Role of Data mining in agriculture.

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DATA MINING IN AGRICULTURE
PRESENTED BY:-
SIBANANDA KHATAI

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Contents
 HISTORY
 INTRODUCTION
 DATA VS. INFORMATION
 DATA MINING- CONCEPT
 TECHNIQUES USED IN DATA MINING
 ALGORITHMS USED IN DATA MINING
 ROLE OF DATA MINING
 REVIEW OF LITERATURE
 CONCLUSION

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 Agriculture is a business with risk
 Depends on climate, geography, political and economic factors
 Some risks which can be quantified by mathematical, statistical
methods, and advanced computing
 Challenge is to extract information from agri. Databases
 Data mining is such a technology which can bring the knowledge to
agriculture development
IntRoDUCtIon

4. HIstoRY
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5. eVoLUtIon oF DAtA
sCIenCe
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Evolutionary Steps Enabling Technologies Product
Providers
Data Collection
(1960s)
Computers, tapes IBM
Data Access
(1980s)
Relational databases Oracle,
Informix, IBM,
Microsoft
Data Warehousing OLAP, multi dimensional
databases, data
warehouses
Pilot,
Microstrategy
Data Mining
(Emerging Today)
Advanced algorithms,
multiprocessor
computers,
Pilot,
IBM,others

6. DAtA Vs. InFoRMAtIon
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6

7. MeAsUReMents oF DAtA
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8.  Databases today are huge:
– More than 1,000,000 entities/records/rows
– From 10 to 10,000 fields/attributes/variables
– Giga-bytes ,tera-bytes and now in peta & exa
 Databases a growing at an unprecendented rate
 The corporate world is a cut-throat world
– Decisions must be made rapidly
– Decisions must be made with maximum knowledge
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RApID gRowtH oF DAtA

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9
KnowLeDge DIsCoVeRY In
DAtABAses (KDD)
Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation
KNOWLEDGEKNOWLEDGE

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DAtA pResentAtIon
Graphical-bar charts, pie charts histograms Geometric- scatter plot
Icon-based- using colors figures as icons Pixel-based- data as colored
pixels
0
5
10
15
20
25
30
35
40
10000 30000 50000 70000 90000

11. wHAt Is DAtA MInIng ?
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DATA MINING

12. DeFInItIon
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The process of discovering interesting and useful patterns and
relationships in large volumes of data.
Encyclopaedia Britannica
• Other synonym of data mining
are knowledge extraction, pattern
analysis, data archeology

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14. goALs oF DAtA MInIng
 Prediction: How certain attibutes within the data will behave in
the future.
 Identification: Identify the existence of an item, an event,an
activity.
 Classification: Partition the data into categories.
 Optimization: Optimize the use of limited resources
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15. Why Mine Data?
 Lots of data is being collected and warehoused
– Web data, e-commerce
– Purchases at department/grocery stores
– Bank/Credit Card transactions
– Crop databases
 Computers have become cheaper and more powerful
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16. Why Mine Data? Scientific
VieWpoint
 Data collected and stored at
enormous speeds (GB/hour)
– Remote sensors on a satellite
– Telescopes scanning the skies
– Microarrays generating gene
expression data
– Scientific simulations
generating terabytes of data
 Traditional techniques infeasible for raw data
 Data mining may help scientists
– In classifying and segmenting data
– In Hypothesis Formation
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17. SoURceS of Data foR MininG
 Data warehouses
 Transactional databases
 Spatial and Temporal
 Time-series
 Multimedia, text
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What aRe techniqUeS
USeD in Data MininG?
1.STATISTICS
2.MACHINE LEARNING
3.DATA BASE SYSTEMS
4,5,6,7…… LET’S SEE

19. 1.StatiSticS
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20. 2.Machine LeaRninG
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Ability to automatically learn to recognize complex patterns
and make intelligent decisions based on data.

21. 3.Data baSe SySteMS anD Data
WaRehoUSeS
 A repository of information collection from multiple sources,
stored under a unified schema and that usually resides at a
single site.
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22. 4.infoRMation RetRieVaL (iR)
 activity of obtaining information resources relevant to an information
from a collection of information resources.
 textual data mining and multimedia mining ,integrated with
information retrieval methods have great importance.
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23. 5.aLGoRithMS
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24. 6.patteRn RecoGnition
 Branch of machine learning that focuses on the
recognition of patterns and regularities in data
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25. 7.hiGh peRfoRMance
coMpUtinG
 it is the use of parallel processing for running advanced
application programs efficiently, reliably and quickly.
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26. ReGReSSion MoDeLS
 Regression is a data mining (machine learning) technique
used to fit an equation to a dataset.
 A straight line is given by the equation y = mx + c and
determines the approximate values for m and c to
calculate the value of y based on a particular value of x.
 multiple regression, uses more than one input variable
and allows for the fitting of more complex models
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SUMMaRy

28. Data MininG MethoDoLoGieS
 Neural Networks
 K-means
 Fuzzy set
 Bayesian network
 K-nearest Neighbour
 Support Vector Machine
 Decision Tree Analysis
 WEKA Tool
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29. Neural Networks
 An information processing paradigm that is inspired by
the way biological nervous systems, such as the brain,
process information.
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30. BayesiaN Network
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• originated from Bayes’ theorm
• Also knownas posterior probability

31. waikato eNviroNmeNt for
kNowledge aNalysis (weka)
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i. Machine learning
software written in
java
ii. Free software
iii. Analyze data from
agricultural domains
iv. Visualization tools
and algorithms for
data analysis and
predictive modelling

32. advaNtages of weka iNclude
• Runs on almost any modern computing platform.
• A comprehensive collection of data preprocessing and modeling
techniques.
• Ease of use due to its graphical user interfaces.
• Supports several standard data minig tasks, more specifically, data
preprocessing, clustering, classification regression, visualization, and
feature selection
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33. commercial tools
 Oracle Data Miner
– http://www.oracle.com
 Data To Knowledge
– http://alg.ncsa.uiuc.edu
 SAS
– http://www.sas.com/
 Clementine
– http://spss.com/clemetine/
 Intelligent Miner
– http://www-306.ibm.com/software
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34. matlaB
 MATLAB (matrix laboratory) is a multi-paradigm numerical computing
environment and fourth-generation programming language.
 A proprietary programming language developed by MathWorks,
 MATLAB allows matrix manipulations, plotting of functions and data,
implementation of algorithms, creation of user interfaces
 interfacing with programs written in other languages, including C, C++,
Java, Fortran and Python.
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35. r
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• R is a programming language and software environment for statistical
computing and graphics supported by the R Foundation for Statistical
Computing
• widely used among statisticians and data miners for developing
statistical software & data analysis.
• Polls, surveys of data miners, and studies of scholarly literature
databases show that R's popularity has increased substantially in
recent years.

36. role of data miNiNg iN
agriculture
 Influence of climate on kharif and rabi crops
 Crop yield estimation
 Estimation of Damage caused by pest
 Mushroom grading
 Spatial data mining reveals interesting pattern related to
agriculture
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37. role iN agriculture domaiN
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Data mining methodologies Applications
Neural Networks Focuses on weather forecasts, Prediction of
rainfall
K-means Classifying soil in combination with GPS, Wine
fermentation problem, Yield Prediction
Fuzzy set For detecting weeds in precision agriculture
Bayesian network Developed the model for agriculture purpose
based on the Bayesian network learning method
K-nearest Neighbour Simulating daily precipitations and other
weather conditions

38. typical applicatioNs of data
miNiNg
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39. review of literature
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40. a study oN effect of weatHer parameters
By artificial Neural Networks oN yield
of aoNla (iNdiaN gooseBerry) uNder
differeNt fertiliZers treatmeNts
 April Month’s Range of temperature has high correlation
coefficient with yield Highest
 Extreme variations negatively, affect the AONLA fruit
yield.
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Kulshrestha et al,2010Anand

41. geospatial data miNiNg tecHNiques
kNowledge discovery iN
agriculture
Visualization of the spatial OLAP of Gujarat
Concluded that integration of computer science with
agriculture will generate new emission in management of
agricultural information
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Bhojani, 2013Anand

42. time series forecastiNg of losses due to pod Borer,
pod
fly aNd productivity of pigeoNpea (caJaNus caJaN)
for
NortH west plaiN ZoNe (NwpZ) By usiNg artificial
Neural
Network (aNN)
Pod damage by pod fly
in2010-11–damage found as 22.21%
2011-12 –damage found as18.77%
2012-13 – damage found found as 16.72%
A linear regression between network outputs and the
corresponding targets with the R2
value as 0.88
Indicating the fit was reasonably good for all data sets
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Kumari et. al. 2014Varanasi

43. A survey on DAtA Mining techniques for
crop yielD preDiction
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 K-Means algorithm is used to perform forecast of the
pollution
 K Nearest Neighbor (KNN) is applied for simulating daily
precipitations
 K-Means approach is used for classifying soils in
combination with GPS
Ramesh et al,2014Bangalore

44. A survey on DAtA Mining techniques in
Agriculture
 Use of information technology in agriculture can change the
situation of decision making and farmers can yield in better
way
 Data mining plays a crucial role for decision making on several issues
related to agriculture field.
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Geetha et al,2015Coimbatore

45. preDiction of stuDents recruitMent
process using DAtA Mining techniques
with clAssificAtion rules
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Malathi et al,2015Tamilnadu

46. text recognition
 Feature Extraction,
 Clustering, and Pattern Matching
 k-NN classifier
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Haridwar Krishan et al,2016

47. the ApplicAtion of DAtA Mining
techniques to chArActerize
AgriculturAl soil profiles
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Armstrong et al.Australia

48. iMpleMentAtion of DAtA Mining
techniques for MeteorologicAl DAtA
AnAlysis
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palestine Sarah et al.

49. DAtA Mining - An evolutionAry view of
Agriculture
 GPS techniques may be employed for discovering important
information from agricultural-related like soil identification.
 Data Mining techniques were adopted in order to estimate crop
yield analysis with existing data and their use in data mining.
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Amaravati Abhishek et al. 2014

50. DAtA Mining in Agriculture on crop
price preDiction: techniques AnD
ApplicAtions
 Data Mining techniques were adopted in order to estimate crop
price analysis with existing data
 K-Means approach, utilize only the basic algorithm
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Bangalore Manpreet et al.,2014

51. DAtA Mining techniques for preDicting
crop proDuctivity – A review Article
 Data mining is relatively a novel research field and it is
expected to grow in the future
 multidisciplinary approach of integrating computer science with
agriculture will help in forecasting/ managing agricultural
crops effectively.
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Bhopal Veenadhari et al. ,2011

52. Applying DAtA Mining techniques in the
fielD of
Agriculture AnD AllieD sciences
 Mentioned different techniques of data mining used in
agriculture domaim
 K-means algorithm,ID3 algoritm,k nearest neighbour are
mostly used
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Bangalore Yethiraj,2012

53. DAtA Mining techniques AnD
ApplicAtions to
AgriculturAl yielD DAtA
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Andhra pradesh
Ramesh,2013
MLR Technique is given as 98 % and using K-Means algorithm is
given as 96% accuracy

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 Data Mining is boon for large data in agriculture
 Extraction of knowledge is a big challenge
 A lot of data mining techniques are developed today to
tackle the challenge
 Skill is also required to handle the tools and techniques
conclusion

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THANK YOU