Voice enabled AI assistant for crop management in regional languages

Registration ID: IJNRD_322842

Published ID: IJNRD2604096

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Subhaharini P, Santhiya V, Niranjana K S

Voice-Enabled Assistant, Crop Management, Artificial Intelligence, Natural Language Processing, Speech Recognition, Regional Languages.

Agriculture remains the backbone of the economy in many developing countries, where a significant percentage of the population depends on farming for their livelihood. Despite technological advancements in various sectors, agriculture still faces numerous challenges such as unpredictable climatic conditions, soil degradation, pest infestations, improper irrigation practices, fluctuating market prices, and limited access to expert agricultural guidance. One of the major barriers preventing farmers from adopting digital agricultural solutions is the lack of accessibility due to language differences, low literacy levels, and limited technical knowledge. Most existing agricultural advisory platforms are text-based and designed in dominant languages, making them difficult to use for rural farmers who primarily communicate in regional languages and dialects.To address these challenges, the project titled “Voice-Enabled AI Assistant for Crop Management in Regional Languages” proposes an intelligent, inclusive, and user-friendly agricultural advisory system that enables farmers to interact with advanced digital technologies using voice commands in their native languages. The primary objective of this system is to bridge the communication gap between modern agricultural technology and rural farming communities by providing real-time, context-aware, and practical farming guidance through natural voice interaction.The proposed system integrates multiple advanced technologies, including Speech Recognition (Whisper Model), Natural Language Processing (NLP), Large Language Models (LLM), Machine Learning, and Text-to-Speech (TTS) synthesis, to create a seamless voice-based conversational experience. The system is developed using Python and Streamlit as the front-end interface, while the backend AI processing is handled through a locally hosted LLM using the Ollama framework. The system supports multiple regional languages such as Tamil, Hindi, Malayalam, Kannada, and English, ensuring linguistic inclusivity and improved accessibility.The workflow of the system begins with the farmer logging into the application and selecting a preferred regional language. The farmer then asks a question related to crop management using voice input through a microphone. The voice input is recorded and preprocessed by converting it into a standardized WAV format with a 16 kHz sampling rate to ensure transcription accuracy. The processed audio is then passed to the Whisper Speech-to-Text model, which accurately transcribes the spoken query into text in the selected language.Once the query is converted into text, it is forwarded to the AI-based advisory module. A predefined agricultural system prompt ensures that the AI behaves as an agricultural expert and provides safe, practical, and farmer-friendly responses. The Large Language Model processes the query and generates contextaware recommendations related to:• Suitable crop selection based on season and region• Pest and disease identification and control measures• Irrigation scheduling and water management practices• Fertilizer usage and soil nutrient management• Preventive measures and sustainable farming techniquesThe generated response is then converted into speech using Text-to-Speech technology. For English responses, an offline TTS engine (pyttsx3) is used, while for Indian regional languages, Google Text-toSpeech (gTTS) is employed to provide clear and natural voice output. The farmer receives both text and audio responses, ensuring better understanding even for users with minimal literacy skills.The system also incorporates secure user authentication and session management, where passwords are stored using hashing techniques to ensure data security. Temporary audio files are automatically deleted after processing to maintain privacy and optimize storage usage. The modular architecture of the system ensures scalability, maintainability, and the ability to integrate future enhancements such as IoT-based soil monitoring, image-based plant disease detection using deep learning, real-time weather API integration, and market price forecasting models.The implementation and testing of the system demonstrate reliable voice recognition accuracy, efficient AI response generation, and smooth multilingual interaction. The response time is suitable for near real-time interaction, making the system practical for daily agricultural usage. By eliminating dependency on textbased interfaces and enabling communication through regional languages, the proposed solution significantly enhances usability, inclusivity, and adoption among rural farming communities.In conclusion, the Voice-Enabled AI Assistant for Crop Management in Regional Languages represents a transformative step toward digital agriculture by combining artificial intelligence, speech technologies, and multilingual support. The system empowers farmers with timely, accessible, and data-driven agricultural guidance, leading to improved crop productivity, better decision-making, enhanced profitability, and sustainable rural development. This project demonstrates how AI-driven voice technologies can effectively reduce the digital divide in agriculture and create a more inclusive smart farming ecosystem.

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Subhaharini P, Santhiya V, & Niranjana K S (April-2026). Voice enabled AI assistant for crop management in regional languages . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), a845-a852. https://ijnrd.org/papers/IJNRD2604096.pdf

Volume 11 Issue 4, April-2026

Pages : a845-a852

Paper Reg. ID: IJNRD_322842

Published Paper Id: IJNRD2604096

Research Area: Other area not in list

Author Type: Indian Author

Country: Coimbatore , Tamilnadu , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2604096.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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