FarmGPT: An AI-Powered Chatbot Using NLP for Accurate Farming and Disease Prediction

Abstract

Precision husbandry is swiftly converting traditional husbandry by integrating advanced technologies analogous as Artificial Intelligence( AI), Machine knowledge( ML), and Natural Language Processing( NLP). This transformation is necessary to address rising food demand, changing climate conditions, and the need for sustainable husbandry practices. This paper presents a comprehensive review of FarmGPT, an AI- powered chatbot designed specifically for agricultural operations. By using NLP, FarmGPT facilitates indefectible relations with farmers, enabling substantiated crop recommendations, complaint opinion, and poison suggestions. This review outlines the provocation, architecture, underpinning technologies, and performance benefits of FarmGPT. It also critically examines being results and identifies disquisition gaps and openings for future work.

Introduction

The global population is expected to reach 9.7 billion by 2050, increasing the demand for food. Agriculture faces challenges like unpredictable rainfall, soil degradation, pests, and lack of timely advice, especially for farmers in developing countries. Traditional agricultural extension methods are insufficient for these dynamic needs.

FarmGPT is an AI-powered intelligent chatbot designed to address these challenges by providing real-time, personalized, and region-specific agricultural guidance. It integrates crop prediction, disease diagnosis, and fertilizer recommendations into a single conversational interface using Natural Language Processing (NLP) and machine learning (ML) models.

The system leverages advanced NLP techniques like tokenization, intent recognition, and named entity recognition to understand farmer queries in multiple languages. Machine learning models including Support Vector Machines, Random Forests, Decision Trees, XGBoost, and Convolutional Neural Networks analyze data such as soil parameters, environmental conditions, and crop images to provide accurate advice.

FarmGPT’s architecture is modular and scalable, consisting of six layers: user interface, NLP engine, dialogue manager, ML engine, knowledge base, and database. It supports multi-turn conversations, voice and text input, image uploads for disease detection, and stores interaction data for continuous learning. The system is containerized for easy deployment and designed to operate even in low-connectivity rural areas.

Comparative analysis shows FarmGPT’s machine learning models, particularly Random Forest, perform with high accuracy, making it a promising tool to revolutionize agriculture through technology.

Conclusion

In an era where precision agriculture is essential for ensuring food security, sustainability, and rural development, FarmGPT stands out as a pioneering solution that harnesses the power of Artificial Intelligence and Natural Language Processing to empower farmers. By offering personalized, multilingual, and real-time agricultural support, FarmGPT bridges the gap between traditional farming practices and modern technological advancements. It not only simplifies access to expert knowledge for small and marginal farmers but also provides actionable insights into crop selection, disease management, and fertilizer optimization. The integration of machine learning and image-based diagnostics makes it an intelligent assistant capable of adapting to dynamic farming needs. While challenges such as internet accessibility, language diversity, and dataset generalization remain, the framework’s modular design offers potential for scalability and future enhancements like voice support, IoT integration, and blockchain-enabled traceability. FarmGPT holds immense promise in transforming agriculture into a data-driven, efficient, and inclusive ecosystem. By continuing to evolve and adapt, it has the potential to revolutionize the agricultural landscape across developing and developed economies alike.

References

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Copyright

Copyright © 2025 Mr. Krishna Swaroop A, H S Suprith, Chinmay Raj, Abhishek Gowda B P, Mohith G. 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.