AI and ML based King
Coconut Assistant for
Farmers and Exporters
Project ID:
*******name*********
IT*******
BSc (Hons) in Information Technology
Specializing in *********
Department of Information Technology
Sri Lanka Institute of Information Technology
Sri Lanka
month year
topic
******Name*****
IT*****
Dissertation submitted in partial fulfillment of the requirements for the Bachelor of Science
Special (Honors) in Information Technology
Specializing in ***** *****
Department of Information Technology
Sri Lanka Institute of Information Technology
Sri Lanka
month year
DECLARATION
I declare that this is my work. This proposal does not incorporate without acknowledgement
any material previously submitted for a degree or diploma in any other university or institute
of higher learning. To the best of our knowledge and belief, it does not contain any material
previously published or written by another person except where the acknowledgement is made
in the text.
Name
Student ID
Signature
Signature:
Date:
Signature of the Supervisor:
Date:
i
ABSTRACT
The King Coconut is a highly valuable crop in Sri Lanka, and its cultivation and export are
vital for the country's economy. However, the cultivation and harvesting of King Coconuts
require a great deal of skill and knowledge, which makes it challenging for small-scale
farmers and exporters to compete with larger, more experienced players in the market.
To address this issue, we propose the development of an AI and ML-based King Coconut
Assistant, which will provide farmers and exporters with the necessary tools and
information to improve their production and profitability. The King Coconut Assistant will
be a user-friendly mobile application that will incorporate various AI and ML technologies
to assist farmers and exporters in every stage of King Coconut cultivation, from planting to
harvesting and marketing.
The King Coconut Assistant will provide a range of features and services, including real-time
weather information, pest and disease detection and prevention, yield forecasting,
harvesting and marketing tips, and a marketplace for buyers and sellers. The application
will be designed to be accessible and easy to use, even for farmers with limited
technological knowledge or resources.
The AI and ML technologies used in the King Coconut Assistant will be trained on large
datasets of historical King Coconut production and export data, as well as other relevant
data sources such as weather patterns and pest and disease outbreaks. This will enable the
Assistant to provide accurate and customized advice to individual farmers and exporters,
based on their specific needs and circumstances.
The King Coconut Assistant has the potential to revolutionize King Coconut production and
export in Sri Lanka, by leveling the playing field for small-scale farmers and exporters and
providing them with the tools and knowledge they need to compete with larger players in
the market. Additionally, the application has the potential to increase the overall quality
and quantity of King Coconut production, which will benefit the country's economy as a
whole.
ii
ACKNOWLEDGEMENT
iii
TABLE OF CONTENT
DECLARATION ........................................................................................................................... i
ABSTRACT..................................................................................................................................ii
ACKNOWLEDGEMENT ..............................................................................................................iii
TABLE OF CONTENT ................................................................................................................. iv
LIST OF TABLES ......................................................................................................................... vi
LIST OF FIGURES ....................................................................................................................... vi
LIST OF ABBREVIATIONS ......................................................................................................... vii
LIST OF APPENDICES ............................................................................................................... vii
1
2
INTRODUCTION ................................................................................................................ 1
1.1
BACKGROUND & LITERATURE REVIEW .................................................................... 3
1.2
RESEARCH GAP ......................................................................................................... 6
1.3
RESEARCH PROBLEM................................................................................................ 7
1.4
RESEARCH OBJECTIVES ............................................................................................ 9
1.4.1
MAIN OBJECTIVE .............................................................................................. 9
1.4.2
SPECIFIC OBJECTIVES...................................................................................... 11
METHODOLOGY ............................................................................................................. 14
2.1
System Architecture ............................................................................................... 14
2.2
Development Process ............................................................................................ 16
2.2.1
2.3
Component ............................................................................................................ 17
2.4
COMMERCIALIZATION ASPECTS OF THE PRODUCT ............................................... 18
2.5
TESTING & IMPLEMENTATION ............................................................................... 19
2.5.1
3
Tools and technologies .................................................................................. 16
Testing ............................................................................................................ 19
RESULTS & DISCUSSION ................................................................................................. 22
3.1
RESULTS.................................................................................................................. 22
3.2
RESEARCH FINDINGS .............................................................................................. 23
3.3
DISCUSSION............................................................................................................ 24
4
SUMMARY OF EACH STUDENT’S CONTRIBUTION.......................................................... 25
5
CONCLUSION .................................................................................................................. 26
6
REFERENCES ................................................................................................................... 27
7
APPENDICES ................................................................................................................... 28
iv
v
LIST OF TABLES
Table 2.1 TEST CASES ........................................................................................................ 21
LIST OF FIGURES
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vi
LIST OF ABBREVIATIONS
Abbreviations
Description
LIST OF APPENDICES
vii
1
INTRODUCTION
King coconut, scientifically known as Cocos nucifera, is a type of coconut that is
mainly grown in Sri Lanka. The tree produces a variety of useful products such as oil,
meat and milk, but young coconuts The sweet water of King Coconut stands out from
other types of coconut. King Coconut Water is a popular drink in Sri Lanka, known
for its refreshing taste and health benefits.
King coconut production and export has become an integral part of Sri Lanka's
agricultural industry and plays an important role in the country's economy. According
to the Sri Lanka Coconut Development Board, King Coconuts account for about 4%
of the total coconut production in Sri Lanka. However, growing king coconuts can be
a difficult process that requires a lot of skill and knowledge.
Cultivating king coconuts is an essential source of income for many smallholder
farmers in Sri Lanka. But farmers face several challenges, including pests and diseases,
climate change and market volatility. In addition, exporters face challenges such as
ensuring product quality during transportation and storage. These challenges often lead
to lower yields, lower quality and lower profits for farmers and exporters. There is
growing interest in using artificial intelligence (AI) and machine learning (ML) in
agriculture to address these challenges. AI and ML can be used to analyze large
amounts of data and provide recommendations to farmers and exporters. We help
farmers and exporters make informed decisions by providing real-time data and
recommendations tailored to their needs.
Therefore, in this work, we propose the development of an AI and ML-based King
Coconut Assistant that provides farmers and exporters with the tools and information
they need to improve their production and profitability. King Coconut Assistant is a
mobile application containing various AI and ML technologies to support farmers and
exporters in all stages of King Coconut cultivation, from planting to harvesting to
marketing.
The main goal of this work is to develop a King Coconut Assistant that will help
farmers and exporters improve the productivity and quality of their King Coconut
1
products.
King
Coconut
Assistant
is
designed
to
provide
personalized
recommendations based on the specific needs of individual farmers and exporters.
The development of King Coconut Assistant involves the integration of various AI and
ML technologies. One of the technologies used is image recognition. Image
recognition can be used to identify different stages of king coconut growth. B. To
recognize when coconuts are ready for harvest. This technology helps farmers and
exporters know exactly when to harvest and avoid picking unripe coconuts.
Another technology used is natural language processing (NLP). NLP can be used to
analyze data from various sources, such as weather forecasts and news articles. King
Coconut Assistant can use this information to provide growers and exporters with
relevant information such as weather conditions and market trends.
King Coconut Assistant also includes predictive analytics. Predictive analytics can be
used to analyze historical data and predict future returns. This information helps
farmers and exporters make informed planting and harvesting decisions.
The development of King Coconut Assistant has the potential to revolutionize Sri
Lanka's King Coconut production and export. King Coconut Assistant provides
growers and exporters with the tools and information they need to help them be more
productive, profitable and competitive in the marketplace. Furthermore, the
application has the potential to increase the overall quality and quantity of king
coconut production, which benefits the economy of the country as a whole.
2
1.1
BACKGROUND & LITERATURE REVIEW
Agriculture is an important sector of the economy in many countries, providing people with
food and other essential products. Sri Lanka is one of the countries where agriculture plays
an important role and contributes significantly to GDP. King Coconut is a popular
agricultural product in Sri Lanka, widely consumed locally and exported to many countries.
King coconut has many health benefits and is an important source of income for farmers
and exporters. We face many challenges in manufacturing and selling at a price. The use of
artificial intelligence (AI) and machine learning (ML) is gaining popularity in agriculture.
These technologies help farmers and exporters make better decisions by providing valuable
market insights and forecasts. Many AI and ML based applications have been developed to
help farmers and exporters improve their production and marketing strategies.
The proposed AI- and ML-based King Coconut Assistant is an important step towards
utilizing these technologies to benefit Sri Lanka's King Coconut industry. This application
aims to provide farmers and exporters with valuable insight into the international King
Coconut products market. The application's ability to predict future prices and volumes of
King Coconut products will help farmers and exporters to better plan their production and
marketing strategies.
The use of AI and ML in agriculture has been extensively researched in recent years. Much
research has focused on crop yield prediction, crop disease detection, and precision
agriculture. A study by Hasanuzzaman et al. (2020) used a machine learning algorithm to
predict wheat yield. In this study, the random forest algorithm was found to be the most
suitable for predicting wheat yield. Similarly, the study by Jiang et al. (2020) used
convolutional neural networks to detect plant diseases in potatoes. This study achieved his
98.4% accuracy, demonstrating the potential of AI and ML in disease detection.
AI and ML are also used to predict market prices and trends in other industries. A study by
Nair et al. (2021) used machine learning algorithms to predict stock prices in the Indian
stock market. In this study, we found that artificial neural network algorithms are the best
at predicting stock prices. Similar to the study by Singh et al. (2020) used machine learning
algorithms to predict demand for airline tickets. In this study, we found the gradient
boosting algorithm to be the most suitable for predicting ticket demand.
These studies demonstrate the potential of AI and ML in predicting future trends and
outcomes in various industries. The proposed AI and ML-based King Coconut Assistant aims
to use these technologies to predict future prices and quantities of King Coconut products.
The application's ability to predict future trends and outcomes helps farmers and exporters
make better decisions about their production and marketing strategies.
Additionally, several AI and ML-based applications are being developed to support the
agricultural sector in developing countries. For example, in the Philippines, a smart
agriculture dashboard developed by the International Rice Research Institute (IRRI) uses
machine learning algorithms to predict rice yields and optimize fertilizer use. In India, the eKrishi project, developed by the Indian government, uses mobile apps and sensors to
provide farmers with real-time information on weather, market prices and crop diseases.
3
These applications demonstrate the potential of AI and ML to transform agriculture and
improve farmers' lives.
AI and ML can be used to predict future prices and quantities of products by analyzing
patterns in data such as historical prices, supply and demand, and economic indicators. For
example, ML algorithms can analyze correlations between product prices and various
economic factors such as inflation, GDP growth, and interest rates to make accurate price
forecasts [3]. Additionally, the intersection of the supply and demand curves can be used to
determine the equilibrium price and quantity of the product [4]. To predict future prices
and quantities of King Coconut products, you can train AI and ML models based on
historical data such as King Coconut product prices and quantities over a specified time
period. Models can then analyze factors such as climatic conditions, supply and demand,
and economic indicators to predict future prices and quantities of products. Additionally,
the analysis of the supply and demand curve for King Coconut products can be used to
determine the equilibrium price and quantity of the product [4].
Applying AI and ML to intelligently help farmers and exporters predict future prices, identify
top and bottom countries, and forecast the volume of King Coconut products sold in
different countries system can be created. The system uses machine learning algorithms to
recognize patterns and trends in historical data related to King Coconut production and
export. By analyzing this data, the system can predict future trends and provide valuable
insights to farmers and exporters.
To determine future prices of King Coconut products, the system can use regression
analysis, a type of supervised machine learning algorithm. The system can be trained using
historical price data and can predict future prices based on various factors such as
production costs, supply, demand and global economic conditions.
To identify countries with high and low sales, the system can use clustering algorithms such
as K-Means to group countries based on their buying behavior. This system can analyze
historical sales data and identify patterns of purchasing behavior in each country. Based on
this analysis, the system can group countries into clusters and predict which countries are
most likely to purchase King Coco's products in the future.
To predict the amount of King Coconut products sold in different countries, the system can
use time series analysis, a kind of unsupervised machine learning algorithm. This system
can analyze historical sales data and identify patterns and trends in the data. Based on this
analysis, the system can predict the future volume of King Coconut products sold in
different countries.
Predicting the future is important in various fields such as finance, economics and
agriculture. For the AI and ML based King Coconut Assistant for farmers and
exporters, future forecasting is critical to identify potential customers and make
informed decisions about price, volume and market strategy. Predictive analytics can
be used to analyze historical sales, consumer behavior and market trend data to gain
insight into future demand and potential growth opportunities for King Cocos
products. You can use this information to identify the best and least selling countries,
4
and the most popular and least popular King Cocos product types. Using this data,
farmers and exporters can optimize their production and distribution strategies to
meet demand and maximize profits.
5
1.2
RESEARCH GAP
Features
Systems
Proposed
System



6

1.3
RESEARCH PROBLEM
Agriculture is an important sector for many developing countries, including Sri Lanka. It
employs a significant portion of the country's workforce and contributes to the country's GDP.
However, the agricultural sector's contribution to the Sri Lankan economy has declined in
recent years. Crop diseases, poor crop management, and ineffective export controls are some
of the factors contributing to the sector's decline. This research problem focuses on
overcoming these challenges by developing an AI- and ML-based King Coconut Assistant for
farmers and exporters.
The Significance of Agriculture in Sri Lanka:
The agricultural sector has played an important role in Sri Lanka's economy for centuries. It
employs most of the country's workforce and contributes significantly to the country's GDP.
However, the agricultural sector's contribution to the economy has declined in recent years,
with agricultural production's share of domestic GDP falling from 20% in 2000 to 7.4% in
2019. This decline is mainly due to several challenges facing the sector.
Crop Diseases:
Crop diseases are a major challenge for farmers in Sri Lanka. Insect-borne diseases and viruses
can reduce crop yields by 10% to 95%. Early detection is essential to prevent severe losses
and limit the use of pesticides that can harm human health and the environment. Farmers in
developing countries and small farms commonly use visual symptoms to identify crop
problems, but this is a time-consuming and laborious process. Therefore, the development of
AI and ML-based King Coconut Assistant can help farmers in disease detection and yield
estimation, making a significant contribution to the agricultural sector.
Poor Crop Management:
Another challenge faced by Sri Lankan farmers is their failure to adjust crop production to
external factors. Since all farmers sow and harvest at the same time, a significant portion of
the harvest is lost each year, and within a year he loses 40%. Excess food and inadequate
infrastructure to cope with other factors such as soil quality, temperature, altitude and
irrigation techniques also contribute to declining crop production. The AI and ML-based King
7
Coconut Assistant helps farmers adapt crop production to external factors, reduce crop losses
and increase yields.
Ineffective Export Management:
Sri Lanka lacks effective controls for marketing and exporting crops. Farmers struggle to sell
their crops quickly and are often unaware of global export markets. Intermediaries between
farmers and traders also struggle to source commodities. Farmers in remote areas may struggle
to sell fresh produce, resulting in massive crop losses. The AI and ML-based King Coconut
Assistant provides farmers with information on global export markets and future prices and
volumes of King Coconut products, facilitating effective management of crop distribution and
exports. Fast and efficient access to market information allows farmers to sell their crops
before they spoil or go bad.
Sri Lanka's agricultural sector faces several challenges including crop diseases, poor crop
management and ineffective export controls. Developing an AI and ML based King Coconut
Assistant for farmers and exporters can make a significant contribution in this field. The
software helps farmers with disease detection and yield estimation, allowing crop production
to be adjusted to external factors. Additionally, the software provides information on global
export markets and future prices and volumes for King Coconut products, facilitating effective
management of crop distribution and exports. By addressing these challenges, Sri Lanka's
agricultural sector can thrive and contribute more to the country's economy.
8
1.4
1.4.1
RESEARCH OBJECTIVES
MAIN OBJECTIVE
King Coconut Assistant is a mobile application developed to assist King
Coconut farmers and export buyers in their cultivation and export activities.
The main purpose of this application is to simplify the process of growing and
exporting King Coconuts while eliminating all potential problems that farmers
and export buyers may encounter.It just simplifies the process. Instead, the
application aims to provide farmers with valuable market insights and tools to
increase profitability. King Coconut Assistant aims to provide comprehensive
solutions for farmers and exporters involved in the King Coconut industry. The
application uses machine learning and artificial intelligence algorithms to
analyze historical data, market trends and other relevant factors to generate
accurate price forecasts and recommendations on growing practices. It also
provides a secure platform for real-time market insights and trading, making it
easier for farmers and exporters to connect and do business.
King Coconut Assistant's primary goal is to provide accurate and reliable price
predictions for King Coconut products. This application uses machine learning
algorithms to analyze various factors that affect prices, including: B. Historical
sales data, weather patterns, and market trends. This information is used to
create accurate price forecasts for a variety of King Coconut products in both
domestic and international markets. The price prediction functionality of this
application is very important as it enables farmers and export buyers to make
informed decisions about pricing and marketing strategies, thereby increasing
profitability. This application uses machine learning algorithms to analyze
various factors that affect prices, including: B. Historical sales data, weather
patterns, and market trends. This information is used to provide accurate price
forecasts for various King Cocos products in domestic and international
markets. Farmers and exporters can use this information to make informed
decisions about pricing and marketing strategies to increase profitability.
9
The second objective of the King Coconut Assistant is to provide
recommendations on cultivation practices based on historical data and other
relevant factors. The application uses machine learning algorithms to analyze
historical data on cultivation practices, soil quality, and weather patterns to
provide farmers with recommendations on the best cultivation practices for
king coconuts. By providing farmers with recommendations on the best
cultivation practices, the application aims to increase the yield and quality of
king coconuts, thereby increasing the profitability of farmers. The application
uses machine learning algorithms to analyze historical data on cultivation
practices, soil quality, and weather patterns to provide farmers with
recommendations on the best cultivation practices for king coconuts. By
providing farmers with recommendations on the best cultivation practices, the
application aims to increase the yield and quality of king coconuts, thereby
increasing the profitability of farmers.
The third objective of the King Coconut Assistant is to provide real-time
market insights to farmers and export buyers. The application uses machine
learning algorithms to analyze market trends, demand patterns, and other
relevant factors to provide real-time market insights to farmers and export
buyers. By providing real-time market insights, the application aims to help
farmers and export buyers make informed decisions about pricing and
marketing strategies, thereby increasing their profitability. The application uses
machine learning algorithms to analyze market trends, demand patterns, and
other relevant factors to provide real-time market insights to farmers and export
buyers. By providing real-time market insights, the application aims to help
farmers and export buyers make informed decisions about pricing and
marketing strategies, thereby increasing their profitability.
The fourth objective of the King Coconut Assistant is to provide a secure
platform for transactions between wholesale king coconut suppliers and buyers
across the country. The application eliminates the need for intermediaries by
10
providing a secure platform for wholesale king coconut suppliers and buyers
to connect and conduct business. By providing a secure platform for
transactions, the application aims to increase transparency and reduce
transaction costs, thereby increasing the profitability of farmers and export
buyers.The application eliminates the need for intermediaries by providing a
secure platform for wholesale king coconut suppliers and buyers to connect
and conduct business. By providing a secure platform for transactions, the
application aims to increase transparency and reduce transaction costs, thereby
increasing the profitability of farmers and export buyers.
The King Coconut Assistant is a mobile application that has been developed to
assist king coconut farmers and export buyers in their cultivation and export
activities. The application has four main objectives: to provide accurate and
reliable
price
predictions
for
king
coconut
products,
to
provide
recommendations on cultivation practices, to provide real-time market
insights, and to provide a secure platform for transactions between wholesale
king coconut suppliers and buyers across the country. By achieving these
objectives, the application aims to simplify the process of cultivating and
exporting king coconuts while increasing the profitability of farmers and
export buyers.
1.4.2
SPECIFIC OBJECTIVES
AI and ML based King Coconut Assistant for Farmers and Exporters aims to assist
farmers and exporters in their King Coconut cultivation and export activities. Several
sub-goals have been identified to achieve this goal. These objectives include providing
valuable advice to clients in international markets, predicting future King Coconut
product prices and volumes, collecting and analyzing historical export data to develop
accurate models. providing comprehensive financial information to buyers and
11
exporters, and providing easy access to information about the future. price and
quantity.
The first sub-goal of this project is to provide valuable advice to farmers and exporters
in the international market and predict future prices and volumes of King Coconut
products. This allows customers to plan accordingly and make informed decisions
based on the latest market trends. The AI and ML-based King Coconut Assistant uses
advanced algorithms to analyze market data and make accurate forecasts, ensuring
clients have the most up-to-date information to make strategic decisions.
His second sub-goal of this project is to collect and analyze all available data on past
King Coconut exports and use machine learning algorithms to develop models that can
accurately predict the future of the industry. to develop. This helps farmers and
exporters make informed decisions and plan their activities accordingly. The AI- and
ML-based King Coconut Assistant also uses advanced statistical techniques to identify
patterns and trends in export data to gain valuable market insights.
The third sub-goal of this project is to provide King Coconut buyers and exporters with
comprehensive financial information, including historical and current data, as well as
projections of future trends. This allows them to make informed decisions and stay
abreast of the latest developments in the market. The AI and ML-based King Coconut
Assistant uses advanced financial analytics techniques to provide detailed financial
information to help buyers and exporters understand the financial implications of their
decisions.
The fourth and final sub-goal of this project is to provide producers and buyers with
easy access to information on expected future prices and volumes of King Coconut
products. This allows you to plan your activities more effectively and maximize your
profits. AI and ML based, King Coconut Assistant provides a user-friendly interface
that is easy to navigate and provides quick access to the latest market information. This
12
enables our customers to make informed decisions based on the latest market trends
and developments.
In summary, for farmers and exporters, the AI and ML-based King Coconut Assistant
can provide valuable advice, forecast future prices and volumes, collect and analyze
export data, provide comprehensive financial information and easily There are several
sub-goals aimed at easy access. Information to enable future pricing and quantities.
Achieving these sub-goals will enable customers to make informed decisions and plan
activities effectively, ultimately leading to increased profits for farmers and exporters.
13
2
2.1
METHODOLOGY
System Architecture
System Diagram
The system diagram provided depicts the architecture of the AI and ML based King Coconut
Assistant for Farmers and Exporters. The diagram is divided into three main sections: the
mobile application, the machine learning model, and the database.
Starting with the mobile application, the first thing to note is that it is the main interface for
users of the system. Farmers, export buyers, and suppliers will all access the system through
the mobile application. The application is responsible for collecting user input and transmitting
it to the backend for processing. It also receives output from the backend and displays it to the
user. The mobile application is designed to be user-friendly, with an intuitive interface that
makes it easy for users to interact with the system.
The backend of the system is composed of two main components: the machine learning model
and the database. The machine learning model is responsible for analyzing data and providing
predictions for the future of the king coconut industry. The model takes input from the mobile
application in the form of past data on king coconut exports, and uses this data to generate
predictions for the future of the industry. These predictions are then sent back to the mobile
application for display to the user.
14
The database component of the backend is responsible for storing and managing data related
to the king coconut industry. It stores information such as past export data, financial
information, and other relevant data points that are used by the machine learning model to
generate predictions. The database is designed to be scalable and flexible, allowing it to handle
large amounts of data and adapt to changing requirements.
The communication between the mobile application and the backend is facilitated by an
Application Programming Interface (API). The API acts as a bridge between the two
components, allowing them to communicate with each other and exchange data. The API is
designed to be secure and reliable, ensuring that user data is protected at all times.
Finally, the system diagram includes a number of external data sources. These sources provide
data that is used by the machine learning model to generate predictions. For example, weather
data might be used to predict the yield of king coconuts in a particular region, while financial
data might be used to predict the future price of king coconut products. The system is designed
to be flexible enough to accommodate a wide range of external data sources, ensuring that the
predictions generated by the model are as accurate as possible.
the system diagram provides a clear and concise representation of the AI and ML based King
Coconut Assistant for Farmers and Exporters. The system is designed to be user-friendly and
flexible, with a mobile application interface that makes it easy for users to interact with the
system. The backend of the system is composed of a machine learning model and a database,
which work together to generate predictions for the future of the king coconut industry. The
system also includes an API that facilitates communication between the mobile application
and the backend, as well as external data sources that provide additional data for the machine
learning model to use in generating predictions. Overall, the system is a powerful tool for
farmers, export buyers, and suppliers, providing valuable insights into the king coconut
industry and helping users to make informed decisions about their business activities.
15
2.2
2.2.1
Development Process
Tools and technologies
16
2.3
Component
The development of an AI and ML based King Coconut Assistant for Farmers and Exporters
involves a number of key components that work together to provide farmers and exporters
with the information and support they need to improve their activities and maximize their
profits.
One of the most important components of this system is the data collection and analysis
component. This involves collecting and analyzing data related to the production and
export of king coconuts, as well as data related to market trends and consumer demand.
This data is then used to train machine learning algorithms and develop predictive models
that can be used to forecast future trends in the market and provide recommendations for
farmers and exporters.
Another key component of the system is the mobile application that is designed to assist
king coconut farmers and export buyers in their cultivation and export activities. The
application aims to simplify the process of exporting king coconuts and eliminate potential
problems by providing real-time updates on market trends, weather patterns, and other
relevant information. The application also facilitates transactions between wholesale king
coconut suppliers and buyers across the country, eliminating the need for intermediaries.
The financial information component of the system is also critical, as it provides king
coconut buyers and exporters with the information, they need to make informed decisions
and stay up-to-date on the latest developments in the market. This includes past and
present financial data, as well as forecasts for future trends. The financial information
component of the system is also linked to the predictive models developed by the machine
learning algorithms, allowing for more accurate predictions of future market trends and
prices.
The system also includes a communication component, which allows farmers and exporters
to communicate with one another and with buyers more easily. This component includes
features such as messaging and video conferencing, which can be used to share
information, discuss potential deals, and collaborate on various aspects of the export
process.
Finally, the system includes a feedback component, which allows farmers and exporters to
provide feedback on the application and the overall system. This feedback can be used to
improve the system and make it more user-friendly, as well as to identify potential areas for
improvement in the export process.
17
2.4
COMMERCIALIZATION ASPECTS OF THE PRODUCT
18
2.5
2.5.1
Test
case
ID
T_1
TESTING & IMPLEMENTATION
Testing
Test case Test input Values
Name
Test
Procedure
T_2
19
Expected
Output
Actual
Result
Test
Resul
t
T_3
T_4
20
T_5
T_6
Table 2.1 TEST CASES
21
3
3.1
RESULTS & DISCUSSION
RESULTS
22
3.2
RESEARCH FINDINGS
23
3.3
DISCUSSION
24
4
SUMMARY OF EACH STUDENT’S CONTRIBUTION
Member
Components
Tasks
25
5
CONCLUSION
26
6
REFERENCES
27
7
APPENDICES
28