CropCare Companion: An AI-Based Multilingual Chatbot for Agricultural Assistance

Abstract

Agriculture remains a vital pillar of economic development in India, yet technological support to farmers is often limited due to language barriers, lack of awareness, and digital illiteracy. This paper presents \"CropCare Companion,\" an intelligent, multilingual chatbot system designed to assist farmers with real-time agricultural guidance using artificial intelligence (AI), natural language processing (NLP), and cloud-based APIs. The system supports major Indian languages (Hindi, Marathi, Gujarati, English), offers voice and text interfaces, tracks user history, and provides personalized farming solutions. It integrates a hybrid response model using both machine learning (Naive Bayes) and fallback to a large language model (LLM) API (DeepSeek) for unmatched query coverage. We evaluate the system’s performance through NLP accuracy, response relevance, multilingual support, and usability. The chatbot demonstrates over 91% accuracy across multilingual inputs and offers high accessibility, reliability, and scalability for rural deployments.

Introduction

Objective:

To provide timely, accessible, and multilingual agricultural guidance to Indian farmers using an AI-powered chatbot that supports text and voice interactions in Hindi, Marathi, Gujarati, and English.

???? Problem Addressed:

• Traditional agricultural advice is often delayed, inconsistent, and language-restrictive.

• Lack of real-time, voice-enabled, and regionally relevant support limits access for rural farmers.

???? Proposed Solution:

An AI-driven chatbot system offering crop, soil, pest, weather, and market guidance with:

• Multilingual support (text and speech).

• Fallback to advanced AI (DeepSeek API) for complex or unknown queries.

• User history tracking for personalized interaction.

???? System Architecture:

1. Input Layer: Accepts voice/text in 4 languages.

2. Preprocessing: Uses NLP tools (tokenization, stemming, stop word removal).

3. ML Model: Trained with Naive Bayes classifier on agricultural data.

4. Fallback Layer: Queries with low confidence are sent to DeepSeek API.

5. Output Layer: Response translated back and read aloud via Speech Synthesis.

6. UI Layer: Built with HTML/CSS/JS + Flask backend.

????? Multilingual & Voice Features:

• Google Translate API: Enables translation between local languages and English.

• Web Speech API: Converts voice to text.

• Speech Synthesis API: Converts text to speech in the user's language.

? Performance & Testing:

• 91% accuracy on multilingual agricultural queries.

• 1.7 seconds average response time.

• All test cases passed, including:

  • Hindi and Marathi voice/text inputs.

  • Fallback query handling.

  • User login and query history tracking.

???? Key Innovations:

• Combines NLP + Machine Learning + LLM fallback.

• Multilingual voice interaction for inclusivity.

• Tracks user history for personalized experiences.

Conclusion

CropCare Companion offers a multilingual AI-based solution for Indian farmers, enabling accessible and accurate agricultural support. By integrating NLP, voice, and cloud-based LLMs, it bridges the gap between traditional farming and smart agriculture. Future work includes mobile app deployment, image-based disease detection, offline capabilities, and integration with real-time weather and subsidy portals.

References

[1] Sharma et al., \"Multilingual Agricultural Chatbot Using NLP,\" IJERT, 2023. [2] Patel et al., \"AI for Market Price Prediction,\" IJCET, 2023. [3] Abbasi et al., \"Digitization in Agriculture 4.0,\" Procedia CS, 2022. [4] Katiyar et al., \"AI-Powered Chatbots for Farmers,\" JAISE, 2024. [5] DeepSeek API Documentation. https://deepseek.com/api [6] Google Translate API Docs. https://cloud.google.com/translat [7] NLTK Documentation. https://www.nltk.org/

Copyright

Copyright © 2025 Saili Sable, Siddhi Dusane, Asawari Gulmire, Chinmayi Vaidya, Shubham Mashalkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.