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1. DEPARTMENT OF INFORMATION TECHNOLOGY
AGRICULTURAL CHATBOT
GUIDED BY:
Ms. B. MANJUBASHINI, AP/IT
TEAM MEMBERS:
 PRAVEEN ESWAR K
 DIVYASHRI P
 GOPINATH K
 KISSHORE S V

2. INTRODUCTION
 The AI-driven interactive Agri Bot is a cutting-edge technology that has the
potential to transform agricultural practices by delivering cultivation support.
 Using advanced artificial intelligence algorithms.
 This unique project intends to provide farmers with individualized, data-driven
insights and recommendations based on their specific crops, soil conditions, and
environmental factors.
 The Agri Bot, helps the farmers to identify the soil type and its best suited crop
variety and gives the pesticide recommendation for that crop.

3. ABSTRACT
 Small-scale farmers face multifaceted challenges in optimizing agricultural
productivity and income.
 Ensuring food security is achieved by deploying a comprehensive approach
to crop management, pest control, and efficient harvesting.
 It enhances the operations such as planting, harvesting, and post-harvest
processing are implemented, reducing labor costs and increasing efficiency.
 The aim of this project is to double agricultural output and income for
small-scale farmers, contributing to the sustainable development of rural
communities.

4. LITERATURE SURVEY
 Data-Driven Artificial Intelligence Applications for Sustainable
Precision Agriculture (Jürgen Bund 2021).
 AI Driven Chat Bot Providing Realtime Assistance in Cultivation
(FEI LEI ET AL,2022).
 Agricultural Helper Chat Bot Using Deep Learning
(Ullas Gurla hosur*1, L2021).
 Survey ofAgriculture Sector (ALSHBATAT ETAL, 2020).

5. EXISTING SYSTEM
 The first and perhaps the simplest bots are rule-based chatbots, also known as
decision-tree bots.
 These bots are the most common, and many of us have likely interacted with one
either through Live Chat features, on e-commerce sites, or via social media.
 Such chatbots have a very limited skill set. Still, you can use them for simple
tasks such as:
1) Customer support agents that provide customers with automated
responses.
2) Engagement bots that inform customers about special offers.

6. DISADVANTAGES:
• Limited Accessibility
• Delay in Information Delivery
• Lack of Personalization
• Dependency on Human Resources
• Limited Interactivity

7. PROPOSED SYSTEM
 AI chatbots are more complex programmed bots based on Natural Language
Processing (NLP) and Machine Learning (ML) algorithms.
 Collecting dataset related to Agriculture: This step involves gathering relevant
data related to agriculture from various sources, such as government websites,
research papers, and industry reports.
 Pre-processing: The collected data is pre-processed to make it suitable for
analysis. This includes techniques such as stemming, lemmatization, removal of
stop words, and tokenization.
 Feature Extraction: The pre-processed data is then converted into a numerical
format that can be used for analysis.

8. ADVANTAGES:
• 24/7 Availability of chatbot
• Personalized Recommendations for users
• Timely Information and Alerts
• Efficient Problem Solving
• Data Collection and Analysis

9. SYSTEM
ARCHITECTURE

10. MODULES DESCRIPTION
Modules Description are classified as:
1. Agri Bot Web App
2. Agri Bot Chat Window
3. End User Interface
4. Agri Bot Training

11. 1. Agri Bot Web App
 The design and development of the Agri Bot web app involve integrating
different technologies and tools to create a seamless user experience for farmers.
1.1. Front End: The front end of the Agri Bot web app was implemented using
HTML, CSS, and JavaScript.
1.2. Back End: The back end of the Agri Bot web app was implemented using
Python Flask.
1.3. Database: The database used in the Agri Bot web app is MySQL. The
database stores user information such as name, email address, and password for
registration and login purposes.

12. 2. Agri Bot Chat Window
 The chat window of Agri Bot is the main interface where farmers can interact
with the chatbot.
2.1. HTML/CSS: The HTML file includes the basic structure of the chat
window, such as chat area, user input area, and send button. The CSS file is used
to style the chat window, such as colour, font, and layout.
2.2. JavaScript: The chat window is interactive and dynamic. The JavaScript file
handles the user input and sends it to the backend for processing.
2.3. Python Flask: The backend of the Agri Bot chat window is developed using
Python Flask.
2.4. MySQL: The chatbot's database is developed using MySQL. It stores the
user's login credentials, user input, and chatbot responses.

13. 3. End User Interface
 Agri Bot is an AI-based chatbot designed to assist farmers with their agricultural
queries.
 The chatbot has two interfaces: one for the admin and another for the farmers.
3.1. Admin Interface: The admin interface consists of modules for collecting,
pre-processing, and training the chatbot with data related to agriculture.
Collect dataset related to agriculture.
Train the chatbot using natural language processing techniques
3.2. Farmer Interface: The farmer interface consists of modules for registering,
logging in, and receiving responses from the chatbot.
 Register with the chatbot by providing their information
Log in to their account

14. 4. Agri Bot Training
 Agri Bot, being an AI-based farmers' chatbot, requires extensive training
in natural language processing (NLP) techniques. Submodules are
4.1. Data Collection
 The first step in building an effective chatbot is collecting data. In this
module, the chatbot administrator gathers datasets related to agriculture.
4.2. Data Exploration
 The module performs an initial exploration of the dataset to understand the
characteristics of the data.

15. 4.3. Feature Extraction
• In "Agri Bot: An AI based Farmers Chatbot", feature extraction is the process of
converting text data into a numerical format that machine learning models can
understand.
4.4. Performance Analysis
• Performance Analysis is an important step in evaluating the effectiveness of a
chatbot. It helps to determine how well the chatbot performs in recognizing the
user's intent, generating appropriate responses, and providing accurate information.

16. LANGUAGES AND TOOLS USED
• Server Side : Python 3.7.4 (64-bit)
• Client Side : jQuery HTML, CSS, Bootstrap
• IDE : Flask
• Back end : MySQL
• Server : WampServer
• OS : Windows 10 or Ubuntu 18.04 LTS
“Bionic Beaver”
• DL Packages : Pandas, SciKit-Learn, NumPy

17. HOME PAGE

18. REGISTRATION

19. ADMIN LOGIN PAGE

20. QUERY UPDATING PAGE

21. CHATBOT PAGE

22. CONCLUSION
 Agricultural chatbots stand as a transformative force for the agricultural
industry. They bridge the knowledge gap, empowering farmers with instant
access to valuable information, right at their fingertips.
 From crop management and pest control to soil health and crop
enhancement, these virtual assistants provide guidance and support.

23. THANK YOU