Natural Language Understanding in Agriculture: a Comprehensive Review of Technologies and Applications
Abstract
Natural language understanding (NLU), a pivotal branch of artificial intelligence, has demonstrated considerable potential in the agricultural domain thanks to its strengths in text processing, knowledge extraction and intelligent decision support. The evolution and core methodologies of NLU was reviewed and representative studies were surveyed across multiple agricultural scenarios, including agricultural text information extraction, construction of agricultural knowledge graphs, intelligent interaction with agricultural equipment and services, and the mining of scientific literature and patents. These applications have significantly enhanced the intelligence level of agricultural information acquisition and processing, providing effective support for agricultural production and management. Despite promising progress, NLU applications in agriculture still face several challenges: linguistic diversity and dialectal variation, small-sample learning and data-annotation scarcity, cross-modal data fusion and semantic alignment, efficient model deployment, and data-privacy protection in pursuit of sustainable development. Looking ahead, the rapid advances in self-supervised learning, transfer learning and multimodal intelligent agriculture were expected to empower NLU to play an even greater role in precision farming, real-time decision support and the broader quest for agricultural sustainability.
Keywords: natural language understanding, smart agriculture, agricultural knowledge graph, intelligent question-answering, text information extraction
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