Agriculture Decision Support System for Pakistan

Vol.1,Issue 2.(pp.01-25)|2013 eCanadian Journal of Technology and Scientific Management

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Agriculture Decision Support System for Pakistan

S. M. Abbas

Department of Computer Science, National University of Computer & Emerging Sciences (FAST), Islamabad,

Pakistan.

[email protected]

R. F. Ahmad Department of Computer Science, National University of Computer & Emerging Sciences (FAST), Islamabad,

Pakistan.

G. Mujtaba


Pakistan.

A. Ahmad


Pakistan.

W. Shahzad


Pakistan.

A. N. Naqvi

Department of Biological Sciences, Karakorum International University, Gilgit Baltistan, Pakistan.

[email protected]

ABSTRACT

The agriculture Decision Support System is intended to ease up the job of different agricultural estimations and

procedures by the automation of processes including future predictions. It acts as a unified model for Agricultural

world as data is coming from diverse sources and it is being processed and integrated at one platform so that the

final decision is taken by the Agro-DSS. It aims at using Agro meta-data for analysis, decision support, research,

education, and ultimately, for solving agriculture problems. Conventionally, agriculture analysis and the decision

making process is based upon the expert judgment. Agro-DSS employs IT tools and techniques and offer decision

makers an advantage over the traditional farmers. To achieve this objective data is collected, digitized, cleansed and

used in Agro-Data ware house. The wealth of the agriculture data is then presented to the Agro-DSS users to aid in

making strategic analysis and for decision-making process. The Collection of the data will be continued during the

execution of project. The system can also be used as a trainer for the beginners. It will increase the probability of

gaining profit and decrease the loss probability in agricultural field. Moreover this system will be made available to

all the major stakeholders in agriculture sector including policy makers at the top most level and the farmer at the

lowest level.

Key words: Agriculture, Decision, Support, System, Agro-DDS

INTRODUCTION

Pakistan’s agricultural sector accounts for about 70 percent of rural household income and nearly one-quarter of

national GDP. We have no such application which links IT with agriculture. So that farmers could maximize their

profits. IT is involved in every field to improve the performance, like banking, air- lines, and daily other

transactions. The farmers got land which is their only resource and they want to utilize it best as much as possible.

The significant loss in agriculture sector on the account of pest attacks, hostile weather conditions and most

importantly, poor on-farm and off-the-farm management practices has rendered it insufficient and non-productive.

As a result Pakistan ranks low on the average yield map of the world. The aim of agriculture decision support

system project is to meet these challenges by providing better data management through continuous analysis of

mailto:[email protected]

mailto:[email protected]


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dynamics of agriculture. Such data driven decision support system helps decision making at various levels such as

agriculture policy makers and most importantly farmers. They will be provided a guideline by Information Systems.

In abroad where IT is involved in agriculture their production has dramatically been improved. The motivation

behind of this project is based on the fact that during the last few decades the economy of Pakistan is being

continuously declined. It is needed to be improved by using technology, and by involving IT into agriculture.

First China started focusing on such system which may benefit their agriculture resources and then for the last few

years a lot of work has been done in this field. Data Mining is being applied for the meaningful and efficient

output.It helps the integrated agricultural data including pest scouting, and metrological recordings for optimization

(and reduction) of pesticide usage. Clusters reveal interesting patterns of farmer practices along with pesticide usage

dynamics and hence help identify the reasons behind this abuse of pesticides (Abdullah A, et.al. 2004). The main

fence to the development of Agro-DSS is the availability of the data and to integrate all the data in an efficient

manner.

Data mining technology is becoming more and more powerful in the decision of modern agricultural logistics

management (Liu dejun, L. and Guangsheng, Z. 2007). The data mining tools including BI and AI are involved to

extract best and effective outputs. Despite the profit loss analysis of Agro-DSS there is also another domain in the

Agriculture which is the logistics management that includes management, supply and prices of logistics.

Apply data mining to a soil science database to establish meaningful relationships and patterns (Leisa Armstrong,

et.al. 2005). In Pakistan most of the places have never been considered for the soil fertility tests. So that some think

meaningful could have been extracted from that or to develop soil fertility relationship with other aspects such as

weather, irrigated areas etc.

Induce a classification system capable of sorting mushrooms into quality grades and achieving accuracy similar to

that attained by human inspectors (Kusabs et al 1998). Agro-IS, will help rural farmers to connect to the urbanized

information systems through sophisticated information gathering mechanism. The notifications are made to the

farmers on demand, along with the precautionary notifications about weather, pest and water management. Such

knowledge, will not only improve the relevance of the responses generated by Agro-DSS for farmer queries but also

minimize the gap between the major stakeholders i.e. lower end farmers and the Government (). The problem which

is of grave concern is the availability of data which is collected is either so rough that cannot be exploited or Ahmad,

Q. and Sarwar, I. 2008available in a poor represent able format.

A data warehouse is needed to store all the agriculture related data to maximize the earning like focusing on cotton

Pakistan is the 5th

largest cotton producer. To monitor cotton growth, different government departments and

agencies in Pakistan have been recording pest scouting, agriculture and metrological data (Ahsan Abdullah 2007).

Motive of Research

Motive of research is to provide all the data in to the user according to his requirement. If a specific crop is to be

grown on a specific land. Agro-DSS explains that weather that crop can or cannot be grown into this land on the

basis of different aspects e.g. Climate, fertility, water level. If it is possible to grow then selection of suitable

insecticides and pesticides would be done. Profit Loss analysis is also an important aspect. If there isn’t any

possibility to grow then an additional suggestion is provided for further possibilities and their after effects.

Agro-DSS will cover all those aspects which are not covered e.g.

Crops : Specific Crops which can be grown in different places

Places : Weather the place is suitable for that crop

Rain water : Rate

Time : Actual time for using the insecticides and pesticides

Pesticides : Suitable

Fertilizer : Type & amount

Supply of water : Sources available other than natural

Any possibility if any problem : If any problem occurs what’s the possible way to solve

Profit : Profit Loss

Loss : Prediction

Suggestion : Knowing all realities a software suggestion


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Prediction : Future climate

Table 1: Problems concerns and current status

Goal Priority Problems and Concerns Current Solutions

Crop High Check whether the specific crop can be grown

in that place or not. If yes go to next step

otherwise generate a No.

Existing soft wares are not that much precise

regarding this

Fertilizer High Quantity and multiple types of fertilizers on the

basis of crop

Don’t provide information about more than one

suitable fertilizers

Supply of

water

High Cover the recourses of water other than rain

fall

Some tell about the rain estimation but don’t

tell that in the case if there is no rain what are

the other resources of water. i.e. Tube wells

Climate

Prediction

Low Also Concerns with Future climate prediction

and constant updation of the software

Existing software tells the existing weather

conditions but what will be done if at once the

conditions change. In such case you much also

know about future climate and conditions

Pesticides High Selection of suitable pesticides for the specific

crop

They only tell about insecticides for the crop

but overall improvement can be achieved by

covering all aspects

Profit & Loss

estimation

High Calculate profit loss estimation on the basis of

all the above given aspects.

They provide all primary information but there

is no final calculation of profit loss percentages

Business Opportunity

Existing Agriculture Software products are not adaptable to the customer's business, in terms of effectiveness,

competitiveness and varying conditions. In addition, they do not scale well as terminals and business increase and

none of them covers the whole scenario in detail for the ease and comfort of the client. Hence, people face losses in

investments just because they don’t have proper idea of the whole scene. Due to which they suffer individually as

well as a family. Hence agriculture department is suffered which eventually leads to the economic crises in the

agricultural field of Pakistan. Traditional agriculture software systems are inflexible, and not in that much detail,

hence the client is not able to have an idea about those aspects which will be known to him for better production,

improvement in methodology, and profit. This leads to ineffectiveness of the software. And if it is not that much

effective and creates no major difference after using it then there is no solid reason to use that and allocate recourses

to operate it.

User Summary

Actor: Operator, Type: Primary, Goals: Software Login, logoff, Perform Authentication

Actor: Admin, Type: Primary, Goal: Access to the data and CRUD

Actor: Client, Type: Primary, Goal: Utilize software output

Note: Clients can be the farmers or any other organization seeking help.

Use Cases

Use case 1:

Use case Name: Login & logoff to system

Scope: Access to the system


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Level: High-level

Primary Actor: Operator

Description: Allow the admin to insert his data (CRUD).

Use case 2:

Use case Name: CRUD Crops and Places data

Scope: Add, delete, update and search the record of crops and places

Level: High-level

Primary Actor: Admin

Description: All the record regarding crops and places will be tackled

Use case 3:

Use case Name: CRUD Rain forecast data

Scope: Entire record of Rain forecast will be tackled

Level: High-level


Description: Add, delete, update and search the data of Rain forecast

Use case 4:

Use case Name: CRUD other resources (water supply) data

Scope: Entire record of other resources (water supply) will be tackled.

Level: High-level


Description: Add, delete, update and search the data of other resources (water supply)

Use case 5:

Use case Name: CRUD Climate data

Scope: Entire record of Climate will be tackled.

Level: High-level


Description Add, delete, update and search data of Climate.

Use case 6:

Use case Name: Estimate Profit loss

Scope: Entire record of profit loss estimation will be tackled.

Level: High-level


Description Estimate the profit loss percentage by using the saved database records

Use case 7:

Use case Name: Give suggestion

Scope: All suggestion will be tackled.

Level: High-level

Primary Actor: Weather forecast data Administrator


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Description: Generate suggestion if loss probability is higher. It will lead the client to the others possible

and profitable ways to grow this crop on this place

Use case 8:

Use case Name: Find District wise crops

Scope: District wise data

Level: High

Primary Actor: System

Description: Generates all crops that are grown in different districts

Use case 9:

Name: Show Sowing Method specific crops

Scope: Methods of sowing all crops

Level: High


Description: Show the sowing method of any crop on demand.

Use case 10:

Name: Check weather updates

Scope: All weather data

Level: High


Description: Weather updates district wise on daily bases including rain forecasts.

Use case 11:

Name: Find crop Rate

Scope: All selling rate record

Level: High


Description: Show the rate of specific crop in a specific regio

Use case 12:

Name: Find irrigated regions

Scope: All irrigated regions record

Level: High


Description: Show that which regions are irrigated so their worth is higher

Use case 13:

Name: Find other sources

Scope: All other sources of water

Level: High


Description: Show if any sources of water other than rain


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Use case 14:

Name: Find Region wise temp

Scope: All temperature data

Level: High


Description: Show average temperatures, humidity level of any region with estimation of forecast also.

Use case 15:

Name: Manage time

Scope: Cover important of time as every aspect

Level: High


Description: Timeline for watering, pest attack times, pesticide and fertilizers usage time.

Use case 16:

Name: Show average production

Scope: All production of crops data

Level: High


Description: makes available province and district wise production data for decision makers to use.

Use case 17:

Name: Identify Area

Scope: Data related to the cultivated area of a region

Level: High


Description: Tells about the data of total cultivated area and also identify how much area which is needed

to be cultivated of a specific district to increase productivity.

Use case 18:

Name: Analysis of Price

Scope: Prices of all inventories

Level: High


Description: Intense analysis of tentative investment & selling prices of crops with respect to the region


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Use Case Model

Figure 1: Use case Model

Product Perspective

If farmer wants to grow a specific crop to a specific land, Agro-DSS will explain weather that crop can or

cannot be grown into this land on the basis of different aspects e.g. Climate, fertility, water level. If it is

possible to grow then selection of suitable insecticides and pesticides would be done by Agro-DSS. It will

also cover the Profit Loss possibility for the precision of the decision. If there isn’t any possibility to grow

then an additional suggestion will provide the further possibilities and there after effects.

Provide all the Data to the user according to his requirement. The input is usually a crop and a place. If he

wants to grow a specific crop to a specific land, Agro-DSS will explain weather that crop can or cannot be

grown into this land on the basis of different aspects e.g. Climate, fertility, water level. If it is possible to

grow then selection of suitable insecticides and pesticides would be done by Agro-DSS. It will also cover

uc Use Case Model

check status

check weather

check pesticide

check av erge rain

fall check supply water

check climate

perdiction

check best cropecheck loctation

manage data

CRUD Weather

CRUD av erge rain fall

CRUD Climate

CRUD of pesticide

CRUD of Fertilizer

check fertilizer

CRUD of crop

CRUD of suply water

CRUD of location

Operator

Admin

calculate suggestion

prov ide profit/lose

Farmer

check

check

check checkcheck

check

check

«include»

«include»

«include» «include»

«include» «include»

«include»

«include»


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the Profit Loss possibility for the precision of the decision. If there isn’t any possibility to grow then an

additional suggestion will provide the further possibilities and there after effects.

Ranking of Functional Requirements

Ranking Use Cases

Some qualities and characteristics to rank use cases:

(a) Significant impact (architecture)

(b) Significant info (reports)

(c) Risky, time critical or complex

(d) New, research oriented

(e) Basic/primary processes

(f) Support increased

(g) Revenue/decreased costs

Table 2: Rank use cases

Use Case a b c d e f Sum

Manage crop & Place 8 8 5 7 9 9 46

Evaluate water level 7 7 5 5 7 8 39

Evaluate Best time 7 5 8 4 5 8 37

Manage Fertilizer 6 6.5 8 6 2 6 34.5

Manage pesticides 6 4 4 7 2 6 29

Evaluate profit/loss 7.5 7 4 3 8 10 39.5

Table 3: Ranking Scheme

Use cases Rank Justifications

Manage crop &

Place

High Check whether the specific crop can be grown in that place or not. If yes go to next

step otherwise generate a No. We can’t move ahead without confirming this phase. It

contains very significant information and is a basic process.

Evaluate water

level

High Check the average rain estimation of that specific place and whether is suitable to

grow at this rain fall level. Significant impact on the other use cases like Manage soil

fertility and Evaluate revenue.


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Use cases Rank Justifications

Evaluate Best

time

High Provides you the perfect time in the season to grow the required crop in that place.

Suitable season & time is another foundational process. It directly influences the

Estimate profit loss Use Case. In this use case time is very critical thing in fact.

Manage

Fertilizer

High Although this use case influences the production, soil fertility, estimation of profit but

still is not considered as very basic process.

Evaluate

profit/loss

High Calculate profit loss estimation on the basis of all the above given aspects. This Use

Case information is the core important & significant information. It is not the basic

primary process but it uses all the result of all primary processes to generate its

approximate result.

Fully Dressed Use Cases Format

Table 4: Manage Crop and Place

Name Manage crop & Place

Scope

Weather the place is suitable for that crop.

1- Check place

2- Manage Crop and place record

3- Farmer

Primary Actor Farmer

Stakeholders and Interests

(Farmer): To prevent themselves from expected loss

Any other Agricultural organization :

Other Agricultural organizations also consult to this software to prevent loss

probability.

Preconditions Input of crop and place where to grow.

Farmer

Post conditions

Evaluate Soil fertility

Evaluate water level

Manage Fertilizer

Manage Pesticides

Evaluate profit/loss

Main Success Scenario Farmer asks the system for the place to check whether the crop is valid for this place

or not and system generates a yes or no.


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Extensions

If the record doesn’t exist in the database wait for updating data.

If growth of the crop is not possible in particular place then close ().

If possible then proceed.

Special Requirements We want right place for right crop and limitations the record was not found or updated

in the database.

Variations in Technology

and Data Changes if transfer from sql to oracle records system

Frequency of Occurrence Mostly

Depends upon the entry


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Activity Diagram

Figure 2: Manage crop & Place

act Primary Use Cases

Farmer

Fill aplication for best

crop/place

Check data existance

Information for

crop and place

Data of application

Confirmation

Check crop for suitable

place

Create report

Application

Informatiopn exist

Crop suitable

Report

Suggestion

end

[NO][YES]

[YES]

[NO]


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Table 5: Estimate profit and loss

Name Estimate Profit loss

Scope

Calculate profit loss estimation on the basis of all the above given aspects.

Involving data from all use cases and process it then to generate output.

Level

User-goal of farmer:

Estimation of profit loss

User-goal of Operator:

To enter correct data so that the estimation would be valid

User-goal of other organization:

Estimation of profit loss


Stakeholders and

Interests

Farmer

Operator

Other organization’s Consultants


Farmer

Post conditions

Output is generated that will identify the estimation of rain water level

Then process all other use cases

Main Success

Scenario

Actors Perform inputs

System Generator processed output (profit loss %)

Extensions

If Profit probability is higher, then generate report.

If the loss probability is high then go into Evaluate Suggestions.

Special Requirements

We want right approximate estimation and limitations is that record was not found or updated

in the database.

Or if no such apparatus is present here to check all the use case process’s output.

Variations in

Technology and Data

Changes if transfer from sql to oracle records system

New apparatus imported

Frequency of

Occurrence

Mostly

Depends and use the data previous Use cases (given above).

Miscellaneous

Research to provide more precise answer


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Activity Diagram

Figure 3: Estimate profit and lose


Farmer

Fill application form for

profit calcultion

Application

Check related data

information exist

Confarmation

Data from

database

Calculate profit loss

Profit Loss

Create profit report Create suggestion report

Profit report

suggestion report

end

[YES]

[NO]


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Table 6: Evaluation of water level

Name

Evaluate water level (rain)

Scope

Suitable Rain water Rate

1- Check water level data status

2- Manage water level record

3- Manage Stored data

4- Manage profit loss

Level

User-goal of farmer:

Estimation of rain water level

User-goal of Operator:

To enter correct data so that the estimation would be valid

User-goal of other organization:

Estimation of rain water level


Stakeholders and Interests

Farmer

Operator

Other organization’s Consultants


Farmer ( if it is possible then)

Post conditions

Output is generated that will identify the estimation of rain water level

Evaluate water level

Manage Fertilizer

Manage Insecticides

Manage Pesticides

Manage Fungicides

Evaluate profit loss

Main Success Scenario Actors Perform inputs

System Generator processed output ( rain water level)

Extensions If the rain water level is suitable then proceed

If not then go into Evaluate Suggestions.

Special Requirements

We want right approximate estimation and limitations is that record was not

found or updated in the database.

Or if no such apparatus is present here to check all the use case process’s

output.

Variations in Technology and Data

Changes if transfer from sql to oracle records system

New apparatus imported

Frequency of Occurrence Mostly

Depends and data Entry.

Miscellaneous Research to provide more precise answer


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Activity Diagram

Figure 4: Evaluate water level


Farmer

Fill application form for

water lev el

Check water information

exist

Application

Data of rain

information

store application

data

information exist

Confirmation

Estimate Av erge rainfall

Suitable for area

Report

Suggestion

End

[YES]

[YES]

[NO]

[NO]


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ER Diagram

Profit loss

PK PL_id

Selling_prize

T investment

FK1 O_id

Farmer

PK F_id

District_name

F_name

F_phone

Nic

Total Area

Operator

PK O_id

O_name

O_phone

Fertilizer

PK Fr_id

Fr_name

Fr_timeline

Fr_cost

FK2 O_id

CropDistric

PK CD_id

FK2 C_id

FK1 D_id

C_area

C_production

C_cost

CropFertilizer

PK CF_id

FK2 C_id

FK1 Fr_id

Fr_method

Division

PK Div_id

Div_name

Province

FK1 O_id

CropPest

PK CP_id

FK1 C_id

FK2 P_id

P_method

P_spraydate

Crop

PK C_id

C_name

L_name

Sowing_method

Sowing_date

Planting_date

Hervesting_date

Watering_date

FK1 O_id

C_typre

District

PK D_id

D_name

FK1 Div_id

Weather

PK RFD

R_monthly

month

Max_temp

Min_temp

sunshine

WeatherDistrict

PK WD_id

FK1 D_id

Humidity

Max_temp

Min_temp

Sunshine_hours

Pesticide

PK P_id

P_name

P_cost

FK1 O_id

Soil

PK S_id

Ph_level

Organic_material

FK1 O_id

Figure 5: ER Diagram


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Class Diagram

Figure 6: Class Diagram

class Class Model

Fertilizer

- Fr_cost: float

- Fr_id: int

- Fr_method: String

- Fr_name: int

- Fr_timeline: String

+ getCost(...)() : void

+ getMethod(...)() : void

+ getTimeline(....)() : void

Pesticides

- P-spraydate: String

- P_cost: float

- P_id: int

- P_method: String

- P_name: String


+ getMethod(...)() : void

+ getSprayDate(...)() : void

District

- D_id: int

- D_name: String

+ getDivision(...)() : void

Crop

- C_area: float

- C_cost: float

- C_id: int

- C_name: String

- C_production: float

- C_Type: String

- Hervesting_date: String

- L_name: String

- Planting_date: String

- Sowing_date: String

- Sowing_method: String

- Watering_date: String

+ getArea(....)() : void


+ getHervestingDate(.....)() : void

+ getPlantingDate(...)() : void

+ getProduction(....)() : void

+ getSowingDate(....)() : void

+ getSowingMethod(....)() : void

+ getWateringDate(....)() : void

Soil

- Organic_material: float

- Ph_level: float

- S_id: int

+ getOrganicMaterial(....)() : void

+ getPhLevel(.....)() : void

Division

- Div_id: int

- Div_name: String

- Province: String

Weather

- D_id(fk): int

- Max_temp: float

- Min_temp: float

- R_monthly: float

- Sunshine_hours: float

+ getMaxTemp(....)() : void

+ getMinTemp(....)() : void

+ rainMonthly(....)() : void

+ sunshineMonthly(....)() : void

Farmer

- District_name: String

- F_id: int

- Total_area: float

+ deleteFarmer(.....)() : void

+ insertFarmer(......)() : void

+ updateFarmer(.....)() : void

Operator

- O_id: int

- Password: String

- Username: String

+ login(String, String) : boolean

+ logoff() : void

Person

- Adress: String

- Age: float

- DateOfBirth: String

- Name: int

- Nic: String

- Phone#: int

ProfitLoss

- date: String

- time: String

- totalProfit: float

+ claculateProfit(....)() : void

+ getBestCrop(string) : void

+ getBestDistrict(string) : void

+ getBestFertilizer(string) : void

+ getBestPesticide(string) : void

+ getSuitableSoil(string) : void

+ getWeatherUpdates(....)() : void

*

Calculates

*Entertain

1

1

1

1

1

1

1

1

1

*

based on

based on

based on

based onbased on

based on

1 1

1


18

Domain Model

Figure 7: Domain Model

class Class Model

Fertilizer

- Fr_cost: float

- Fr_id: int

- Fr_method: String

- Fr_name: int

- Fr_timeline: String

Pesticides

- P-spraydate: String

- P_cost: float

- P_id: int

- P_method: String

- P_name: String

District

- D_id: int

- D_name: String

Crop

- C_area: float

- C_cost: float

- C_id: int

- C_name: String

- C_production: float

- C_Type: String

- Hervesting_date: String

- L_name: String

- Planting_date: String

- Sowing_date: String

- Sowing_method: String

- Watering_date: String

Soil

- Organic_material: float

- Ph_level: float

- S_id: int

Division

- Div_id: int

- Div_name: String

- Province: String

Weather

- D_id(fk): int

- Max_temp: float

- Min_temp: float

- R_monthly: float

- R_yearly: float

- Sunshine_hours: float

Farmer

- District_name: String

- F_id: int

- Total_area: float

Operator

- O_id: int

- Password: String

- Username: String

Person

- Adress: String

- Age: float

- DateOfBirth: String

- Name: int

- Nic: String

- Phone#: int

ProfitLoss

- date: String

- time: String

- totalProfit: float

*

Calculates

*Entertain

1

1

1

11 1

1 1 1

*

based onbased on

based on

based on based onbased on

1

1


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Operation Contracts

1. Manage crop and place (enter item)

Name: Enter crop & Place (Crop_Name: String, Crop_ID: Integer, Place Name: String, Place_ID:

Integer)

Responsibility: Enter the data of crop and place

Type: System

Cross Reference: Use case: Manage crop & Place

Notes: Use superfast Oracle database access.

Exceptions: If the crop or place in not valid or not saved in the database

Output:

Pre-conditions: Access to the system (Login).

Post-conditions:

A crop and place instance Mpc was created.

Mpc was associated with the current manage crop and place.

Mpc Crop_Name became Crop_Name .

Mpc Crop_ID became Crop_ID.

Mpc. Place_Name became Place_Name.

Mpc. Place_ID became Place_ID.

MPc was associated with a manage crop and place, based some criteria match.

Manage crop and place (end)

Name: End enter crop and place

Cross Reference: Use case: manage crop and place

Pre-conditions: There is an evolution underway.

Post-condition: Crop and place is Complete became true


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Figure 8: Check Crop and Place

2. Manage Profit loss (enter data)

Name: Enter_data (Crop_Name: String, Crop_ID: Integer, Place_Name: String, Place_ID: Integer)

Responsibility: Enter all the data to calculate profit loss .

Type: System

Cross Reference: Use case: Estimate Profit loss

Exceptions: If the data is not valid then generate an error.

Output:

Pre-conditions:

Crop and place was calculated.

Water level of the place was estimated.

Fertilizers were selected with respect to the crop and place.

Suitable Pesticides were selected with respect to the crop as well as place.

Post-conditions: A profit loss instance plc was created.

uc Actors

Farmer

System

Enterdata(crop, place)

Record Available

End

Check crop and place

Select crop and

place

[More check

crop and

place]


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Plc was associated with the current evaluation of profit loss (association formed).

Plc. Crop_Name became Crop_Name (attribute modification).

Plc. Crop_ID became Crop_ID.

Plc. Place_Name became Place_Name.

Plc was associated with a Profit loss calculation, based on Crop_ID, Place_ID match (association

formed).

Manage profit loss evaluation (End)

Name: End profit loss evaluation

Cross Reference: Use case: Estimate Profit loss

Pre-conditions:

There is a evolution underway.

Post-conditions:

Profit loss is Complete became true

Figure 9: Profit Lose Calculation


Farmer

SYESTEM

Select profit loss

Enter data(crop, place,pesticide,climate,fertilizer)

Calculate total profit /loss

Total profit/loss

TO genrate report

Report generated

End

PROFIT/LOSS

CALCULATION

[more item

profit/loss

calculaton]


22

3. Estimate water level (enter data)

Name: Enter data water_level (Crop_Name: String, Crop_ID: Integer, Place_Name: String, Place_ID:

Integer)

Responsibility: Enter data to check the average rain estimation of that specific place.

Type: System

Cross Reference: Use case: Evaluate water level (rain)

Notes: Use superfast Oracle database access.

Exceptions: If the data is not valid generate an error message.

Pre-conditions: Crop and place was entered.

Post-conditions:

A water_level instanceWL was created .

WL was associated with the current evaluate water level.

WL. Crop_Name became Crop_Name .

WL. Crop_ID became Crop_ID.

WL. Place_Name became Place_Name.

WL. Place_ID became Place_ID.

WL was associated with a evaluate water level, based some criteria match.

Estimate Water level (end)

Name: End water level evolution.

Cross Reference: Use case - Evaluate water level .


Post-conditions: Water level is Complete became true.


23

Figure 10: Check Rainfall

4. Manage Pesticide (Enter)

Name: Enter data (Crop_Name: String, Crop_ID: Integer, Place_Name: String, Place_ID: Integer)

Responsibility: Enter data to evaluate Pesticides quantity against the specific place and crop.

Type: System

Cross Reference: Use case: Manage Pesticides Enter

Exceptions: If the data is not valid generate an error message.

Post-conditions:

An Pesticides instance I was created.

I was associated with the current evaluate Pesticides quantity.

I. Crop_Name became Crop_Name.

I. Crop_ID became Crop_ID.

I. Place_Name became Place_Name.

I. Place_ID became Place_ID.

I was associated with a Pesticides, based some criteria match.

Manage Pesticides quantity (end)

Name: End Pesticides quantity calculation

uc Actors

Farmer

System

Enter data(crop,place)

Record available

Check average rain

Calculation of rain fall

End

Check Rain Fall

Select average rain fall estimation

[More estimation

of rain fall]


24

Cross Reference: Use case Manage Pesticides


Post-conditions: Pesticides quantity calculation is Complete became true.

Figure 11: Check Pesticide

CONCLUSION

This AGS is developed to achieve the mentioned criteria. The primary role of it is:

Progress in agriculture side of country

Doing things in an effective and easy way

Giving Farmer or layman a better idea and understanding of what’s going on

Making the decisions more precise

Decreasing the ratio of loss

Optimized and intelligent use of recourses

uc Actors

Farmer

System

Enterdata(crop, place)

Record Available

Check Pesticides

Show the best one

End

Check Pesticides

Select Pesticides

[More check

pesticdes]


25

REFERENCES

Abdullah, A. (2007) Data Mining Using the Crossing Minimization Paradigm, thesis of PhD submitted to University

of Stirling, Scotland, UK, Pp. 159

Abdullah, A., Stephen, B., Pervaiz, I., Umar, M.and Nisar, A. (2004). "Learning Dynamics of Pesticide Abuse

through Data Mining". Australasian Workshop on Data Mining and Web Intelligence, Dunedin, New

Zealand.

Ahmad, Q. and Sarwar, I. (2008). Texting for General Awareness for Sustainable Agriculture", OIC Telecom & IT,

International Conference & Expo, Lahore.

Kusabs, N., Bollen, F., Trigg, L., Holmes, G., Inglis, S. (1998). Objective measurement of mushroom quality. In:

Proc. New Zealand Institute of Agricultural Science and the New Zealand Society for Horticultural Science

Annual Convention

Leisa Armstrong, J, Diepeveen, D and Maddern, R. (2005) The Application of Data mining Techniques to

Characterize Agricultural Soil Profiles, http://crpit.com/confpapers/CRPITV70Armstrong.pdf

Liu dejun, L. and Guangsheng, Z. (2007) Application of Data Mining Technology in Modern Agricultural Logistics

Management Decision, International Conference on Agriculture Engineering: Shenyang Agricultural

University. ISBN： 978-0-646-48134-0

Index： ISTP，ISSHP，SEI

http://crpit.com/confpapers/CRPITV32Abdullah.pdf

http://crpit.com/confpapers/CRPITV32Abdullah.pdf

http://crpit.com/confpapers/CRPITV70Armstrong.pdf

Agriculture Decision Support System for Pakistan

Documents

Agriculture Decision Support System for Pakistan