高光谱成像技术在作物种子方面的应用

Application of hyperspectral imaging technology in crop seeds

作物种子作为种植业最基本、最原始的生产资料,选择出高质量的种子直接决定着农业生产的经济效益和生产效益。高光谱成像技术出现于20世纪80年代,具有无损、快速成像以及“图谱合一”等特点。运用高光谱成像技术在作物种子方面的研究,前人主要集中于作物种子的品种鉴别、活力检测和种子品质检测等方面。在前人的研究基础上进行深化总结凝炼可知,高光谱成像在作物种子品种鉴别研究主要应用数据处理模型包括偏最小二乘法(partial least squares,PLS)、Ada-Boost算法、极限学习机(extreme learning machine,ELM)、随机森林(random forest,RF)、支持向量机(support vector machine,SVM)和人工神经网络(artificial neural network,ANN)等。综上所述,本研究旨在为各种类型的作物种子研究提供最佳的光谱范围、样本种类、降噪方法、特征波段提取和模型建立等方面的依据,且对未来研究的方向提供了建议。

Crop seeds are the most basic and original means of production in the planting industry.The selection of high-quality seeds directly determines the economic and production benefits in the agricultural production process.Hyperspectral imaging technology emerged in the 1980s,which has the characteristics of non-destruction,rapid imaging and“integration of atlas”.Previous studies of crop seeds using hyperspectral imaging technology mainly focused on the variety identification,vigor detection,and seed quality of crop seeds.In this paper,based on the previous research,the authors summarize and refine the data processing models,which include such methods as partial least square method,Ada-Boost algorithm,limit learning machine(ELM),random forest(RF),support vector machine(SVM),and artificial neural network(ANN).To sum up,the purpose of this paper is to provide the best spectral range,sample types,noise reduction methods,feature band extraction,model building and other aspects as the basis for various types of crop seed research,and to provide suggestions for future research direction.