TEAM NO 11 AGRI CHAT BOTfggtgfhgfffnjhgjnhjnhjnhjnhj.pptx

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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 of Agriculture Sector (ALSHBATAT ET AL, 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: Agri Bot Web App Agri Bot Chat Window End User Interface 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 informatio n 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