作物种子活力无损检测技术研究进展

Research Progress in Non-destructive Testing Technology for Crop Seed Viability

作物种子是农业的基础,对延续物种十分重要,无损、高效地筛选出健康的种子对农业生产至关重要。传统种子活力检测方法如发芽实验、TTC染色法等,具有破坏性且耗时费力,无法满足现代农业的需求。近年来,随着检测技术研究的深入,多种无损检测技术方法得到发展,并被引入到种子活力检测中,这些技术具有无损、高通量等优势。本文针对种子所含物质的分子振动效应和贮藏过程中活力代谢的挥发,从光学和电化学两个角度综述了包括近红外光谱、多光谱、高光谱、拉曼光谱以及以电子鼻为代表的电化学技术在种子活力检测中的应用,重点表述了这些技术方法在不同研究对象、技术研发、模型算法开发等方面内容,分析了人工智能在种子活力检测领域的发展前景,提出将高光谱检测技术和电化学检测技术相结合构建多模态信息融合数据集,配合人工智能算法进行分析作为种子活力检测的新方向,期望在单一作物种类及跨种类的种子活力预测中取得更准确的结果。

Seeds are the foundation of agriculture and are crucial for sustaining species.The selection of high viability seeds through appropriate methods plays a crucial role in agricultural production.Traditional seed viability detection methods such as germination experiments and TTC staining methods were destructive and time-consuming,and couldnot meet the needs of modern agriculture.In recent years,with the deepening of research on detection technology,various non-destructive testing methods have been developed and introduced into seed viability detection.These technologies have advantages such as non-destructive and high-throughput.This article reviewed the molecular vibration effects of substances contained in seeds and the volatilization of viability metabolism during storage from both optical and electrochemical perspectives,including near-infrared spectroscopy,multispectral,hyperspectral,Raman spectroscopy,and the application of electrochemical techniques represented by electronic noses in seed viability detection.The focus was on describing the content of these technical methods in different research objects,technology research and development,model algorithm development,and other aspects.In conclusion,this paper analysed the development prospects of artificial intelligence in the field of seed viability detection.It proposed integrating hyperspectral detection technology and electrochemical detection technology to construct a dataset that fused multimodal information.Coupled with AI algorithms,this approach represented a new direction for seed viability detection,aiming to achieve more accurate results in predicting seed viability for both single crop species and across species.