Natural Language Processing (NLP) is revolutionizing agriculture and allied sciences by enabling machines to understand and process human language for actionable insights. With the rapid increase in unstructured data, from research articles and weather reports to farmer queries, NLP offers transformative potential in enhancing decision-making, knowledge dissemination, and automation in the agricultural ecosystem. This chapter explores core NLP techniques, their practical applications in agriculture, and the evolving role of intelligent language-based systems. Key NLP techniques such as tokenization, named entity recognition, and semantic search have been discussed, along with popular tools like NLTK, SpaCy, and transformer-based models, including BERT and RoBERTa. A major highlight of the chapter is the development of a virtual agent named SHRIA (Smart Heuristic Response-based Intelligent Agent), a domain-specific chatbot designed to support livestock farmers by offering multilingual, real-time, and expert-validated advisory services. Developed using over two lakhs curated Q&A pairs and leveraging state-of-the-art NLP models, SHRIA demonstrates how AI-driven virtual assistants can bridge information gaps in resource-constrained environments. The chapter presents SHRIA’s architecture, user interface, evaluation metrics, and performance outcomes.