AI Web Interaction Using Voice Agent in KRISHI-SHETRA

Abstract

Millets are resilient crops that are nutrient-rich and vital for sustainable farming. However, the value chain for millets in India remains fragmented and inefficient. Farmers often face challenges like limited market access, language barriers, poor quality assessment, and reliance on middlemen. These issues result in unfair prices and reduced incomes. This paper introduces KRISHI-SHETRA, a digital marketplace powered by AI and voice technology, designed for the millet ecosystem. The system integrates artificial intelligence, and voice interfaces in various languages. This approach allows farmers, Farmer Producer Organisations (FPOs), Self-Help Groups (SHGs), buyers, consumers, and government agencies to engage openly and fairly. The voice-first model enables farmers to list their products, check prices, and learn about government programs in their local languages without needing digital skills. AI-based image processing assesses millet quality using smartphone images.The paper outlines the system\'s architecture, functional components, data flow, and security measures. The expected results include higher incomes for farmers, reduced exploitation by intermediaries, improved quality assurance, and increased adoption of millets. This framework supports India’s Shree Anna initiative and illustrates how AI-powered voice interfaces can transform digital agriculture.

Introduction

The millet farming sector in India has significant potential for improving nutrition and environmental sustainability, but it is hindered by fragmented supply chains, multiple intermediaries, poor market connectivity, limited standardization, and low digital adoption. Voice-based technologies have emerged as an effective solution, enabling farmers to access market information more easily, reduce price discovery time, and secure better prices. Studies show that farmers strongly prefer voice interfaces over text-based systems, particularly in regional languages.

To address these challenges, the KRISHI-SHETRA platform is proposed as an AI-powered, voice-enabled digital marketplace for the millet ecosystem. It connects farmers, Farmer Producer Organizations (FPOs), Self-Help Groups (SHGs), buyers, processors, consumers, and government agencies through a single platform. Farmers can list crops, receive AI-based quality assessments, access government schemes, and sell directly to buyers, while consumers benefit from product traceability, nutritional information, and quality assurance. The platform aligns with the Government of India's Shree Anna initiative to promote a technology-driven millet economy.

The literature review highlights the growing role of voice-based agricultural systems, AI-powered crop quality assessment, and scalable microservices architectures. Research demonstrates that multilingual voice interfaces significantly improve user adoption, while AI-based image analysis enables accurate grain quality grading. Technologies such as speech recognition, large language models (LLMs), and microservices provide the technical foundation for scalable agricultural applications.

KRISHI-SHETRA employs a layered architecture consisting of a user accessibility layer, AI voice interaction layer, and microservices-based backend. Farmers interact with the system through toll-free voice calls, eliminating the need for smartphones or digital literacy. The AI voice agent performs speech-to-text conversion, intent recognition, and regional language processing, while backend services manage product listings, pricing, notifications, and session continuity through secure APIs.

The platform follows a multimodal methodology by combining structured farm data, voice commands, and smartphone images. AI-generated quality scores are integrated with regional market prices to ensure fair pricing, while logistics optimization groups nearby farmers to reduce transportation costs. QR-code-based traceability links each product to its origin, quality grade, certification, and transaction history, improving transparency and consumer trust.

Performance evaluation demonstrates that the platform supports over 2,000 concurrent voice sessions, maintains response times below two seconds, processes up to 1,000 requests per second, and provides 99% system availability. Speech-to-text accuracy remains high even under noisy rural conditions, confirming the platform's suitability for real-world agricultural environments. Overall, KRISHI-SHETRA offers a scalable, AI-driven, voice-first digital marketplace that enhances market access, transparency, efficiency, and inclusiveness across the millet value chain.

Conclusion

This research successfully presents the design and architectural framework of KRISHI-SHETRA, an AIpowered, voice-first digital marketplace tailored for the millet value chain. By integrating multilingual voice interaction, AI-based quality assessment, secure data handling, and end-to-end traceability, the proposed system addresses critical challenges of accessibility, transparency, and trust in digital agriculture. The modular, microservices-driven architecture ensures scalability and extensibility while supporting diverse stakeholders across the ecosystem. Although the platform is currently under development, the proposed design demonstrates strong potential to enhance farmer participation, reduce intermediary dependency, and promote sustainable agricultural practices. The framework establishes a foundation for future implementation, evaluation, and expansion to broader agricultural domains.

References

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Copyright

Copyright © 2026 Samiksha Karpe, Dr. Aparna Hambarde, Omkar Kathile, Tejas Khairnar , Aniket Lakade. 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.