# Bio-Inspired Multi-Granularity Model for Rice Pests and Diseases Named Entity Recognition in Chinese

## TL;DR

This paper introduces a deep learning model inspired by biology to improve the identification of rice pests and diseases in Chinese text.

## Contribution

A novel bio-inspired multi-granularity model for named entity recognition in agricultural contexts.

## Key findings

- The model achieves an F1 score of 91.74% on a self-constructed rice pests and diseases dataset.
- Multi-granularity CNNs enhance the model's ability to capture nested entity boundaries.
- BERT-based text encoding combined with BiLSTM-CRF improves entity recognition accuracy.

## Abstract

Rice, as one of the world’s four major staple crops, is frequently threatened by pests and diseases during its growth. With the rapid expansion of agricultural information data, the effective management and utilization of such data have become crucial for the development of agricultural informatization. Named entity recognition technology offers precise support for the early prevention and control of crop pests and diseases. However, entity recognition for rice pests and diseases faces challenges such as structural complexity and prevalent nesting issues. Inspired by biological visual mechanisms, we propose a deep learning model capable of extracting multi-granularity features. Text representations are encoded using BERT, and the model enhances its ability to capture nested boundary information through multi-granularity convolutional neural networks (CNNs). Finally, sequence modeling and labeling are performed using a bidirectional long short-term memory network (BiLSTM) combined with a conditional random field (CRF). Experimental results demonstrate that the proposed model effectively identifies entities related to rice diseases and pests, achieving an F1 score of 91.74% on a self-constructed dataset.