APPLICATIONS OF NATURAL LANGUAGE PROCESSING IN AGRICULTURE: A THEMATIC SURVEY AND RESEARCH ROADMAP

Abstract

The integration of Natural Language Processing (NLP) into agriculture has opened new avenues for knowledge extraction, intelligent decision-making, and farmer assistance. This paper reviews recent research across five major areas: ontology construction, question answering systems, information extraction, language modeling, and multimodal NLP applications in the agricultural domain. The review identifies key strengths in precision agriculture support and knowledge representation, while also outlining existing limitations such as lack of annotated datasets, low cross-domain adaptability, and limited deployment in rural settings. The paper concludes with an analysis of research gaps and proposes future directions aimed at improving scalability, multilingual support, interpretability, and real-world applicability of NLP technologies in agriculture.