ISSN:2229-6093.
Demonstrate a critical awareness of previous research in an IT context within a chosen topic area through a basic understanding of research theory and techniques.
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Added on 2022-10-01
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ISSN:2229-6093.
Demonstrate a critical awareness of previous research in an IT context within a chosen topic area through a basic understanding of research theory and techniques.
Added on 2022-10-01
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Multidisciplinary Model for Smart Agriculture using Internet-of-Things
(IoT), Sensors, Cloud-Computing, Mobile-Computing & Big-Data Analysis
Hemlata Channe1, Sukhesh Kothari2 , Dipali Kadam3
Assistant Professors, Department of CE, PICT, Pune, India.
hemlata.channe@gmail.com1,sakothari@pict.edu2, ddkadam@pict.edu3
Abstract
Although precision agriculture has been
adopted in few countries; the agriculture industry
in India still needs to be modernized with the
involvement of technologies for better production,
distribution and cost control.
In this paper we proposed a
multidisciplinary model for smart agriculture
based on the key technologies: Internet-of-Things
(IoT), Sensors, Cloud-Computing, Mobile-
Computing, Big-Data analysis. Farmers, Agro-
Marketing agencies and Agro-Vendors need to be
registered to the AgroCloud module through
MobileApp module. AgroCloud storage is used to
store the details of farmers, periodic soil properties
of farmlands, agro-vendors and agro-marketing
agencies, Agro e-governance schemes and current
environmental conditions. Soil and environment
properties are sensed and periodically sent to
AgroCloud through IoT (Beagle Black Bone).
Bigdata analysis on AgroCloud data is done for
fertilizer requirements, best crop sequences
analysis, total production, and current stock and
market requirements. Proposed model is beneficial
for increase in agricultural production and for cost
control of Agro-products.
Keywords: Internet-of-Things, Cloud
Computing, Big-Data Analysis, Mobile
Computing, Sensor, Smart Agriculture
1. Introduction
Internet-of-Things and Big-Data analysis
are recent technologies from last few years and
applications are being developed in various
domains using these as key technologies. Sensor
technology has also been advanced and many types
of sensors like environmental sensors, gas sensors
are developed and used in applications as per the
need. Cloud-Computing and Mobile-Computing
are mature technologies and applications exists in
almost every field using those technologies. Uses
of these technologies in the field of agriculture are
also introduced and are used for improvement in
this sector.
1.1 Internet of Things (IoT), Wireless
Sensor Networks and Sensors
Internet of Things is a technology which
tends to connects all the objects in the world to the
Internet. It involves the use of RFID, wireless and
other sensors with Internet stack inbuilt into the
device. Applications are developed based on IoT
enabled devices for monitoring and control in
various domains including industrial processes,
home appliances, health monitoring applications,
smart homes, smart cities [4,5,11,16,30,33]. In
agriculture domain few researchers have proposed
architectures based on IoT to monitor supply chain
management of agricultural products [6,7].Hemlata Channe et al, Int.J.Computer Technology & Applications,Vol 6 (3),374-382IJCTA | May-June 2015
Available online@www.ijcta.com374ISSN:2229-6093
(IoT), Sensors, Cloud-Computing, Mobile-Computing & Big-Data Analysis
Hemlata Channe1, Sukhesh Kothari2 , Dipali Kadam3
Assistant Professors, Department of CE, PICT, Pune, India.
hemlata.channe@gmail.com1,sakothari@pict.edu2, ddkadam@pict.edu3
Abstract
Although precision agriculture has been
adopted in few countries; the agriculture industry
in India still needs to be modernized with the
involvement of technologies for better production,
distribution and cost control.
In this paper we proposed a
multidisciplinary model for smart agriculture
based on the key technologies: Internet-of-Things
(IoT), Sensors, Cloud-Computing, Mobile-
Computing, Big-Data analysis. Farmers, Agro-
Marketing agencies and Agro-Vendors need to be
registered to the AgroCloud module through
MobileApp module. AgroCloud storage is used to
store the details of farmers, periodic soil properties
of farmlands, agro-vendors and agro-marketing
agencies, Agro e-governance schemes and current
environmental conditions. Soil and environment
properties are sensed and periodically sent to
AgroCloud through IoT (Beagle Black Bone).
Bigdata analysis on AgroCloud data is done for
fertilizer requirements, best crop sequences
analysis, total production, and current stock and
market requirements. Proposed model is beneficial
for increase in agricultural production and for cost
control of Agro-products.
Keywords: Internet-of-Things, Cloud
Computing, Big-Data Analysis, Mobile
Computing, Sensor, Smart Agriculture
1. Introduction
Internet-of-Things and Big-Data analysis
are recent technologies from last few years and
applications are being developed in various
domains using these as key technologies. Sensor
technology has also been advanced and many types
of sensors like environmental sensors, gas sensors
are developed and used in applications as per the
need. Cloud-Computing and Mobile-Computing
are mature technologies and applications exists in
almost every field using those technologies. Uses
of these technologies in the field of agriculture are
also introduced and are used for improvement in
this sector.
1.1 Internet of Things (IoT), Wireless
Sensor Networks and Sensors
Internet of Things is a technology which
tends to connects all the objects in the world to the
Internet. It involves the use of RFID, wireless and
other sensors with Internet stack inbuilt into the
device. Applications are developed based on IoT
enabled devices for monitoring and control in
various domains including industrial processes,
home appliances, health monitoring applications,
smart homes, smart cities [4,5,11,16,30,33]. In
agriculture domain few researchers have proposed
architectures based on IoT to monitor supply chain
management of agricultural products [6,7].Hemlata Channe et al, Int.J.Computer Technology & Applications,Vol 6 (3),374-382IJCTA | May-June 2015
Available online@www.ijcta.com374ISSN:2229-6093
Wireless Sensor Networks is said to be mature
technology and lot of work has been done for
agriculture domain [35, 44]. Sensors are available
for sensing and analysing the various parameters
that are required in agriculture domain. Many
applications are in use which utilizes sensors in
agriculture [13,15,20,21,22,23,24,25,26,27,28,29,
35,49]. WSN architectures were proposed,
implemented and tested for monitoring the soil
properties.
1.2 Mobile Computing
Mobile computing has affected lots in
number in our day to day life due to its availability
and has a cheaper cost of communication. It is in
use in almost every field including agriculture
sector. System based on mobile computing has
been proposed for sending daily, seasonal messages
to farmers regarding the product information and
weather information in [53].
1.3 Big-data and Big-Data Analytics
Big-data is a massive amount of data
collected from different sources and for longer
period like sensor data, social networking data, and
business data. The major challenge is capture,
storage, analysis, search [54]. It is in use for
business data processing along with big-data
analytics to search for hidden patterns in the data.
Big-data in agriculture domain [10] is used for
supply chain management of agro products, to
minimize the production cost.
1.4 Data Mining, Analysis and Knowledge
Building
Data mining is process of analysing data
to find some patterns hidden in the data. Data
mining for agriculture sector have been the topic of
research for many years [9, 38, 39, 41, 42, 45, 46,
47, and 48]. Data mining have been used for
analysing the soil types and properties to classify
them. Also soil data mining is useful for crop
prediction and deciding the better crop sequence
based on previous crop sequences in the same
farmland with the current soil nutrient information.
1.5 Cloud Computing
Cloud computing provides sharing of resources
with cheap cost [54]. Cloud computing service
provider offers services like Infrastructure as a
Service (IaaS), Platform as a Service (PaaS), and
Software as a Service (SaaS) with cheap cost.
Cloud computing has been used for storage of
agriculture data [8, 37]. It has been used in
agriculture sector along with IoT [2, 3].
1.6 Agriculture Industry in India:
Agriculture is the major source of income
for the largest population in India and is major
contributor to Indian economy. However
technological involvement and its usability still
have to be grown and cultivated for agro sector in
India. Although few initiatives have also been
taken by the Indian Government for providing
online and mobile messaging services to farmers
related to agricultural queries, agro vendor’s
information to farmers [53], it provides static data
related to soil quality at each region. The system
which utilizes real time data of soil quality based
on its current properties for decision making has
not been implemented. Soil properties determine
the quality of soil. The soil pH value and amount of
properties like Nitrate, Phosphate and Potassium in
the soil is an important factor which determines the
soil quality and type of crop production. Real time
monitoring of these properties helps to maintain
soil health intact by applying only required amount
of fertilizers. Soil moisture analysis helps to apply
the water whenever necessary avoiding wastage of
water. Also environmental conditions such as
temperature and moisture also affect the crop
production and crop diseases. In this respect we
need a dynamic model which collects such real
time data. In support to this; all agriculture entities
need to be connected to have decision making
system to increase the production and ease the
distribution of agricultural products from farmers to
marketing agencies and from vendors to farmers.
Such system will also be responsible for controlling
other parameters like agro product rates.
Smart mobile phones are available now
days to many users including in the rural areas.
Beagle black bone is a cheap IoT device which can
be interfaced to soil and environmental sensors to
collect soil properties and current environmental
conditions. This motivates to develop a cost
effective and portable sensor kit for sensing the soil
properties for current requirements of fertilizers.
The soil data from farmlands needs to be collected
through sensor kit and sent to AgroCloud storage
for further processing. The collected big-data thenHemlata Channe et al, Int.J.Computer Technology & Applications,Vol 6 (3),374-382IJCTA | May-June 2015
Available online@www.ijcta.com375ISSN:2229-6093
technology and lot of work has been done for
agriculture domain [35, 44]. Sensors are available
for sensing and analysing the various parameters
that are required in agriculture domain. Many
applications are in use which utilizes sensors in
agriculture [13,15,20,21,22,23,24,25,26,27,28,29,
35,49]. WSN architectures were proposed,
implemented and tested for monitoring the soil
properties.
1.2 Mobile Computing
Mobile computing has affected lots in
number in our day to day life due to its availability
and has a cheaper cost of communication. It is in
use in almost every field including agriculture
sector. System based on mobile computing has
been proposed for sending daily, seasonal messages
to farmers regarding the product information and
weather information in [53].
1.3 Big-data and Big-Data Analytics
Big-data is a massive amount of data
collected from different sources and for longer
period like sensor data, social networking data, and
business data. The major challenge is capture,
storage, analysis, search [54]. It is in use for
business data processing along with big-data
analytics to search for hidden patterns in the data.
Big-data in agriculture domain [10] is used for
supply chain management of agro products, to
minimize the production cost.
1.4 Data Mining, Analysis and Knowledge
Building
Data mining is process of analysing data
to find some patterns hidden in the data. Data
mining for agriculture sector have been the topic of
research for many years [9, 38, 39, 41, 42, 45, 46,
47, and 48]. Data mining have been used for
analysing the soil types and properties to classify
them. Also soil data mining is useful for crop
prediction and deciding the better crop sequence
based on previous crop sequences in the same
farmland with the current soil nutrient information.
1.5 Cloud Computing
Cloud computing provides sharing of resources
with cheap cost [54]. Cloud computing service
provider offers services like Infrastructure as a
Service (IaaS), Platform as a Service (PaaS), and
Software as a Service (SaaS) with cheap cost.
Cloud computing has been used for storage of
agriculture data [8, 37]. It has been used in
agriculture sector along with IoT [2, 3].
1.6 Agriculture Industry in India:
Agriculture is the major source of income
for the largest population in India and is major
contributor to Indian economy. However
technological involvement and its usability still
have to be grown and cultivated for agro sector in
India. Although few initiatives have also been
taken by the Indian Government for providing
online and mobile messaging services to farmers
related to agricultural queries, agro vendor’s
information to farmers [53], it provides static data
related to soil quality at each region. The system
which utilizes real time data of soil quality based
on its current properties for decision making has
not been implemented. Soil properties determine
the quality of soil. The soil pH value and amount of
properties like Nitrate, Phosphate and Potassium in
the soil is an important factor which determines the
soil quality and type of crop production. Real time
monitoring of these properties helps to maintain
soil health intact by applying only required amount
of fertilizers. Soil moisture analysis helps to apply
the water whenever necessary avoiding wastage of
water. Also environmental conditions such as
temperature and moisture also affect the crop
production and crop diseases. In this respect we
need a dynamic model which collects such real
time data. In support to this; all agriculture entities
need to be connected to have decision making
system to increase the production and ease the
distribution of agricultural products from farmers to
marketing agencies and from vendors to farmers.
Such system will also be responsible for controlling
other parameters like agro product rates.
Smart mobile phones are available now
days to many users including in the rural areas.
Beagle black bone is a cheap IoT device which can
be interfaced to soil and environmental sensors to
collect soil properties and current environmental
conditions. This motivates to develop a cost
effective and portable sensor kit for sensing the soil
properties for current requirements of fertilizers.
The soil data from farmlands needs to be collected
through sensor kit and sent to AgroCloud storage
for further processing. The collected big-data thenHemlata Channe et al, Int.J.Computer Technology & Applications,Vol 6 (3),374-382IJCTA | May-June 2015
Available online@www.ijcta.com375ISSN:2229-6093
can be analysed for the required actions for
production.
2. Related Work
Researchers have proposed different
models for agriculture sector with one or multiple
technologies mentioned above.
Use of IoT has been proposed in
agriculture domain in [1, 6, and 7]. In [1] authors
have described FMS architecture which utilizes
Future Internet characteristics. The farmers will get
Easy access to information and advice through this
architecture. In [6, 7] IoT has been used for product
supply chain business process. In [2, 3] IoT and
Cloud computing have been used for agriculture
sector. In [2] authors have explained this in the
context of service providers and supply chain for
cost effective services for farmers. In [3] authors
have described controlled architecture of smart
agriculture based on IoT and Cloud Computing.
Use of cloud computing for agriculture sector for
storing details of agriculture information has been
explained in [37]. Cloud storage stores work
history information, fertilizers distribution,
cultivation images through camera and
environment information collected through sensors,
collection and recording information. Authors have
analysed the collected data for correlation between
environment, work and yield for standard work
model construction. Monitoring for adverse signs
and fault detection. In [8] authors have used image
processing on crop images for crop disease
detection and image data is stored on the Cloud.
In [9] an approach is proposed based on
artificial neural networks to predict crop yield by
sensing soil properties and atmospheric parameters.
Big-data technology in agriculture domain and how
it will affect the cost reduction and benefits is
explained in [10]. Challenges in agriculture sector
and remote sensing applications are discussed in
[13] which include crop estimation and cropland
mapping. In [15] authors have designed and
implemented a wsn based on soil temperature,
humidity monitoring system for agriculture using
ZigBee and GPS technologies for the operation. In
[24] authors have proposed development of rice
cropping monitoring system for real time
monitoring to increase rice production. This system
makes use motes with external sensors for leaf
wetness, soil moisture, soil pH, atmospheric
pressure sensors attached to it. PH values are sent
to the farmer from base station via GSM modem in
the SMS form. Using the pH values farmer can
decide the amount of fertilizers to be used. IoT with
data mining is discussed in [32], the data generated
from IoT and applying various data mining
techniques on this data. Authors have also
discussed changes required for data mining in IoT
perspective along with issues and future trends.
WSN based greenhouse environment
monitoring system is explained in [35] which
makes use of temperature, humidity, CO2 and light
detection modules. This combined wsn technology
and greenhouse control technology provides
automatic adjustment of greenhouse.
Bigdata applications in data mining are
explained in [36]. In [38] authors have surveyed
data mining techniques to find most effective
techniques to extract new knowledge and
information from existing soil profile data
contained within soil data set. They have described
data-mining techniques suitable for different
prediction in agriculture. Crop yield estimation
using existing data through data mining is proposed
in [39]. For this they have utilized four attributes
namely year, rainfall, area of sowing and
production. In [41] authors have analysed data
mining algorithms to predict crop yield with more
accuracy and generality using existing data. E-
agriculture information system for farmers to
provide information of current schemes for
agribusiness and information regarding the
plantation is proposed in [43]. In [44] authors have
reviewed WSN technology and applications in
agriculture domain. Authors have also discussed
existing frameworks in agriculture domain. The
application of WEKA-based data mining and
analysis model is discussed in [45]. Authors have
discussed use of machine learning algorithms
through a case study in agricultural domain for
mushroom grading process. In [47] authors have
explained the use of spatial data mining in
agricultural domain. They have used K-means
algorithm along with optimization method
progressive refinement for spatial association
analysis. Temperature and rainfall is given as initial
spatial data and analysing it for the improving the
crop yield and to reduce the crop losses.
Although researchers have proposed few
models in agriculture domain using one or more of
the technologies mentioned; the dynamic model is
needed that provides an integrated approach to:
1. Monitor various soil properties from each
farmland and environmental conditions periodically
through portable cost effective IoT device and
usable by multiple users, enquires about crop
production details to the farmers after crop
harvesting and stores these details at the central
place as in the cloud storage. This in result
producing Big-data over the time and will be
analysed for fertilizer requirements for current
crop, mapping of crop production to soil propertiesHemlata Channe et al, Int.J.Computer Technology & Applications,Vol 6 (3),374-382IJCTA | May-June 2015
Available online@www.ijcta.com376ISSN:2229-6093
production.
2. Related Work
Researchers have proposed different
models for agriculture sector with one or multiple
technologies mentioned above.
Use of IoT has been proposed in
agriculture domain in [1, 6, and 7]. In [1] authors
have described FMS architecture which utilizes
Future Internet characteristics. The farmers will get
Easy access to information and advice through this
architecture. In [6, 7] IoT has been used for product
supply chain business process. In [2, 3] IoT and
Cloud computing have been used for agriculture
sector. In [2] authors have explained this in the
context of service providers and supply chain for
cost effective services for farmers. In [3] authors
have described controlled architecture of smart
agriculture based on IoT and Cloud Computing.
Use of cloud computing for agriculture sector for
storing details of agriculture information has been
explained in [37]. Cloud storage stores work
history information, fertilizers distribution,
cultivation images through camera and
environment information collected through sensors,
collection and recording information. Authors have
analysed the collected data for correlation between
environment, work and yield for standard work
model construction. Monitoring for adverse signs
and fault detection. In [8] authors have used image
processing on crop images for crop disease
detection and image data is stored on the Cloud.
In [9] an approach is proposed based on
artificial neural networks to predict crop yield by
sensing soil properties and atmospheric parameters.
Big-data technology in agriculture domain and how
it will affect the cost reduction and benefits is
explained in [10]. Challenges in agriculture sector
and remote sensing applications are discussed in
[13] which include crop estimation and cropland
mapping. In [15] authors have designed and
implemented a wsn based on soil temperature,
humidity monitoring system for agriculture using
ZigBee and GPS technologies for the operation. In
[24] authors have proposed development of rice
cropping monitoring system for real time
monitoring to increase rice production. This system
makes use motes with external sensors for leaf
wetness, soil moisture, soil pH, atmospheric
pressure sensors attached to it. PH values are sent
to the farmer from base station via GSM modem in
the SMS form. Using the pH values farmer can
decide the amount of fertilizers to be used. IoT with
data mining is discussed in [32], the data generated
from IoT and applying various data mining
techniques on this data. Authors have also
discussed changes required for data mining in IoT
perspective along with issues and future trends.
WSN based greenhouse environment
monitoring system is explained in [35] which
makes use of temperature, humidity, CO2 and light
detection modules. This combined wsn technology
and greenhouse control technology provides
automatic adjustment of greenhouse.
Bigdata applications in data mining are
explained in [36]. In [38] authors have surveyed
data mining techniques to find most effective
techniques to extract new knowledge and
information from existing soil profile data
contained within soil data set. They have described
data-mining techniques suitable for different
prediction in agriculture. Crop yield estimation
using existing data through data mining is proposed
in [39]. For this they have utilized four attributes
namely year, rainfall, area of sowing and
production. In [41] authors have analysed data
mining algorithms to predict crop yield with more
accuracy and generality using existing data. E-
agriculture information system for farmers to
provide information of current schemes for
agribusiness and information regarding the
plantation is proposed in [43]. In [44] authors have
reviewed WSN technology and applications in
agriculture domain. Authors have also discussed
existing frameworks in agriculture domain. The
application of WEKA-based data mining and
analysis model is discussed in [45]. Authors have
discussed use of machine learning algorithms
through a case study in agricultural domain for
mushroom grading process. In [47] authors have
explained the use of spatial data mining in
agricultural domain. They have used K-means
algorithm along with optimization method
progressive refinement for spatial association
analysis. Temperature and rainfall is given as initial
spatial data and analysing it for the improving the
crop yield and to reduce the crop losses.
Although researchers have proposed few
models in agriculture domain using one or more of
the technologies mentioned; the dynamic model is
needed that provides an integrated approach to:
1. Monitor various soil properties from each
farmland and environmental conditions periodically
through portable cost effective IoT device and
usable by multiple users, enquires about crop
production details to the farmers after crop
harvesting and stores these details at the central
place as in the cloud storage. This in result
producing Big-data over the time and will be
analysed for fertilizer requirements for current
crop, mapping of crop production to soil propertiesHemlata Channe et al, Int.J.Computer Technology & Applications,Vol 6 (3),374-382IJCTA | May-June 2015
Available online@www.ijcta.com376ISSN:2229-6093
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