74
of 2 seconds. We can see the measurement results for this round simulating 100
clusters with 1200 pots. In Figure 14 we can see that the results reveal a peri-
odic resource usage fluctuation denoting the data processing activities. For the
network and I/O utilization shown in Figure 15 we can still see a constant grow.
Finally, for the largest experiment we further increased the number of pot
clusters to 250 arriving to a total number of 3000 simulated pots. For this third
round, we performed the simulation for 30 minutes, again, with the same periods
as defined for the first round (of 50 clusters). We can see the measurement results
in Figure 16 and Figure 17. If we take a look at the middle 10 minutes period
we can see the periodic resource usage spikes for CPU and memory, as in the
previous round. And we can also observe the utilization growth in network and
I/O data transfers.
Fig. 15. Network and I/O usage measurement results for 100 pot clusters
To summarize our investigations, Table 2 compares the average and maxi-
mum resource utilization values measured during the experiments. We can see
that by increasing the number of pots to be managed by the gateway service, the
utilization also increases. As expected, the CPU utilization was the highest in
the third round for managing 3000 pots at the same time with almost 40 percent.
The memory utilization is also the highest in this case with almost 25 percent.
Table 3 shows a detailed comparison for the longest experiments denoting the
different phases of the measurements. This table highlights that the CPU uti-
lization generally reaches its maximum in the first phase, then it generally drops,
while memory utilization shows a quite balanced load all over the three phases.
Finally, we can state that these results prove that we can easily serve numerous
phenotyping projects monitoring up to thousands of pots with a single gateway
instance in a Cloud.
5 Real world measurements
We have seen in the previous section that our gateway service is scalable enough
to manage a few thousands of pots in a single cloud VM. To exemplify real
world utilization, we connected the gateway to the IoLT Smart Pot prototype.
It is able to hold 12 Arabidopsis plants in small pots organized to a cluster
(as shown in Figure 1). We configured the python scripts of a Raspberry Pi
board placed beside the pot cluster to perform sensor readings periodically, and
send the environmental values to the IoT-Cloud gateway service. The wiring of
the smart pot cluster formed a single IoT device with a camera and 7 sensors
(attached to some of the 12 pots).
We performed the monitoring of the growth of Arabidopsis plants under
standard greenhouse conditions for several periods, taking up around 2-3 months
Fig. 16. CPU and memory usage measurement results for 250 pot clusters
Fig. 17. Network and I/O usage measurement results for 250 pot clusters
in total. RGB image taking was performed every hour, and the sensor sampling
frequency was set to 5 minutes (to generate a JSON message). Figure 18 depicts
a query at the gateway portal resulting in a chart of the sensor and leaf area
values for over a month of monitoring. As we can see from the chart, the smart
pot was disconnected for a certain period after around 20 days). If we zoom in
by using the bar below the chart, we can view detailed results. Figure 19 shows
the sensor values of a day of utilization of the smart pot cluster.
6 Conclusions
Smart farming approaches are meant to revolutionize agriculture to improve pro-
ductivity and reduce waste by exploiting the latest ICT technologies and trends.
Fig. 18. Data visualization of a real world measurement in the IoLT Smart Pot Gate-
way
Affordable phenotyping has the goal to provide low cost and easily scalable so-
lutions to create greenhouses of the future.
In this paper we aimed to contribute to this field by proposing the IoLT
Smart Pot Platform and Gateway that can be used to manage smart pot clusters
by monitoring environmental parameters. This solution is capable of collecting,
storing and visualizing sensor data, as well as performing leaf area calculations
with image processing to allow plant growth analysis. We also evaluated the pro-
posed solution with scalable simulations, and exemplified real world utilization
in standard greenhouse conditions.
In our future work we plan to redesign the smart pot with a solar cell system
to enable portable installations and remote monitoring at outdoor location.
Software availability
Its source code of the proposed cloud gateway is open and available at the
following website:
https://github.com/sed-inf-u-szeged/IoLT-Smart-Pot-Gateway
References
1. N. Jones, Top Strategic IoT Trends and Technologies Through 2023, Gartner report.
Online: https://www.gartner.com/en/documents/3890506, September 2018.
2. Beecham Research. 2017. Smart Farming. Smart Farm-
ing Sales Brochure, http://www.beechamresearch.com/files/Bee-
cham%20Research%20%C2%BB%20Smart%20Farming%20(sales)%202017%20.pdf
Fig. 19. Sensor data of a day in the IoLT Smart Pot Gateway
3. A. Botta, W. De Donato, V. Persico, and A. Pescap´e. Integration of cloud computing
and internet of things: a survey. Future Generation Computer Systems, 56, pp.684-
700, 2016.
4. M. Reynolds, U. Schurr, The 4th International Plant Phenotyping Symposium.
Plant Science, Vol. 282, Page 1, 2019.
5. P. Sharma. 2018. Xiaomi Smart Flower Pot Flora Review,
https://www.xiaomitoday.com/xiaomi-smart-flower-pot-flora-review/, June, 2019.
6. Parrot Pot, https://www.parrot.com/global/connected-garden/parrot-pot, June,
2019.
7. GAIAA sPlant smart pot, http://www.splant.com.cn/en/index.php?m=Cpzs&a=-
show&id=14, June, 2019.
8. PlantRay, https://www.plantray.com, June, 2019.
9. AeroGarden, https://www.aerogarden.com, June, 2019.
10. Odyseed, https://www.odyseed.com, June, 2019.
11. Tregren, https://www.tregren.com/, June, 2019.
12. Click & Grow, https://www.clickandgrow.com, June, 2019.
13. CitySens, https://www.citysens.com, June, 2019.
14. LeGrow, https://www.legrow.co/, June, 2019.
15. SmartPot, https://www.smart-pot.net/, June, 2019.
16. Lua, https://www.indiegogo.com/projects/lua-the-smart-planter-with-feelings/,
June, 2019.
17. TOKQI, https://www.tokqismartflowerpot.com, June, 2019.
18. Vincross HEXA, https://mymodernmet.com/little-robot-smart-planter/, June,
2019.
19. Czedik-Eysenberg, A., Seitner, S., G¨uldener, U., Koemeda, S., Jez, J., Colombini,
M. and Djamei, A. 2018. The ’PhenoBox’, a flexible, automated, open-source plant
phenotyping solution. New Phytol, 219: 808-823.
20. B. Kang, D. Kim, H. Choo. 2017. Internet of Everything: A Large-Scale Autonomic
IoT Gateway. IEEE Transactions on Multi-Scale Computing Systems, vol. 3, no. 3,
pp. 206–214.
21. R. Dagar, S. Som and S. K. Khatri. 2018. Smart Farming – IoT in Agriculture. In-
ternational Conference on Inventive Research in Computing Applications (ICIRCA),
Coimbatore, India, pp. 1052-1056.
22. Kaa project website, https://www.kaaproject.org/documentation/, January, 2019.
23. Mosquitto website, https://mosquitto.org/, June, 2019.
24. MongoDB website, https://www.mongodb.com/what-is-mongodb, June, 2019.
25. Docker Container Environment, https://www.docker.com/, June, 2019.
26. MTA Cloud service, https://sztaki.cloud.mta.hu, June, 2019.
27. IoLT Smart Pot Gateway on GitHub. Online: https://github.com/sed-inf-u-
szeged/IoLT-Smart-Pot-Gateway, June, 2019.
28. Hadabas, J.; Hovari, M.; Vass, I. and Kertesz, A. 2009. IoLT Smart Pot: An IoT-
Cloud Solution for Monitoring Plant Growth in Greenhouses. In Proceedings of the
9th International Conference on Cloud Computing and Services Science - Volume 1:
CLOSER, pages 144–152.
... The BRC is also involved in the development of affordable phenotyping solutions, which can lower the significant price barrier, which currently stands as an obstacle to the wider application of phenotyping approaches. In collaboration with informaticians of the Szeged University they have developed a cloud-based 'smart pot' system that provides a low-cost solution for monitoring plant growth in greenhouse conditions (Pflanzner et al., 2020). In addition to plant phenotyping, they also developed phenotyping methods for microalgae by using precisely designed and operated photobioreactor systems with multiple sensors of physicochemical parameters (dissolved oxygen, pH, and optical density) and gas control, allowing real-time monitoring of the physiological changes and net photosynthesis under Ci limitation (Patil et al., 2020). ...
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