热损伤玉米种子的高光谱无损检测  

Hyperspectral Non-Destructive Detection of Heat-Damaged Maize Seeds

作　　者：张伏 禹煌 熊瑛[3] 张方圆 王新月 吕庆丰 武一戈 张亚坤 付三玲 ZHANG Fu;YU Huang;XIONG Ying;ZHANG Fang-yuan;WANG Xin-yue;L Qing-feng;WU Yi-ge;ZHANG Ya-kun;FU San-ling(College of Agricultural Equipment Engineering,Henan University of Science and Technology,Luoyang 471003,China;Collaborative Innovation Center of Advanced Manufacturing for Machinery and Equipment of Henan Province,Luoyang 471003,China;College of Agriculture/Peon,Henan University of Science and Technology,Luoyang 471023,China;Henan Pingan Seed Industry Limited Company,Jiaozuo 454881,China;School of Physical Engineering,Henan University of Science and Technology,Luoyang 471023,China)

机构地区：[1]河南科技大学农业装备工程学院,河南洛阳471003 [2]机械装备先进制造河南省协同创新中心,河南洛阳471003 [3]河南科技大学农学院/牡丹学院,河南洛阳471023 [4]河南平安种业有限公司,河南焦作454881 [5]河南科技大学物理工程学院,河南洛阳471023

出　　处：《光谱学与光谱分析》2024年第4期1165-1170,共6页Spectroscopy and Spectral Analysis

基　　金：龙门实验室前沿探索课题(LMQYTSKT032);国家“十三五”重点研发计划项目(2017YFD0301106);河南省重点研发专项(221111111000);河南省科技攻关计划项目(242102110337);河南省高等学校青年骨干教师培养计划项目(2017GGJS062)资助。

摘　　要：玉米是世界主要粮食作物之一,使用不符合国家标准的劣质种子将严重影响玉米作物产量,如何快速准确高效鉴别劣质玉米种子亟待解决。采用高光谱图像系统获取900粒“豫安三号”玉米种子的900~1700 nm光谱曲线,其中训练集和测试集比例为3∶2,分别为540粒和360粒。利用电鼓风式烘干箱对种子损伤处理,获得不同损伤程度的玉米种子样本,采集光谱后完成发芽试验,以此判别种子活力。为提高信噪比,截取963.27~1698.75 nm范围内的玉米种子光谱波段作为有效波段;采用标准正态变换(SNV)、多元散射校正(MSC)两种预处理方式对原始光谱数据预处理,并采用连续投影算法(SPA)、竞争性自适应重加权算法(CARS)两种特征波段提取算法对预处理后的光谱数据提取特征波段,波长反射率作为输入矩阵X,预设样本类别作为输出矩阵Y;最后采用支持向量机(SVM)模型建模分析,研究结果表明:MSC-CARS-SVM模型为最佳模型,模型识别成功率为98.33%,其Kappa系数为0.985。在此基础上,采用遗传算法(GA)对SVM中惩罚系数c和核函数参数g寻优,模型准确率提升至100%,可实现对热损伤劣质玉米种子的鉴别。该研究为劣质玉米种子及其他作物种子快速鉴别提供了新思路和方法。Maize is one of the three major food crops in the world,and the use of substandard seeds that do not meet the national standards will seriously affect the yield of maize crops,so how to identify substandard maize seeds quickly,accurately and efficiently is particularly important.The hyperspectral image system to obtain the 900~1700 nm spectral curves of 900“Yu an 3”corn seeds,in which the training set and test set ratio was 3∶2,540 and 360 seeds respectively.The seeds were treated with an electric blast dryer to obtain corn seed samples with different degrees of damage,and the germination test was completed after collecting the spectra to determine the viability of the seeds.In order to improve the signal-to-noise ratio,the spectral bands of maize seeds in the range of 963.27~1698.75 nm were intercepted as the effective bands.Standard Normal Variation(SNV)and Multiplicative Scatter Correction(MSC),were used to pre-process the raw spectral data.The Successive Projections Algorithm(SPA)and Competitive Adaptive Reweighted Sampling(CARS)were used to extract feature bands from the pre-processed spectral data,with wavelength reflectance as input matrix X and preset sample categories as output matrix Y.The Support Vector Machine(SVM)was used to model and analyze the data,and the results showed that the MSC-CARS-SVM model was the best model,with a model recognition success rate of 98.33%and a Kappa coefficient of 0.985.Genetic Algorithm(GA)was used to optimize the penalty coefficient c and kernel function parameter g in the SVM,and the model accuracy was improved to 100%for the identification of heat-damaged counterfeit and poor-quality maize seeds.This study provides a new idea and method for rapidly identifying the pseudo-inferior quality of maize seeds and seeds of other crops.

关 键 词：高光谱成像技术 玉米种子 热损伤 支持向量机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]