💡[Feature]: NLP-Based Farmer Chatbot with Tokenization and Lemmatization

[Titus210]

Is there an existing issue for this?

• I have searched the existing issues

Feature Description

Problem Description:

Farmers need quick and accessible advice related to crop health, weather predictions, and farming practices. Implementing an NLP-based chatbot will enable them to interact easily, get personalized recommendations, and receive real-time answers to their agricultural queries.

Features to Include:

Core NLP Tasks:

• Tokenization: Break down user input into meaningful tokens.
• Lemmatization: Convert words to their base form to improve response matching.

Backend (Python):

• Implement logic to understand questions and provide appropriate responses.
• Integrate a knowledge base for farming best practices and crop advice.
• Provide recommendations based on user inputs, e.g., suggesting fertilizers, crops, or preventive measures.

Frontend (React):

• Build an interactive chat interface for farmers to enter queries.
• Display chatbot responses in real-time with typing animations.
• Provide support for multiple languages (optional).
• Integration with Backend API:

React frontend sends user input to Python backend.
Backend processes the query using NLP and returns a response.

Use Case

24/7 Availability:

The chatbot provides instant support anytime, without requiring human intervention. Farmers can access advice at their convenience, even outside working hours.

Improved Accessibility:

Farmers from remote areas gain easy access to agricultural knowledge through a simple chat interface. The chatbot can be multilingual to support non-English speakers.

Enhanced Productivity:

With timely and accurate recommendations, farmers can improve crop yields and avoid losses due to preventable issues like pests or improper watering.

Benefits

• Farmers can get quick answers anytime, without needing to wait for a human advisor. This ensures timely advice, especially in urgent scenarios like pest infestations or sudden weather changes.

• With NLP techniques like tokenization and lemmatization, the chatbot can understand farmers’ queries even if they use informal language, typos, or dialect-based variations. Farmers don't need technical knowledge—just conversational input.

• Queries logged by the chatbot provide valuable insights into common farming challenges. This data can be used to develop better farming policies, improve the chatbot, or design new products/services.

Add ScreenShots

No response

Priority

High

Record

• I have read the Contributing Guidelines
• I'm a GSSOC'24 contributor
• I want to work on this issue

[github-actions]

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊

Note: I Maintain the repo issue twice a day, or ideally 1 day, If your issue goes stale for more than one day you can tag and comment on this same issue.

You can also check our CONTRIBUTING.md for guidelines on contributing to this project.
We are here to help you on this journey of opensource, any help feel free to tag me or book an appointment.

[github-actions]

Hello @Titus210! Your issue #1554 has been closed. Thank you for your contribution!