水稻品种智能识别建模范式演进与高精度识别应用研究进展

Research Progress on Modeling Paradigm Evolution and High-Precision Recognition Application for Intelligent Rice Variety Identification

摘要: 大米品种真实性与纯度鉴定是保障我国粮食安全、推进种业振兴、规范粮油市场秩序的核心技术环节。传统鉴定方法存在效率低、主观性强、破坏性大等局限，随着计算机视觉与人工智能技术的迭代，以卷积神经网络(CNN)为核心的深度学习技术已成为大米品种鉴定的主流方向，同时多模态融合、大模型构建等前沿技术正为该领域带来革命性突破。本文系统梳理了大米品种鉴定技术的四代发展历程，剖析了基于AI的大米品种鉴定核心技术体系，结合现有研究成果总结了当前技术应用的瓶颈与挑战，并从多模态大模型、端侧边缘计算、全链条溯源、跨学科融合等维度提出了未来技术发展的前瞻性展望，旨在为大米品种智能鉴定技术的基础研究与产业化落地提供理论参考与技术路径。

Abstract: The identification of rice variety authenticity and purity serves as a core technical procedure for safeguarding national food security, advancing seed industry revitalization, and standardizing the order of grain and oil markets in China. Traditional identification methods suffer from limitations including low efficiency, strong subjectivity and high destructiveness. With the iteration of computer vision and artificial intelligence technologies, deep learning represented by convolutional neural networks (CNNs) has become the mainstream for rice variety identification. Meanwhile, cutting-edge technologies such as multimodal fusion and large model development are bringing revolutionary breakthroughs to this field. This paper systematically reviews the four-generation evolution of rice variety identification technologies, analyzes the core AI-based technical system for rice variety identification. Combining existing research findings, it summarizes the bottlenecks and challenges in current technical applications. Prospective outlooks for future technological development are proposed from dimensions including multimodal large models, edge computing, full-chain traceability and interdisciplinary integration, aiming to provide theoretical references and technical routes for basic research and industrial implementation of intelligent rice variety identification technologies.