33

CHAPTER 2

A METHODOLOGY FOR TEXT-BASED MEDICINAL

PLANTS’ INFORMATION RETRIEVAL

In Chapter 1, we have introduced certain applications of

computers connected to the research work carried out. The literature

survey revealed that inspite of computers used in various fields such

as agriculture, horticulture and forestry, we do not have standard

databases on images of medicinal plants. India has rich treasure of

medicinal plants spread across the country. These medicinal plants

have disease healing properties. Hence, this Chapter deals with the

development of a methodology for text-based information retrieval

from a developed medicinal plant’s database.

2.1 NECESSITY OF MEDICINAL PLANT’S DATABASE

At present, we find a renewed interest in traditional system of

medicine, known as Ayurveda. India has rich repository of medicinal

plants. A proper care is needed to conserve, domesticate and use the

medicinal plants. The efforts are on to improve Ayurveda and

naturopathy as alternate systems of medicine. There is a proverb,

prevention is better than cure. To keep ourselves healthy and to

prevent diseases the knowledge of medicinal plants is necessary. The

documentation on medicinal plants exists in the form of books and

palm leaves. There also exist some online databases with free access.

The data available on the said media is inadequate. A holistic and

34

systematic approach is very much essential to promote productivity of

medicinal plants.

The knowledge of Indian medicinal plants is required by a

common man in view of prevailing home remedies. It would be

difficult, if not impossible, to identify, classify and use the Indian

medicinal plants by novice users. The knowledge of botanical and

colloquial names of medicinal plants are essential. Even though the

medicinal plants are catalogued over the years, still there is a serious

deficiency in terms of conservation, establishment of natural

preserves, location and protection of rare species. There is no

database for wide usage at present. The need for leveraging the

advanced technology is necessary for enabling easy, rapid

identification and retrieval of medicinal plants.

In this connection, we have developed a database and proposed

a methodology for information retrieval from the database. The work is

useful to common people, Ayurveda practitioners, researchers and

other agencies are involving in the use of medicinal plants. The design

of database depends on the data content, accessing methods and user

requirements. We have obtained the basic authentic data from (P.K.

Warrier, [99] for the development of database. We have carried out

literature survey to know about the state-of-the-art in medicinal plant

database.

35

2.2 RELATED WORK

Following is the gist of the literature survey in the area of

application of databases in different fields.

Carsten Kettner et. al., have created relational database system

MEDPHYT, which contains a complete information of European

pharmaceutical and toxicological plants. The database contains the

details of plant botanical characteristics, therapeutic use, etymology,

and synonyms. Apart from the botanical characterization, there exists

information on medical relevant biochemical compounds and their

physicochemical characteristics and toxicological as well as

pharmaceutical facts. The user queries for the required data using

AND Boolean operator. The information is retrieved with matching text

strings about botanical name, biochemy and toxication [76].

Srikanta Bedathur et al., have created a database system, called

BODHI (Bio-diversity Object Database arcHItecture). The database is

specifically designed for catering the special needs of biodiversity

applications. BODHI hosts purely plant-related data. An object-

oriented database is constructed to process queries in multi-domain

such as taxonomy characteristics, spatial distribution and genomic

sequences [119].

A multi-faceted information on medicinal plants of India is aggregated

by Foundation for Revitalization of Local Health Traditions (FRLHT),

Bangalore, India in the form of computerized databases, specialized

36

reports, information products, websites and trade bulletins

(http://www.frlht.org.in/ -Encyclopedia of medicinal plants) [52].

A plant conservation software called “PlantCon” is proposed

(http://www.cimap.res.in), Central Institute of Medicinal and

Aromatic Plants) [51].

(Andres, have proposed an approach to retrieve plant Images using

fuzzy concepts such as fuzzy subset theory and fuzzy thesauri. Few

electronic databases like, MAPA, CABabstracts, AGRIS, AGRICOLA,

PASCAL, MEDILINE, EMBASE, APINMAP are presented in this work

on the database retrieval, certain query processing and access

mechanism are also necessary to achieve time and space complexity.

Hence, to achieve an optimal query processing of medicinal plants

database, the authors have presented few literature on database

access approaches [6].

(Cao and Badia), has proposed an approach for representing query

in the form of graph model, in which each node represents an

operation and an edge associated with weight gives the cost. The

query transformation algorithms are provided for writing nested SQL

queries into equivalent flat queries, which are processed more

efficiently. Algebric optimization rules are used. The selection and

projection operations are pushed down. The join operation is executed

to reduce the number of tuples. The method is efficient and takes

shorter time with increasing sizes of relation [18].

37

(Ceri and Gottlob), have developed a translator that transforms SQL

queries into relational algebra with aggregate functions. The SQL

queries are written in the form relational algebra and query

optimizations are carried out using aggregate functions. (Hellerstein),

[50] has introduced the use of rank parameters to each operation

based on selectivity and cost per tuple. A heuristics algorithm is

proposed for selecting the tuples for execution with lower rank having

less number of attributes [22].

From the literature, it is observed that there exist few databases

on plants giving information on only plants name, chemical

composition and biodiversity. We have also found some medicinal

plants database in the form of books and websites. However, a

standard Indian medicinal plants database is not available to the

researcher, practitioners and common users. Further, it is also

observed that amount of technology application effort gone into this

area is very less. Hence, we have designed a database for medicinal

plants keeping in mind the need for medicinal values of the plants and

image based information retrieval. We have also proposed a

methodology for efficient information retrieval from the database of

medicinal plants.

2.3 DESIGN OF DATABASE SCHEMA

Over the last decade, the database research community is

actively involved in building varieties of ways of data management,

namely, genetic data, criminal data, demographic data and the like.

38

There is no data management work cited on Indian medicinal plants.

A medicinal plant is identified by its morphological characteristics.

Each plant has certain biochemical content through which it is drawn

for therapeutic use. Such information is widely used by the people in

the treatment of diseases. Hence, we have listed plants with their

features of parts and medicinal usage. The Figure 2.1 shows the major

available features about the medicinal plants.

Figure 2.1: Medicinal plant properties

A medicinal plant is identified and classified based on these

features. But, sometimes, the medicinal plants identification becomes

a problem due to overlapping features. Hence, for proper identification

of plants, an unique discriminating features need to be devised. In

Botany, taxonomy expert classifies the plants based on many criteria.

The plants having common characteristics are grouped into a class.

Therefore, to classify plants, it is necessary to identify the

characteristics of the given group. One of the oldest and commonly

used methods of grouping plants is based on physical or

Plant

Leaf arrangement

code

MedicinalProperty

Family code

Leaf shape

Plant picture

Local name

Seed Shape Part Blooming Type

Flower position

Reproduction

Bark Type

Tree Type

Leaf Composition

FruitType

Type Medicinal PropertyType

39

morphological characteristics. These characteristics are shown in

Table 2.1. A plant is identified with its height, type, flowers and leaves.

Table 2.1: Database Schema PLANTS for Medicinal Plants

Sl

No

Coll

No

Hindi

name

Kannada

name

Englis

h name

Scientifi

cname

Distri

bution

About_pant Parts_

used

Property_used

1 AVS

2358

Tikho

r

Kuvehittu

Tavaksiri

Arrow

root

Marant

a

arundin

acea

Linn.

Cultiv

ated

throu

ghout

India

Herb,90-180cm high

leaves ovate-oblong

to ovate, lanceolate

base rounded or

cuneate,tip acute;

flowers white in

clusters,fertile

stamen with

appendage,ovary

one-celled,one-

ovuled.

Under

ground

rhizome

Starch of rhizome

refrigerant, tonic, cough

aphrodisiac, diarrhea

dyspepsia, bronchitis, a

nourishing food for infants,

invalids conval escents. As

ingredient in biscuits,cakes,

puddings, jellies,facepowder

2 AVS

1174

Jasu

m,

Jasut,

Java

Dasavala Shoe-

flower

plant,

hibisc

us

Hibiscu

s rosa-

sinensis

Linn.

Malvace

ae

Throu

ghout

india,

cultiv

ated

An evergreen shrub,

with pale grey or

whitish bark.

Leaves simple,

bright green, ovate,

entire,Serrate

towards the top,

minutehairs eneath;

flowers, solitary and

axillary, pedicels

jointed with pistil

and stamens

projecting from the

centre, anters

reniform or kidney

shaped; 1-celled.

roots,

leaves,

flowers

The roots are sweetish

useful in cough, venereal

diseases, menorrhagia,

pruritus and fever.

The leaves are burning

sensation, hepatopathy,

fatigue, abscesses,

expulsion of the placenta,

skin diseases, fever,

constipation and pruritus.

The flowers are used as

brain tonic, constipating,

urinary astringent and

cardiotinic. Useful in

kapha and pitta, boils,

inflammations,epilepsy,

cerebropathy,dysentery,

haemorrhoids,~

urethrorrhea, diabetes,

cardiac debility,

haemoptysis.

3 AVS 2516

Nim, Nimb

Huccabev, Cikkabevu

Neem tree,

Azadirachta indica

Throughout india,

in deciduous

forests,

also widely

cultivated

A medium to large sized tree, 15-20 m in height and 7 mt having grayish to bark greytubercled bark;

leaves compound, imparipinnte,leaflets

,opposite, elliptic, very oblique at

base,acute tip; flowers cream or

yellowish white in axillary panicles, staminal cylindric, widening above, 9-

10 lobed at the apex;

fruits1-seeded drupes,woody endocarp greenish yellow when ripe,

seeds ellipsoid,

bark,

leaves,

flowers,

seeds,

oil

The bark is astringent,

acrid,antiperiodic,

demulcent, insecticidal,

liver tonic, expectorant,

urinary astringent,

anthelmintic, pectoral and

tonic.

Use vitiated conditions of

pitta hyperdipsia, leprosy,

skin diseases, eczema,

leucoderma, pruritus,

intermittent and malarial

fevers, wounds, ulcers,

burning sensation, tumour,

uterine stimulant and

urinary astringent. They are

useful in fumours, leprosy,

skin diseasesl odontalgia,

intestinal worms,

haemorrhoids, pulmonary

tuberculosis,

opthalmopathy, wounds.

40

≈ : : : : : : : : : : :

≈

The arrangements include parts within a flower, arrangements of

groups of flowers, leaves shapes, patterns of veins, leaves bases and

apices shapes, margins, arrangements of leaflets, stems types, shapes

of fruits, sap color and smell of flowers etc.

We have designed a database for medicinal plants containing

the text, image features and information on medicinal plants in the

form of a relational database. The MS-Access format is used for data

storage and accessing is given through ASP.net interface. The text

database comprises of 500 medicinal plants species of which 164 are

herbs, 125 are shrubs and 159 are trees. The database also includes

52 plant species of climbers and creepers. The features for climbers

and creepers are not considered for image based retrieval. We have

collected images of 174 plant species from forests and parks, of which

32 plants species are herbs, 80 shrubs and 62 trees. However, few

plants species has irregular plant structure and uneven background.

From the images it is observed that these plants species are not

Sl No

Coll No

Hindi name

Kannada name

English

name

Scientificname

Distribution

About_pant Parts_ used

Property_used

500

AVS

2428

Lobiya

Santa

Alasandi Cow

pea

Vigna

unguicu

lata

Throu

ghout

india,

cultiva

t

A herb; leaves

pinnately 3-foliate,

leaflets 7.5-15 cm

long, broadly or

narrowly ovate,

often rhomboidal,

entire or slightly

lobed; flowers white,

pale violet or purple

with a yellow eye,

fruits pods, upto 90

cm long,10-20

seeded; seeds vary

in size, shape and

colour

Seeds The seeds are sweet,

astringent, appetiser,

laxative, anthelmintic,

anaphrodisiac, diuretic,

galactagogue and liver

tonic. They are useful in

vitiated conditions of kapha

and pitta, anorexia,

constipation, helminthiasis,

strangury, agalactia,

jaundice and general

debility.

41

suitable for developed feature extraction. Hence, we have considered

images of 30 plant species of each class, herbs, shrubs and trees for

the work. The leaves images of medicinal plant species are used to

investigate the leaf characteristics. The leaves samples of certain

species have varying shapes and margin features. The sample images

of plants species are shown in Figure 2.2.

(a) (b) (c) (d) (e)

(f) (g) (h) (i) (j)

Figure 2.2: Sample plant species in the database (a) Ocimum basilicum (b) Costus speciosa (c) Solanum melongena(d) Cycas circinalis

(e)Hibiscus rosa-sinensis (f)Ixora coccinia (g)Cocos nucifera

(h)Mangifera indica (i) Nerium indica (j) Prunus domestica

The master database (named as PLANTS) and contains the properties

such as, SlNo (Serial number), CollNo (Collection number), plant

names in Hindi, Kannada, English languages, Distribution,

About_plant, Parts_used and Property_used. The Hindi, kannada, and

English languages are national, Karnataka region and international

languages respectively. The property About_plant includes the

description of leave, flower, fruit and other properties required for the

description of a particular plant. The SlNo is used as primary key. The

Collection numbers are drawn from the book written by P.K.Warrier,

et. al,(2003). In addition to this, scientific names associated with the

42

medicinal plants are also kept as part of the database for unique

identification.

The global schema for medicinal plant database, PLANTS is

represented with Slno, Collno, Hindiname, Kanadaname,

Englishname, Scietificname, Distribution, About_plant, Parts_used,

Property_used. This schema contains few anomalies because of which

information is not inferred for further sub level access operation.

Hence, normalization of the databases is carried out.

2.3.1 Normalization of Databases

The database (PLANT) is not in 1NF (first normal form) and does

not satisfy the atomicity requirement. The schema contains multi-

valued attributes, about_plant (Mark I. Hwand et. al., 2001). The

about_plant is a composite attribute and consists of more than one

data. Hence, the attribute about_plant is decomposed to satisfy

atomicity and uniqueness as shown in Table 2.2. However, this

approach produces more number of duplications, which increases the

redundancy. Once the database is normalized, the 1NF produces

more number of null values if a plant does not contain the attributes

such as flower or fruit. Hence, we have formed separate sub schemas’

such as PLANT_BODY, LEAVES, FLOWERS and FRUIT_SEEDS. These

are self-descriptive and contain atomic values as shown in Tables 2.2

to 2.6. The tables contain no grouping of elements and each row has

unique identifiers such as serial number and collection number,

which forms a concatenated key.

43

Table 2.2: PLANTS database schema in 1 NF with atomic values

Sl

No

Coll

No

Hind

i nam

e

Kannada

name

Englis

h name

Scientificna

me

Distributio

n

About_pant Parts_

Used

Property_used

3 AVS 251

6

Nim, Nim

b

Huccabev,

Cikkabevu

Neem tree,

Azadirachta indica

Throughout india…,

medium to large sized tree, 15-20 mt in height and

7mt grayish bark,tubercled bark

bark, leaves,

flowers,

seeds..

The bark is astringent,

acrid,antiperiodic,….

3 AVS

2516

Nim,

Nimb

Huccabe

v, Cikkabev

u

Neem

tree,

Azadirachta

indica

Througho

ut india, , …

leavescompound,

imparipinnte,leaflets,opposite, elliptic,oblique

atbase,acute tip

bark,

leaves,

flowers

,

seeds,.

.

The bark is

astringent…..,

tonic

3 AVS

2516

Nim,

Nimb

Huccabe

v, Cikkabev

u

Neem

tree,

Azadirachta

indica

Througho

ut india, deciduous

forests, …

flowers cream or yellowish

white in axillary panicles, staminal cylindric,

widening above, 9-10 lobed at the apex;

bark,

leaves,

flowers

,

seeds,

…

The bark is

astringent……,

tonic

3 AVS

2516

Nim,

Nimb

Huccabe

v, Cikkabev

u

Neem

tree,

Azadirachta

indica

Througho

ut india, deciduous

forests, …

fruits 1-seeded

drupes,woody endocarp greenish yellow when ripe,

seeds ellipsoid,

bark,

leaves,

flowers

,

seeds,.

.

The bark is

astringent……,

tonic

Table 2.3: Decomposing PLANTS into 1NF Relation PLANT_BODY

≈ ≈

: : : : : : : :

i … … … … … …

Table 2.4: Decomposing PLANTS into 1NF Relation LEAVES

SlNo CollNo Tip Base Hairy Special Shape

3 AVS 2516 acute

tip

oblique at base with

acute angle

- Leaves compound,

imparipinnte,leaflets,opposite,

Elliptic

≈ : : : : :

≈

j … … … … … …

Table 2.5: Decomposing PLANTS into 1NF Relation FLOWERS

SlNo CollNo Color Petals Shape Others

3 AVS 2516 cream or yellowish white

9-10 lobed atapex

Cylindric flowers in axillary panicles, staminal cylindric, widening above,

≈ : : : : : : ≈

K … … … … …

Table 2.6: Decomposing PLANTS into 1NF Relation FRUIT_SEEDS

SlNo CollNo Fruit_shape Fruit_color Seed_property Overy Seed_no

3 AVS 2516 drupes,woody endocarp, seeds greenish yellow - - 1

SlNo CollNo Type Height_from Height-To Stem Branching Roots

3 AVS

2516

Evergreen Tree 15mt 20mt 7 mt having

grayish bark grey,tubercled bark, ….

- -

44

ellipsoid when ripe

≈ : : : : : : : ≈ L … … … … … …

The subschema LEAVES and FLOWERS violate 2NF (second

normal form). Because every non-prime characteristic, such as,

shape, base and apices of leaves are completely dependent on primary

key. The flower is also dependent on the color and petals. Hence,

further normalization makes the features to be independent so that

leaves and flowers are used to identify uniquely the plants. Therefore,

the databases LEAVES and FLOWERS are normalized into 2NF and

further into sub schemas. Thus, this property helps us to recognize

the medicinal plants. Similarly, other tables PLANT_BODY and

FRUIT_SEED are also subjected to 2NF normalization.

SubSchemas:

LEAVES1(SlNo,CollNo,Tip)

LEAVES2(SlNo,CollNO,Base),LEAVES3(SlNo,CollNo, Shape)

FLOWER1(SlNo,CollNo,Color),FLOWER2(SlNo,CollNo,Petals)

FLOWER3(SlNo,CollNo,Shape).

2.4 QUERY PROCESSING

The database design contains two major components, the

database and the query processor. The data base design writes the

data to and reads data from the data store. It manages record level

access with minimum cost. The query processor accepts Structured

Query Language (SQL) query from user, executes the query and

produces the relevant information. A typical query processing on a

Medicinal Plant Database for retrieving the required information is

45

shown in Figure 2.3. The typical stages through which a query

proceeds have the following functionality.

Figure 2.3: Query execution using heuristic rule

The query parser is designed to check the validity of the query

syntax. A syntactically correct query is represented translated into an

internal form. We have used a query tree, which is a useful

representation for relational calculus expressions. The query

standardization examines all the algebraic expressions that are

equivalent to the given query and chooses the one that has the least

cost. By cost, we mean the number of tuples executed. The Code

Generator transforms the access plan generated by the

standardization process into calls to the query processor. The query

processor is responsible for actual execution of the query.

The queries may have alternate execution plans, which are

equivalent in terms of the results, but there is variation in costs. The

cost is expressed in terms of amount of time needed to execute a

query. We have used two approaches for querying, Heuristic approach

Relational Calculus

Query Parser

Query Standardization process

Code Generator/Interpreter

Query Processor

Record–at-a-

time calls

Relational Algebra

Query Language (SQL)

Retrieved information

46

and Cost Based approach. The cost-based approach uses the

knowledge of the underlying data and storage structures, which is not

considered in this work. The goal of our approach is to retrieve the

image based information as efficiently as possible. We have used

heuristic approach for medicinal plants information retrieval.

The heuristic approach is rule-based method for producing an

efficient execution plan. The selection, projection and join operations

are used to convert a given SQL query into standard relational

algebraic expression. A query tree represents the algebraic query.

During execution, query tree is transformed into an equivalent tree by

rearranging the operations without loosing information (Ceri.S.and

Gottlob [22]. For the purpose of rearrangement, a heuristic rule is

used, where cascaded selects are broken into individual selects. In

addition, the selects and projects are pushed down so that query

results in minimum number of tuples during inner query and join

operation executions. Consider the query “Retrieve the medicinal

plants with ovate leaves”. The inner query is written as under:

σ shape = ‘ovate’ (LEAVES)

The query has resulted in retrieval of 30% of plants from the

database. From the detailed execution of a query with different

alternatives, the characteristics we observed that the number of tuples

returned for the characteristic ‘root’ is minimum and for ‘leaves’ is

maximum. We have concluded that mainly leaves than their other

parts characterize all the plants. Thus, leaves help for image based

47

retrieval of medicinal plants. For an illustration, the number of tuples

obtained after query execution on different parts of plants on plants

database of 100 plants are given in Table 2.7.

Table 2.7: Plants Database parameters after query execution

From the Table 2.7, it is revealed that ‘plant-body’ and ‘leaves’

are the most prominent parts in the identification of plants. Hence,

plant-body dimensions and leaves features are used for further image

based retrieval of information. For efficient and accurate access of the

information of plants, consider an illustration for the execution of the

query “Retrieve the plants with Kanada name and SlNo and having

ovate leaves”. The query is written in relational algebra as given

under.

∏ Kanada_name, slno ( ( σ shape= ‘ovate’(LEAVES)) ∞ LEAVES. slno = PLANTS. slno PLANT)

The possible three alternatives for execution of this query are shown

in Figure 2.4.

The push down strategy is adopted for the above query given in Figure

2.4. The query processing strategy by T3 is effective than T2 and T1.

The selection of ovate leaves is moved down at the leaf level of the

Relation Cardinality

PLANTS 100

FLOWERS 84

LEAVES 100

FRUIT_SEED 80

PLANTBODY 100

48

∏ Kanada_name, slno ∏ Kanada_name, slno ∏ Kanada_name, slno

∞ Slno=Slno σ Shape= ‘ovate’ ∞ Slno=Slno

σ Shape= ‘ovate’ PLANTS ∞ Slno=Slno ∏ slno ∏ Kanada_name,slno

LEAVES

LEAVES PLANTS σ Shape= ‘ovate’ PLANTS

LEAVES T1 T2 T3

Figure 2.4: (i) T1 and T2 are Query Trees (ii) T3 Efficient Tree

Tree and the projections and joins are rearranged so that

minimum number of tuples is obtained giving a good selectivity factor.

Based on selectivity and dilation factor efficient query execution plan

is generated. From this approach, we infer that the proposed approach

gives a good result for any type of plant species.

2.4.1 Query based retrieval of medicinal plant species

In order to retrieve the medicinal plants from the database with

respect to user needs a view-based approach is developed. The

characteristics differ from plant to plant.

We have established the relations in the view level. Many entries

in database PLANT are NULL as their values are either well defined or

exist. It not only suppresses the insert or update or delete anomalies

but also minimizes the normalization overhead. The main purpose of

the work undertaken is to uniquely identify the medicinal plants,

immaterial of normalization of the database for optimum processing

and storage. A typical example of common queries is given as in the

Box 2.1.

49

The variable names (preceded by @) represent the pass values

because the statements are all compounded and the ‘*value*’ format

detects the key words evenwhen they are embedded in a sentence

along with other words. Since “height” attribute is numeric, absolute

values need to be passed in order to detect the medicinal plants with

specific heights. The developed database design allows access to the

plant information by joining more than one subschema. Hence, we

have accessed the plant information with the combination of more

than one feature.

Therefore, the user expresses a detailed query, using

conjunctions and disjunctions of the medicinal plants features.

Moreover, in the case of medicinal plants, we use continuous

property, such as the leaf size, or an interval data such as between

2cm and 5 cm or a linguistic variable such as small, medium and tall.

It is also common to use modifiers such as very small to attribute a

different level of importance. An illustration of complex query

execution on global and fragmentation schemas for medicinal plant

database is given in Box 2.2. The query retrieves the medicinal plant’s

English name that has white flower, conical fruits and herbaceous.

We have presented some preliminary results of experimentation

on the designed database. Basically plants are classified as herbs,

shrubs and trees. Hence, to find the percenatage of these classfication

large numbers of queries are executed on the database. Plants are

divided mainly into three categories herbs, shrubs and trees using the

50

query as follows.

Box 2.1. Example of common queries

SELECT * from PLANT_BODY where type = ‘herb’.

SELECT * from PLANT_BODY where type = ‘herb’.

Figure 2.5 gives the percentage of retrieval of different medicinal

plants from the database. Pie chart indicates percentage of plants

grown in India in following categories of growth forms, herbs, shrubs

and trees.

(i) Classification based on the base tip of leaves SELECT[plants].[kanada_name],[plants].[hindi_name],[plants].[sci_name],[plants].[distribution],

[plants].[parts_used], [plants].[about_plant]

FROM plants, leaves

WHERE([plants].[slno]=[leaves].[slno]) And ([plants].[collno]=[leaves].[collno]) And (([leaves].[base] Like

[@base]) Or ([leaves].[tip] Like [@tip]));

(ii) Classification based on flower color

SELECT[plants].[kanada_name],[plants].[hindi_name],[plants].[sci_name],[plants].[distribution],[plants

].[parts_used],[plants].[about_plant], [flowers].[color], [flowers].[shape]

FROM plants, flowers

WHERE(([plants].[slno]=[flowers].[slno])And[plants].[collno]=[flowers].[collno])And (([flowers].[shape]

Like [@shape]) Or ([flowers].[color]=[@color])))

ORDER BY [COLOR];

(iii) Classification based on fruit shape description

SELECT *

FROM fruit_seed

WHERE len(fruit_shape)>1;

(iV) Classification based on fruit seed

SELECT[plants].[kanada_name],[plants].[hindi_name],[plants].[sci_name],[plants].[distribution],

[plants].[parts_used], [plants].[about_plant]

FROM plants, fruit_seed

WHERE(([plants].[slno]=[fruit_seed].[slno])And([plants].[collno]=[fruit_seed].[collno])And(([fruit_seed].[fr

uit_shape]Like[@Fruit_shape])Or [fruit_seed].[seed_property]=[@Seed])));

51

Box 2.2: Typical query execution on global and fragmentation schemas

Box 2.2 Different schema of the database

Hence, the conjugate queries usually require plant classification based

on plant type and their features. The shrubs are in small numbers

and considered for query processing.

Global schema

PLANTS(slno,collno,hindiname,kanadaname,englishname,scietificname,

distribution,about_plant, parts_used,property_used)

Subschema

LEAVES(slno,tip,base,spacial,hairy, shape)

FRUIT(slno,fruit_shape,fruit_color,seed_property,ovary,seed_no)

FLOWER(slno,color,shape,petals,others)

PLANTBODY(slno, type,height_from,height_to,stem,branching,roots)

Fragmentation Schema

PLANT_FL = SL FLOWER.color= ‘white’ PJslno (FLOWER)

PLANT_FR = SL FRUIT.shape= ‘conical’ PJ slno (FRUIT)

PLANT_TP = SL PLANTBODY.type= ’ herb’ PJ slno (PLANTBODY)

PLANT_TEMP = (PJ slno (PLANT_FL JN PLANT_FL.slno = PLANT_FR.slno PLANT_FR ))

PLANT_RECN = PLANT_TEMP JN PLANT_TEMP.slno = PLANT_TP.slno PLANT_TP

PLANTNAME_RECN_UNIQUE = PJslno,Englishname(PLANTS JN(PLANTS.slno =

PLANT_RECN.slno ) PLANT_RECN)

Plants_with_white_Flower={2,3,4,63,71,72,75,10,83,54,84,62,8,33,55,16,1,7,9,

13, 18,34, 5,11,6,20,61}

Plants_ With_Conical_Fruits = { 7,54 }

Herb_Plants={1,2,4,5,6,12,19,25,27,32,33,36,37,42,45,49,54,55,60,65,66,74,7

5, 76,77,82,84}

White flower∩ Conical_Fruits ∩ Herb_Plants = {54}

Therefore plant 54 is a herb which possesses a conical fruit with white flower.

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Figure 2.5: Percentage of medicinal plants with categories of growth forms

Figure 2.6: Classification of medicinal plants based on the

plant properties

From the graph shown in Figure 2.6, we conclude that leaves, height,

flower, branching and fruits are the most common properties which

are defined for majority of the plants and amongst them leaves are the

most well defined properties in the recognition of plants. Hence, the

retrieval percentage obtained is high.

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Figure 2.7: Classification based on the plant sub properties

The plants with properties given in Figure 2.6 constitute 90% of the

entire database. The climbers and creepers constitute only 10% of the

medicinal plants and are also identified by leaf and tendril properties.

Moreover, there are some plants, which have the same global but

varies with local properties. Hence, in order to classify, we have found

out some minute local properties specific to a particular plant. Such

properties are used to differentiate between similar plants. Figure 2.7

gives the percentage of retrieval versus sub properties. The medicinal

plants are categorized based on the sub properties combined in a

single query. From the experimentation, we have observed that most

of the plants are well defined by the common properties.

2.5 INDIAN MEDICINAL PLANT DATABASE ON THE WEB

In order to make the database online, it is web enabled and

implemented using ASP.Net and Web Matrix Server. The developed

design schema shows how easily the plant properties are fragmented

54

and analyzed. The OLEDB database interface is considered, which

involves the MS-Access and core component ADO.Net integrated with

Web Matrix. The connection string provides a direct path to MS-

Access, which is located on the web directory. For record

representation, a serialized object (DataGrid) and one non serializable

object (DataReader) are used. The grid is used for displaying and

joining results, where the reader is used to fetch independent records.

Summary

We have developed a standard database for Indian medicinal

plants from using various sources such as books, images and utility

package. The proposed methodology has given 85% accuracy for a

unique property. The database facilitates retrieval of plants from their

properties. Hence, the developed design methodology helps in the

preparation of home remedies. The work focuses on combining text

and image for image based information retrieval. Ultimate goal is to

develop a machine vision system for medicinal plants considering

their properties and sub properties for retrieval.