A hybrid approach to advanced NER techniques for AI-driven water and agricultural resource management

Introduction Named Entity Recognition (NER) plays a crucial role in extracting valuable insights from unstructured text in specialized domains like agriculture and water resource management. These fields face challenges such as complex terminologies, heterogeneous data distributions, data scarcity, and the need for real-time processing, which hinder effective NER. In agriculture, for example, variations in crop names, irrigation methods, and environmental factors add additional complexity. The increasing availability of sensor data and climate-related information has led to more dynamic, time-sensitive text, requiring NER systems to continuously adapt. Methods This paper introduces a hybrid NER approach combining ontology-guided attention with deep learning. It includes two core components: the Adaptive Representation Neural Framework (ARNF) for multiscale semantic feature encoding, and the Adaptive Task Optimization Strategy (ATOS), which dynamically balances learning priorities to enhance multitask performance in heterogeneous and resource-constrained environments. Results Experimental results on several benchmark datasets demonstrate that our method significantly outperforms state-of-the-art models. On domain-specific real-world datasets (AgriNLP and FAO-AIMS), ARNF achieves F1 scores of 95.54% and 96.75%, respectively. Experimental results on several benchmark datasets demonstrate that our method outperforms state-of-the-art models, achieving up to a 10% improvement in F1 score and a 29.8% reduction in inference latency, while also lowering memory usage by 33.4%, highlighting both its accuracy and efficiency. Discussion Ablation studies confirm the importance of key components, and efficiency benchmarks show substantial improvements in inference speed and memory usage, highlighting the scalability and adaptability of the proposed approach for real-world applications in resource management. By achieving high accuracy and scalability, our method enables timely and reliable extraction of critical information from agronomic reports and policy documents-supporting applications such as precision irrigation planning, early detection of crop diseases, and efficient allocation of water resources in data-scarce regions.