Mining Accumulated Crop Cultivation Problems and their Solutions

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Mining Accumulated Crop Cultivation Problems and
their Solutions
Shivendra Tiwari Arvind Kumar Mahla
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Outline

• Introduction
• Motivation
• Related Work
• Objectives
• Challenges Problem Definition
• Proposed Solution - Template Based Association
  Rules Mining
• Case Study VERCON 2006
• Discussion and Questions

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Introduction

• Fast access of the solution to various problems
  can greatly influence the agricultural
  productivity. The farmers in the developing
  countries dont have expertise and are dependent
  on the experts advices.
• Farmers Problems Database
• (VERCON subsystem in Egypt Agricultural problem
  database)
• A web interface for users to input problem
  (meta-data descriptors and free text description)
• Problem forwarded to researcher
• Solution in free text from researcher
• Problem and its solution stored in textual
  database
• Usage
• Search for similar problem and solution
• Post as a new problem

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Motivation

• The Agricultural problems database grew
  significantly (10000) over a period of five
  years.
• Locating similar problems became difficult with
  increasing size leading to redundancy
• The queries and the solutions are unstructured
• The problems and corresponding solutions can be
  extensively used by the Decision makers,
  Researchers, and Farmers

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Objectives

• Addition Insertion of new Problems - Avoid
  Duplicate Problems Insertion.
• Validation Modification of the solutions by the
  domain experts.
• Accessing the existing solutions efficiently and
  accurately.
• Inconsistency resolution in the problems and
  corresponding solutions.
• Removal of the outdated material from the DB.
• Problem resolution without domain experts help
  on the basis of the past pattern.
• Decision/Policy Making using the Patterns and
  relations.
• Problems predictions

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Challenges Problem Definition

• Plain Text The problems and solutions are stored
  in the plain text format.
• Information Extraction to convert the plain text
  to the structured data first.
• Problem Classification (i.e. weeds, diseases,
  pests, fertilization and irrigation)
• Identification of the Complaint Object the
  farmers even dont know what problem is it? They
  just enter the symptoms.
• Feature description of the Complaint Object.
• Text data variety
• Discovery of similar complaint written in
  different styles.
• Single complaint may contain one or more primary
  complaints.
• Complaints can look similar, but they are
  actually different.
• Data representation
• Structured Problem/Query Formulation
• Structured Solution Formulation
• Extraction Algorithm
• Summarize and Analyze Information

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Related Work

• Opinion Mining Used to assist customers in
  product review before purchase.
• Display bright, dark, clear etc (for a mobile
  phone)
• Look stylish, traditional, moderate etc.
• Weight heavy, slim, light etc
• Association Rules Extracting Product Feature
  from English Product Reviews.
• Opinion Observer observe the advantages and
  disadvantages of a product by collecting positive
  and negative words in the review.
• Ontology Usage use of ontology to discover the
  problem object, extracting key words and
  sentences etc.

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Sample Problems

• There are spots on the leaves and on the spikes
  which have a cotton like texture and which turn
  to grey in some areas within the planted 25
  feddan land.
• colorgray, texturecotton like
• There are white, non-uniform spots with cotton
  like texture on the lower surface of plant
  leaves.
• plant, colorwhite, texturecotton like,
  locationlower surface, distributionnon-uniform

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Template Based Association Rules Mining

• Template Based Data Storage
• Named Entries
• Timed Based Entries
• Number Based Entries
• Percentage and Rates
• Data Representation (Predefined Template) -
  Multi-Faceted Object Extraction Methodology
• Structured Problem Formulation
• Structured Solution Formulation
• Information Extraction Algorithm
• Summarize and Analyze Information (association
  rules mining)

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Template Based Association Rules Mining cont

• Metadata is used to classify problems extract
  attributes.
• The complaint text is scanned word by word.
• Ontology is used to identify the agricultural
  objects and associated features.
• The word found is marked as identified and
  location is stored.
• A template is used to store a problem and the
  solutions.
• Main Object of Complain (MOC) and Main Object of
  Solution (MOS) is extracted finally.
• One Complaint Object contains both the MOC and
  the MOS.

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Association Rule Mining

• Given Item sets, minimum support confidence
  levels
• Output Association rules (A -gt B, A -gt C, B -gt
  C)
• Algorithm Apriori or its variants
• The algorithm finds out frequent item sets
  containing 1 to many items.
• Based on these frequent item sets association
  rules are formulated.
• A rule B -gt C holds with confidence level c if at
  least c of records which contain B also contain
  C
• A rule B -gt C has support s in the dataset if at
  least s of records contain B U C

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Case Study - VERCON (Virtual Extension and
Research Communication Network)

• VERCON is a conceptual model of the Food and
  Agricultural Organization (FAO) of United
  Nations.
• It has been adopted by over 7 countries (i.e.
  Govt. of Bhutan, Govt. of Egypt etc).
• It is used to improve and establish a national
  agricultural knowledge
• Aims and Challenges
• Strong linkage between research and field
  implementation.
• Easy access of agricultural information.
• Connecting geographically dispersed people and
  enhance two-way communication.
• Rapid data collection, processing, disseminating
  information and managing large volumes of data
  are the key challenges.

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Case Study VERCON objectives
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VERCON globally shared information
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VERCON how does it work, a new problem?

• Problem reporting
• Cabbage crop has infected with a new leaf disease
• A leave is sent to location extension
• Disease identification at local extension office
• The database contains locally taken snaps and
  internationally compiled images of the leaves.
• Extentionist matches the image, but could not
  recognize the disease.
• Contact the specialist at nearest research
  station
• Post online enquiry with photos of the infected
  leaves.
• Also send the sample leaves by postal mail.
• The email message is copied to other extension
  and research stations to alert them to the new
  problem.

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VERCON how does it work?

• Problem Diagnosis at Specialist level
• The specialist discusses this problem with
  other colleagues if this is a new problem.
• contacts the extensionist via email to find out
  more information such as what crops are growing
  nearby.
• Diagnosis at Researchers End
• The researcher confirms the diagnosis as a fungus
  previously found only in another part of the
  country.
• Suggest the suitable disease management practices
  in the context of the farmers situation.
• Publish the factsheet of the new problem to every
  stakeholders and the extensions.
• Extension services communicate new problem and
  corresponding solutions to the farmers.

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Thank You
Questions Discussion