Technologies 10 00013
Technologies 10 00013
Technologies 10 00013
Article
IoT Framework for Measurement and Precision Agriculture:
Predicting the Crop Using Machine Learning Algorithms
Kalaiselvi Bakthavatchalam 1 , Balaguru Karthik 1 , Vijayan Thiruvengadam 1 , Sriram Muthal 2 , Deepa Jose 3 ,
Ketan Kotecha 4, * and Vijayakumar Varadarajan 5, *
1 Department of Electronics and Communication, Bharath Institute of Higher Education and Research,
Chennai 600073, India; kalaigopal1973@gmail.com (K.B.); karthikguru33@gmail.com (B.K.);
tvij16@gmail.com (V.T.)
2 Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research,
Chennai 600073, India; msr1sriram@gmail.com
3 Department of Electronics and Communication Engineering, KCG College of Technology,
Chennai 600097, India; deepa.ece@kcgcollege.com
4 Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Sena Pati
Bapat Road, Pune 411004, India
5 School of Computer Science and Engineering, The University of New SouthWales, Sydney 466, Australia
* Correspondence: head@scaai.siu.edu.in (K.K.); v.varadarajan@unsw.edu.au (V.V.)
Abstract: IoT architectures facilitate us to generate data for large and remote agriculture areas and the
same can be utilized for Crop predictions using this machine learning algorithm. Recommendations
are based on the following N, P, K, pH, Temperature, Humidity, and Rainfall these attributes decide
the crop to be recommended. The data set has 2200 instances and 8 attributes. Nearly 22 different
Citation: Bakthavatchalam, K.; crops are recommended for a different combination of 8 attributes. Using the supervised learning
Karthik, B.; Thiruvengadam, V.; method, the optimum model is attained using selected machine learning algorithms in WEKA. The
Muthal, S.; Jose, D.; Kotecha, K.; Machine learning algorithm selected for classifying is multilayer perceptron rules-based classifier JRip,
Varadarajan, V. IoT Framework for and decision table classifier. The main objective of this case study is to end up with a model which
Measurement and Precision
predicts the high yield crop and precision agriculture. The proposed system modeling incorporates
Agriculture: Predicting the Crop
the trending technology, IoT, and Agriculture needy measurements. The performance assessed by
Using Machine Learning Algorithms.
the selected classifiers is 98.2273%, the Weighted average Receiver Operator Characteristics is 1 with
Technologies 2022, 10, 13. https://
the maximum time taken to build the model being 8.05 s.
doi.org/10.3390/technologies
10010013
Keywords: precision agriculture; WEKA; machine learning; multilayer perceptron; JRip; decision table
Academic Editor: Pedro
Antonio Gutiérrez
management, Pest control and management, Precise detection and nutrients management
and safely storing management.
Historical development of sensors shows the progress in measuring various parame-
Historical development of sensors shows the progress in measuring various param-
ters like temperature, pH, Humidity, Analytical parameters like potassium, phosphorous,
eters like temperature, pH, Humidity, Analytical parameters like potassium, phospho-
Nitrogen measurements from a remote location and the data acquisition is possible to attain
rous, Nitrogen
all the measurements
measurements from a remote
using sensors therebylocation
the dataand the data
is stored inacquisition is possible
the cloud or network
to attain all the measurements using sensors thereby
server for further processing as shown in Figure 1. the data is stored in the cloud or
network server for further processing as shown in Figure 1.
Sensors
Sensors areare combined
combined totoform forma network
a network thatthatcan can be accessed
be accessed or linked
or linked by cloud/ by
cloud/backend where the sensor responses in a different geographical
backend where the sensor responses in a different geographical area are linked by the area are linked by
the cloud
cloud [4–6].
[4–6]. ThereThere
are are
fourfour different
different phasesphases
fromfromsmart smart
thingsthings without
without connectivity
connectivity next
next progression was a local exchange of information and
progression was a local exchange of information and distributed control systems distributed control systems
with
with programmable
programmable logiclogic controllers,
controllers, the network
the network of things.
of things. The next
The next phasephase includes
includes in-
internet-
ternet-based communication
based communication for monitoring
for monitoring and control,
and control, thingsthings
on theon the internet.
internet. Thephase
The final final
phase is regional,
is regional, global,global, and open
and open control
control loops loops
andandIoT IoT [7,8].
[7,8]. Examples
Examples Seamless
Seamless internet
internet of
of things
things supply
supply chain
chain management
management and
and product
product lifelife cycle
cycle management
management [9–11].
[9–11].
Organization
Organizationof ofthe
thepaper:
paper:The Thearticle
articleisiscomposed
composedof offive
fivesections
sectionsandandstarted
startedfrom
from
strengthening
strengthening the concept to deploy a module to recommend the crop for irrigationand
the concept to deploy a module to recommend the crop for irrigation and
attain
attainmaximum
maximumyield yieldwith
withthetherecommended
recommendedcrop. crop.The Thenext
nextsection
sectionisisrelated
relatedto to works
works
from
fromIoTIoTininthe
theAgriculture
Agriculturesector
sectorandandprecision
precisionagriculture
agricultureusingusingmachine
machinelearning
learningalgo-algo-
rithms.
rithms.This
Thisisisfollowed
followedby bythe
themethodology,
methodology,proposed
proposedblock blockdiagram,
diagram,and andexperimental
experimental
setup.
setup.Then
Thenthetheexperimental
experimentalanalysis
analysisandanddiscussion
discussionend end with
with references.
references.
2.
2. Literature
LiteratureReview
Review
Machine
Machinelearning
learningisisaamajor
majorsource
sourceofoftechnological
technologicaltrending
trendingrevolutions.
revolutions.With
Withrecent
recent
developments in the process control industry, expectations on both the client and server
developments in the process control industry, expectations on both the client and server
sides
sides suggest
suggest when recommendingspecific
when recommending specificcrops
cropsusing
usingthetheInternet,
Internet, reputable
reputable magazine
magazine ar-
articles, and machine learning algorithms of choice. It is detailed from the available re-
ticles, and machine learning algorithms of choice. It is detailed from the available resources,
such as the
sources, suchconferences that support
as the conferences thatthe system.
support theOnline
system.web journals
Online webprovide
journalsimportant
provide
information and generally provide tips and solutions in the event of a
important information and generally provide tips and solutions in the event of a malfunc- malfunction. It is
essential
tion. It is to anticipate
essential such problems
to anticipate suchand deceptions
problems and that can leadthat
deceptions to serious
can leadconsequences
to serious
of failure.
consequences of failure.
M. S. Paroquet et al. (2019) [12] automatic maintaining and monitoring agricultural
farms using IoT.S. Al-Sarawi et al. (2017) [13] collection of smart devices exchange data
using wireless IoT and communication technologies and protocols using BLE, NFC,
LPWAN, LoWPAN. Agrawal et al. (2019) [14] best utilization of technology for farmers
Technologies 2022, 10, 13 3 of 12
help greatly help farmers to decide crops for cultivation. H. B. Biradar et al. (2019) [29]
estimating the crop water requirement and incorporating IoT, Cloud computing, and CPS-
Cyber-physical systems which plays a vital role in improving productivity, feeding the
world, and preventing starvation. Ravesa Akhter et al. (2021) [30] this article summarizes
the latest trend in interfacing the IoT, Wireless sensor networks, data mining cum analytics,
and Machine learning in agriculture. Smart agriculture is the trending technology, and
this article mainly predicts the apple disease in apple orchards in Kashmir valley using
machine learning (simple regression model) and IOT Data analytics. Archana Gupta
et al. (2021) [31] this article gives sound knowledge about smart farming using machine
learning what crop will give maximum yield depending upon the environmental and
sensor network parameters. IoT-based smart farming improvising the entire agriculture
sector and increasing the crop productivity, recommend the crop which will give real-time
monitoring on the parameters to give maximum productivity and quality. Vivekanandhan
et al. (2021) [32] this article influences the feature selection, preprocessing and followed
by classification using fuzzy rule-based to validate the input parameters and predict the
environmental changes efficiently and attain smart irrigation.
Table 1 describes the comparison of existing IoT framework in agriculture, this related
works support to implement in the proposed work.
3. Methodology
Technologies 2022, 10, x FOR PEER REVIEW 5 of 12
Technologies 2022, 10, x FOR PEER REVIEW
The 5 of 12
terms/materials used for this experiment are described for the sake of improving
the readability of the proposed framework with clarity.
3.1.IoT
3.1. IoTFramework
Frameworkfor forAgriculture
Agriculture
3.1. IoT Framework for Agriculture
The proposed systemconsists
The proposed system consistsofof interfacing
interfacing thethe real-world
real-world data
data from
from storage
storage media
media to
The proposed system consists of interfacing the real-world data from storage media
to cloud Database management system from where the request is sent to the Machine
cloud Database management system from where the request is sent to the Machine learning
to cloud Database management systemoutput
from where the request is among
sent to 22thedifferent
Machine
learning
trained trained
model model as
as shown in shown in The
Figure 2. Figure 2. Theof output of theismodule
the module one is one among 22
learning trained model as as shown
shown in Figure 2. The output of the module is one among 22
different
crops crops for implementation
for implementation as Figure
in shown3.in Figure 3.
different crops for implementation as shown in Figure 3.
Figure2.2.Proposed
Figure ProposedIoT
IoTClient-Server
Client-ServerModel.
Model.
Figure 2. Proposed IoT Client-Server Model.
Figure4.4.Modelling
Figure Modellingofofcrop
croprecommended
recommendedmodule
modulesetsetup.
up.
Irrigationofofcrops
Irrigation cropsdepends
dependson ondifferent
differentenvironmental
environmentalfactors
factorsandandsoil
soilfertility
fertilityi.e.,
i.e.,
available nutrients present in the soil like nitrogen, phosphorous, and Humidity. The 7
available nutrients present in the soil like nitrogen, phosphorous, and Humidity. The
7 attributes
attributes can decide aa crop
can decide crop for
for irrigation
irrigation sosothat
thatmaximum
maximumyield yieldcancanbebeattained.
attained.This This
articlecan
article canbebe used
used toto make
make a strong
a strong decision
decision making
making in planting
in planting different
different crops.
crops. WEKA WEKA or
or Waikato
Waikato Environment
Environment for Knowledge
for Knowledge Analysis
Analysis the University
the University of Waikato
of Waikato Hamilton, Hamilton,
New
New Zealand
Zealand is an open-supply
is an open-supply facts facts
miningmining software
software program
program issuedissued beneath
beneath GNU GNU pub-
public
lic license
license softwaresoftware program
program that gives
that gives freedomfreedom to its customers
to its customers in appearing
in appearing most ofmost theseof
these
facts facts mining
mining works[34].
works [34].
ItItisisa aset
setofof many
many devices
devices gaining
gaining knowledge
knowledge of algorithms
of algorithms for for facts
facts mining
mining tasks.
tasks. It isIt
a is a percent
percent of a of a device
device that includes
that includes diversediverse operations
operations known known
as file as file preparation,
preparation, clus-
clustering,
tering, regression,
regression, facts preprocessing,
facts preprocessing, classification,
classification, Association
Association rules, instance-primarily
rules, instance-primarily based
basedclassifying,
totally totally classifying, and picturing.
and picturing. The technique
The technique worried worried in organizing
in organizing the project theis project
done
is done the use of this software program known as WEKA for modeling the above stated
clever positioning the use of photo processing
the use of this software program known as WEKA for modeling the above stated clever
positioning the use of photo processing.
3.3.3. JRip
This classifier in the WEKA tool is a class-based prepositional rule learner, Repeated
Incremental Pruning to Produce Error Reduction (RIPPER). There are two basic phases
that are grown phase and the pruning phase. Ingrow phase is the rule by greedily adding
highest information gain: p(log(p/t) − log(P/T)). In the pruning phase, any final sequences
are added to the growing phase. Optimization stage and fixation of discretion length and
the whole ruleset is fixed.
Tools Applied
WEKA (Waikato Environment for Knowledge Analysis) is selected for machine learn-
ing algorithm implementation for crop recommendation in this experimental analysis.
WEKA is an open-source innovative tool for all research communities working on both
supervised and unsupervised learning methodologies. WEKA with java platform imple-
mentation is the best-suited tool for machine learning techniques [34].
Technologies 2022, 10, 13 8 of 12
Correctly
Selected WEKA Weighted Avg. Time to Build
S. No Category Classified Analysis
Classifier ROC the Model
Instances (%)
Kappa statistic
1 Functions MLP 98.2273 0.997 10.56
0.9814
Mean absolute
2 Lazy Decision table 88.5909 0.991 0.23
error 0.004
Root mean squared
3 Lazy JRip 96.2273 0.993 0.58
error 0.035
Technologies
Technologies 2022, 10,10,
2022, 13x FOR PEER REVIEW 9 9ofof1212
%
100.00% 98.23%
Accuracy
88.59% 96.23%
90.00%
Accuracy % 95.00%
85.00%
90.00% 88.59%
80.00%
85.00%
Selected Classifiers
80.00%
MLP Decision Table JRip Linear (MLP)
Selected Classifiers
Figure5.5.Classifier
Figure Classifier versusDecision
MLP versus Performance accuracy
Table accuracy
Performance percentageLinear
JRip
percentage characteristics.
(MLP)
characteristics.
Theperformance
The next characteristics in building
of the three thenamely
classifiers model multilayer
weighted receiver operator
perceptron, character-
decision table,
Figure 5. Classifier versus Performance accuracy percentage characteristics.
istics
and of has
JRip 1 will give very
shown us anminimum
optimumerror modelandand MLPError
RMSE showing
in thethe bestofROC
range than
0.1384 the se-
to 0.058.
lectedThe
Theclassifiers
next as show inin
nextcharacteristics
characteristics Figure 6. The
inbuilding
building the time
the taken
model
model to build
weighted
weighted the model
receiver
receiver is spanned
operator
operator from
characteris-
character-
10.56
tics of to
1 0.23
will s
givefor
usmultilayer
an optimumperceptron
model and
and decision
MLP table
showing classifiers.
the best ROC
istics of 1 will give us an optimum model and MLP showing the best ROC than the se- than the selected
classifiers as show in Figure 6. The time taken to build the model is spanned from 10.56 to
lected classifiers as show in Figure 6. The time taken to build the model is spanned from
0.23 s for multilayer perceptron and decision table classifiers.
10.56 to 0.23 s forClassifier
multilayervs.
perceptron
Weighted andAvgdecision
ROCtable classifiers.
0.998 0.997
Classifier vs. Weighted Avg ROC
Avg ROC
0.996
0.998 0.997
0.994 0.993
Avg ROC
0.996
Weighted
0.992 0.991
0.994 0.993
0.99
Weighted
0.992 0.991
0.988
0.99 MLP Decision Table JRip
0.988 Classifiers
MLP Decision Table JRip
Classifiers
Figure 6. Classifier versus Receiver Operator characteristics.
The preprocessing of the data set by normalization leads to time reduction in build-
Figure6.6.Classifier
Classifierversus
versusReceiver
ReceiverOperator
Operatorcharacteristics.
characteristics.
ing the model and
Figure improving the ROC measure to one almost all classifiers show the same
measure approximately.ofThe
Thepreprocessing
preprocessing performance
thedata
data of the model has given an accuracy percentage
The of the setset
byby normalization
normalization leads
leads to time
to time reduction
reduction in build-
in building
of 98.2273%
ingmodel
the model for multilayer perceptron and 88.59% for Lazy category decision table classi-
the and and improving
improving the the
ROC ROC measure
measure to one
to one almost
almost allall classifiersshow
classifiers showthe thesame
same
fier.
measure approximately.The The performance
measure approximately. performance of of
thethe model
model hashas given
given an an accuracy
accuracy percentage
percentage of
The for
of 98.2273%
98.2273% second iterated
for multilayer
multilayer model clearly
perceptron
perceptron andand dictates
88.59%
88.59% that
for forthe
Lazy time
Lazy taken
category
category to build
decision
decision the
table model
table is
classi-
classifier.
reduced by preprocessing the data set using normalization as shown in
fier.The second iterated model clearly dictates that the time taken to build the model is Table 4. The iter-
ated The
reducedmodel after pre-processing
bysecond iteratedthe
preprocessing dataindicates
model clearly
set using that
dictatesthethat
accuracy
normalization the as performance
time taken
shown into did4.not
build
Table thevary
The model even
iterated is
after normalizing the data set. But the time to build the model greatly
reduced by preprocessing the data set using normalization as shown in Table 4. The iter-
model after pre-processing indicates that the accuracy performance did changed.
not vary The
even data
after
set ismodel
ated normalized
normalizing after to reduce
the data set. Butredundancy
pre-processingtheindicates and
thatto
time to build get
the
the the required
accuracy
model result
performance
greatly changed.indid
less
The time
not vary
data [4].
even
set is
after normalizing
normalized theredundancy
to reduce data set. Butand
theto
time
getto
thebuild the model
required result greatly changed.
in less time [4]. The data
set is normalized to reduce redundancy and to get the required result in less time [4].
Technologies 2022, 10, x FOR PEER REVIEW 10 of 12
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10ofof12
12
98.00%
96.00% 96.23% 96%
percentage
96.00%
94.00%
94.00%
92.00%
92.00%
90.00% 88.59% 88.59%
Accuracy
90.00%
88.00% 88.59% 88.59%
Accuracy
88.00%
86.00%
86.00%
84.00%
84.00%
82.00%
82.00% MLP Decision Table JRip
MLP Decision Table JRip
Classifier
Classifier
First iterated model accuracy %
First iterated model accuracy %
Second iterated model accuracy percentage
Second iterated model accuracy percentage
Figure7.7.Classifier
Figure Classifierversus
versusPerformance
Performanceaccuracy
accuracypercentage
percentagecharacteristics.
characteristics.
Figure 7. Classifier versus Performance accuracy percentage characteristics.
8.78
Sec
8
6
in in
Time
6
4
Time
4
2
2 0.58
0.58
0 0.24
0.23
0.23 0.15
0 MLP Decision
0.24 Table JRip
0.15
MLP Decision Table JRip
Classifiers
Classifiers
Time to build the model 1 Time to build the model 2
Time to build the model 1 Time to build the model 2
Figure 8. Classifier versus time taken to build model 1 and model 2.
Figure8.8.Classifier
Figure Classifierversus
versustime
timetaken
takentotobuild
buildmodel
model11and
andmodel
model2.2.
Technologies 2022, 10, 13 11 of 12
Author Contributions: Conceptualization, K.B. and V.T.; methodology, B.K.; software, V.T. and
S.M.; validation, K.B., D.J. and K.K.; formal analysis, V.V.; investigation, V.V.; resources, D.J.; data
curation, K.B. and S.M.; writing—original draft preparation, V.T.; writing—review and editing, D.J.;
visualization, K.K.; supervision, D.J. All authors have read and agreed to the published version of
the manuscript.
Funding: No funding for this Research Manuscript.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data collected from public data base. (www.kaggle.com, 20 November
2021).
Conflicts of Interest: The authors declare no conflict of interest.
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