ontology based flexible querying system for farmers

ONTOLOGY BASED FLEXIBLEQUERYING SYSTEM FOR FARMERS

GISE Advanced Research Lab, CSE Dept, IIT Bombay

Presented By: Neha Arora

Monday,17 Sept,2012

INTRODUCTION Farmers express their queries in natural language which are usually

answered by human experts.

Purpose is to enable the system to understand the user query as exactly as expert does.

Farmers have many questions regarding the type of soil/climate, type of pests, diseases and activity timelines related to their crop.

Existing Agro Advisory Systems aAQUA system by IIT Bombay eSagu by IIIT Hyderabad mKRISHI at TCS

GISE Lab, IIT Bombay Monday,17 Sept,2012

PROBLEM STATEMENT

System which handle farmers query without the need of agro-expert handling them

Farmers posts his observation/query to the system System advises him or provides information on query

System acquires knowledge as exhibited by an agro-expert This knowledge is stored as knowledge models or ontologies

Purpose is to provide context-based knowledge driven advisory solution


KNOWLEDGE REPOSITORY

System contains knowledge as:

- Ontology: schematic or intelligent view over information

resources

- Information in Database

Farmer’s past activity records

Current activity records of farmer

Data for weather forecast


CROP ONTOLOGY

Stores complete knowledge about the cotton crop

Information stored as classes, instances, properties and literal values

Ontology contains information of various areas

- Variety

- Disease

- Pest

- Symptoms

- Control Measures

- Activity (Fertilizing, Harvesting, Hoeing, Irrigation)


Generic/Specific Ontology Generic Ontology - Ontology which defines classes and properties

between them


Generic/Specific Ontology Specific Ontology - Ontology which defines classes with their

instances


Contextual ad Dependent Information

Annotation properties - associates information with classes and properties in ontology (*planning to use another way out)

In crop ontology,

- Context Information: information dependent on context of crop

(stage of crop and ongoing season)

- Dependent Information: information giving related details of a

class


Information In Database

Data collected from three districts of Punjab for past 5 years It contains information regarding farmers and their farming

practices

Current farming practices are also captured in the system

◮ Variety sown, date of sowing

◮ Irrigation details

◮ Fertilizer details - Nitrogen, Potassium, Phosphorous Application

◮ Spraying details - for Jassid, Whitefly, Tobacco Caterpiller

Data on weather forecast. And classification on that basis.


Ontology Based System What it does?

System has knowledge in both ontology and relational database

- User queried information is searched over the ontology

- Context information about the crop added from database filters this

search


System Architecture


System ArchitectureQuery Interface:

•where farmer’s posts his observations about the crop or information which he seeks

•Farmer enters a keyword based query

Query Engine:

•core part where query processing takes place

•Understands user query, interprets and generates advice or information

Database:

•place where knowledge resides

•Ontology is stored here along with farmer’s activity details and

weather data


Ontology Querying Overview of Algorithm


Farmer’s Query

• State of farmer’s crop

Date of sowing: 15th April

Farming activity: Nitrogen Application on time

Potassium Application on time

Phosphorous Application on time

Irrigation not on time

• Query: Boll Shedding


Mapping Keywords over Ontology

• First, exact match is searched

• If not found, using WordNet find synonym matches

• Keyword matches are found over the ontology

Boll => {Boll, Boll Infection, Boll Shedding, Bad Boll Opening ...}

Shedding => {Boll Shedding, Flower Shedding, Buds Shedding ...}

• Final keyword matches

Boll => {Boll, Boll Shedding}

Shedding => {Boll Shedding, Flower Shedding, Buds Shedding ...}

• Keywords classified as classes C, instances I, object property O,

datatype property D, literal L

C => {}

I => {Boll, Boll Shedding, Flower Shedding, Buds Shedding ...}

O => {}

D => {}

L => {}GISE Lab, IIT Bombay Monday,17 Sept,2012

Find farmer’s activityActivity information stored in ontology as:


Find farmer’s activity Check Activity Consistency

Dates from Database Dates from Ontology

17 April 17 April

09 May 07 May

28 May 27 May

17 June 16 June

Activity is fine. Next date for activity is recommended




17 April 17 April

09 May 07 May

28 May 27 May

17 June 16 June

27 June

Excess of activity is performed




17 April 17 April

09 May 07 May

28 May 27 May

????? 16 June

Lack of activity is found

• Check stage at which activity is missed

• If stage is critical, immediate action to be taken

information marked on ontology

Else, farmer is advised to perform activity

• If lack of activity, add ontology instances to set activityI

activityI => {Lack of Irrigation}GISE Lab, IIT Bombay Monday,17 Sept,2012

Find Context Information

• Information which is based on crop context

• Classes are associated with :context property in ontology

• Context checked for stage and ongoing season

- stage: whether the stage of class instance is same as of farmer

crop

- ongoing season: whether the class instance has a current period

of existence

• Find :context property for classes of instance set I, activityI and

store in set contextI

contextI => {Spotted Bollworm, Bacterial Blight, Tirak, ... }


Find Dependent Information

• Related information which we would like to display to the user

• Eg: For a Disease queried by user, its Symptom and Control

Measures would be of interest to him

• Dependent information stored as :dependentClass property for

classes in ontology

• Find :dependentClass property for classes of instance set I,

activityI, contextI and store in set dependentC

dependentC => {Damage, Symptom}


Searching On Graph Search is performed on graph, returning paths connecting the

selected nodes

Figure: Specific Ontology


Searching On Graph Search is performed on graph, returning paths connecting the

selected nodes

Figure: Generic Ontology


Calculate Paths Generic ontology has nodes as classes and edges as properties Classes of instance sets I, activityI and contextI along with

classes in dependentC and C are added to set SN of Selected Nodes Find all possible paths between nodes in SN using BFS

SN => {Damage, Part, Control Measure, Factor, Pest, Disease,

Symptom}

P => {Damage is_Caused_By Pest,

Damage is_Caused_By Factor may_Lead_To Disease,

Symptom is_Symptom_Of Disease occurs_Due_To Factor

is_Controlled_By Control Measure,

... }


Calculate Instance Paths For the paths obtained from Generic ontology, instance paths

are calculated on Specific ontology Paths with known instances of classes are selected

Instance paths P => {Boll_Shedding is_Caused_By

Lack_of_Irrigation may_Lead_To Tirak is_Controlled_By Proper_Irrigation,

Flower_Shedding is_Caused_By Lack_of_Irrigation

May_Lead_To Tirak is_Prevented_By Proper_Irrigation,

Boll_Shedding is_Caused_By Spotted_Bollworm,

Boll_Shedding is_Caused_By Lack_of_Irrigation

May_Lead_To Tirak has_Symptom Black_Boll_Color,

Spotted_Bollworm causes Buds_Shedding,

... }


Ranking of Paths Paths are ranked based on several factors –

+ Keyword similarity count

◮ Exact matches

◮ Synonym matches

+ Actionable information

Information derived from activity and context

+ Dependent information

Information obtained from :dependentClass property

+ Paths containing edges selected by user in sets O and D


Output to the User Finally the user is presented with set of paths which best match his search Highest ranked path is most relevent, but other matched path are also

returned

Instance paths P => {Boll_Shedding is_Caused_By



May_Lead_To Tirak is_Prevented_By Proper_Irrigation,



May_Lead_To Tirak has_Symptom Black_Boll_Color,


... }


Similar Queries by other Farmers State of farmer’s crop

◮ Date of sowing: 15th April

Farming activity: Nitrogen Application not on time

P => {Boll_Shedding is_Caused_By

Nitrogen_Deficiency is_Controlled_By Application_of_Urea,


Buds_Shedding is_Caused_By Nitrogen_Deficiency

Is_Controlled_By Application_of_Urea,


Flower_Shedding is_Caused_By Spotted_Bollworm,

... }


Similar Queries by other Farmer State of farmer’s crop

◮ Date of sowing: 15th April

Farming activity: Nitrogen Application not on time

Irrigation not on time

P => {Boll_Shedding is_Caused_By


Boll_Shedding is_Caused_By Nitrogen_Deficiency

is_Controlled_By Applicatio_of_Urea,


may_Lead_To Tirak has_Symptom Black_Boll_Color,



may_Lead_To Tirak is_Prevented_By Proper_Irrigation,

Buds_Shedding is_Caused_By Nitrogen_Deficiency

is_Controlled_By Application_of_Urea,

... }

Interface

Information on Weather Forecast

Weather data for next 5 days captured from meteorological department for the three districts of Punjab

Data is captured about rainfall, temparature and humidity

System uses the rainfall data to help suggesting the farmer


Example Queries Input Query 1: Leaf Turning Black

Result: {Black_Leaf_Color is_Symptom_Of

Bacterial_Blight is_Prevented_By Release_Trichogramma,

Black_Leaf_Color is_Symptom_Of Bacterial_Blight

Is_Controlled_By Blitox,

Black_Leaf_Color is_Symptom_Of Bacterial_Blight

Is_Controlled_By Streptocycline,

Black_Boll_Color is_Symptom_Of Tirak

Is_Controlled_By Green_Manuring,

... }


Example Queries Input Query 2: Varities of Cotton

Result: {Cotton has_Variety PAU_626 H

Cotton has_Variety LD_694

Cotton has_Variety RCH_308

Cotton has_Variety LH_1556

Cotton has_Variety MRC_6304

Cotton has_Variety MRC_6301

... }


Example Queries Input Query 3: Round Patches on Leaf

Result: {Leaf_Shedding is_Caused_By Leaf_Blight

has_Symptom Circular_Leaf_Spot

Yellow to Red_Leaf_Color is_Caused_By

Potassium_Deficiency may_Lead_To Leaf_Blight

has_Symptom Circular_Leaf_Spot

Circular_Leaf_Spot is_Symptom_Of Leaf_Blight

is_Controlled_By Mancozeb

Circular_Leaf_Spot is_Symptom_Of Leaf_Blight

occurs_Due_To Potassium_Deficiency is_Controlled_By

Application_of_Potassium

... }


Ontology Validation


Conclusion

Web-based interface is designed where farmer can post his query

Search over ontology is performed which is aided by farmer’s context information from database

Results are ranked and returned as set of paths to the user

Farmer is also advised about his farming activity based on weather predictions


Future Work

Past data of the farmer can be mined to generate rules which would assist in better farming of current crop.

Protégé does not support rule execution, so in order to execute rule we need rule engine. Jess Rule engine is one of them which can be easily integrated with Protégé. Taking an example:

Pest(?x) ^ Interval(?y) ^ Cure(?z) ^ Has_Interval(?x,?y) ^

Has_Interval(?y,?z) →Action(?z)

If a Pest that occurs in Season July and for July season the Cure is Ethion

Spray then Ethion spraying will be done. New observations seen by farmers may be recorded and this knowledge

may be updated in the crop ontology. Current context based search focuses on temporal aspect. It can be

extended to support spatial locality.


ontology based flexible querying system for farmers

Documents

iit bombaymonday

ontology context information

information dependent

iit bombayesagu

system architecturegise

tcsgise lab

irrigationgise lab

themgise lab