Enhancing Agricultural Practices with AgroDialogue: A Chatbot Framework

The agriculture sector faces significant challenges due to the limited adoption of efficient practices by farmers, leading to reduced crop yields and quality. This research introduces a specialized chatbot tailored for agricultural inquiries, leveraging advanced technologies such as Large Language Models integrated with Knowledge Graphs and vector databases. The chatbot performs thorough query analysis and delivers personalized information, facilitated by a user-friendly interface that supports seamless interaction, effective query categorization, and clear responses. Additionally, it incorporates voice functionality to improve accessibility for users with learning disabilities or literacy challenges. Unlike existing solutions, this chatbot is uniquely designed for agriculture, offering free, portable, and reliable guidance on farming techniques and related topics. By empowering farmers to make informed decisions about crop selection, pest management, and irrigation methods, the system aims to promote sustainable farming practices and enhance financial outcomes. The research methodology includes data preparation, model selection, and the implementation of a knowledge graph memory base. The development of APIs and databases ensures effective communication and user engagement, with comprehensive testing validating the system’s functionality, performance, and accuracy in diverse scenarios. This paper highlights the potential of advanced technologies in creating specialized agricultural chatbots, providing a promising approach to disseminate agricultural knowledge and support stakeholders in the sector.