A of low-cost technology alternatives to address

A major scientific focus or target of achievement in commercial
development of any system is to optimize result efficiency with less input
(resources) and thus diversity of options exist today to fit this aim. (Majur, 2011)
said “the nice thing about standards is that there is so many to choose from”.
This study has to an extent highlighted the usefulness of low-cost technology
alternatives to address the issues related to cost, reduce the intensive on-site
presence of labourers, and to facilitate the automation of control systems in
hydroponics environs remotely. To decide on a more appropriate technology for
this project, it is important to review similar studies first.


In 2008, Nachidi et al proposed
and described the implementation of a discrete timed-system to control the temperature
and humidity concentration in greenhouses by means of simultaneous Ventilation
and heating systems using Takagi-Sugeno (T-S) fuzzy models and the Parallel
Distributed Compensation (PDC) approach. They demonstrated how robust
fuzzy controller really achieved the desired climate conditions in a greenhouse
and by using this T-S fuzzy model, the stability analysis and control design
problems can be reduced to sufficient conditions expressed as Linear Matrix
Inequalities (LMIs) (Guerbaoui,
Ed-Dahhak, Elafou, Lachhab, Belkoura and Bouchikhi, 2013: 1-13). The impediment of the system is that fuzzy models and PDC
concepts are complex and require a high level of knowledge and understanding.

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In the work of Sumit, Mohit,
Aswani, and Praneet (2012), they built an embedded system that closely
monitors the microclimatic parameters of a greenhouse in real-time and
activates actuators when their set safe thresholds are exceeded. The actuators
were responsible of restoring optimum conditions within the close environment.
Their design employs a Liquid Crystal Display (LCD) directly interfaced to a
microcontroller to ensure that the user ceaselessly visualize the readings of
the system and caution about the climatic state within the greenhouse. They
used a PC as their base monitoring station. Vividly, with such a system, users are
unaware of changes unless they are physically present at the location. Their
system is wired and thus does not include a wireless capability to remotely
update the users when absent.


(Rahali, Guerbaoui, Ed-dahhak, El Afou,
Tannouche, Lachhab and Bouchikhi, 2011) built up a framework that depends on
the Global System for Mobile Communication (GSM) which enables a greenhouse
user to screen and control the microclimatic parameters by means of Short
Message Service (SMS). It robotizes alarms, specifically dribble water system
stations, by sending SMS messages to the end client of the framework. The
framework utilizes a SIM card for the getting and sending of messages. A
LabVIEW graphical User Interface (GUI) programming is utilized for the
procurement of information. Observing is done from a PC and the storing of all data
by means of a PCL812PG card was produced. The framework’s GUI is exceptionally
solid, adaptable to utilize, very intuitive, easy to understand and can process
the entire information progressively. The proposed framework introduced a few points
of interest: ease of use, a focussed checking of parameters, the utilization of
GSM telephones due to their accessibility, exploited the minimal effort of SMS
in Morocco, and system scope. The principle difficulty of their framework is
that it doesn’t consolidate a programmed control activity; it requires
introduction by the client who may not generally be mindful and this could
represent a danger to the harvests. The decision of a PC for capacity
additionally includes weight, size and cost to the overall framework.


Deore and Umale (2012) have given
emphasis on a Wireless Sensor Network (WSN) approach of greenhouse monitoring
and control. They developed and tested a control system using recent ATmega
microcontrollers. Here, ATmega microcontrollers are preferred over other
microcontrollers because of some distinctive features such as a 10bit Analog to
Digital Converter (ADC), sleep mode, wide input voltage range and higher memory
capacity. The design system considered optimization and functional improvement
of the system. The system shows several advantages in term of its compact size,
low cost and high accuracy.

(Jindarat and Wuttidittachotti, 2015) in their study, investigated the establishment of an
Intelligent System which employed the use of an Embedded System and smartphone
for poultry farming administration using Raspberry Pi Model B and Arduino Uno.
They found that the system could
monitor encompassing climatic conditions like temperature, air quality, and the
channeling fan’s switch control within the farm. The farmers in their study found
the system to be comfortable, easy to use and facilitated the interaction with
the system any time and place remotely, hence, acknowledging reduction in cost,
resource sparing, and profitable management for their poultry farming. The
hitches about the system is that it was slow. It needed an external Wi-Fi
module and a separate wireless communication board to facilitate the remote
controlling of the system by the farmers. For this reason, they
recommended the use of a newer series of Raspberry Pi Model B to expand
functionality of the system. Raspberry Pi 3 Model B has an on-board Wi-Fi chip
which eliminates the need for an external Wi-Fi module.


Kurniawan (2017) examined the mechanization of a
cultivating framework utilizing Arduino and Raspberry Pi 2 to screen
temperature and damply which had colossal effect on the solid or poor
development of his plants. He utilized a Proportional-basic subordinate (PID -the most common
control algorithm used in the industry and has been universally accepted in
industrial control) controller to manage all inputs from his sensors, and in
decision system. He quantified soil dampness, temperature, and humidity as parameters
for his system and controlled the water system in view of the information he
gathered from his gadgets. He concluded that the Raspberry Pi framework was
more productive and exact in his examination than the Arduino.


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