基于近红外光谱技术及ELM对小麦中不同生长阶段米象的分类识别  

Classification and recognition of Sitophilus oryzae in different growth stages of wheat based on near-infrared spectroscopy and ELM

作　　者：鲁玉杰[1,2] 王文敬 张俊东 王争艳 卢少华[1] LU Yujie;WANG Wenjing;ZHANG Jundong;WANG Zhengyan;LU Shaohua(College of Food Science and Engineering,Henan University of Technology,Zhengzhou 450001,China;School of Grain Science and Technology,Jiangsu University of Science and Technology,Zhenjiang 212100,China)

机构地区：[1]河南工业大学粮油食品学院,河南郑州450001 [2]江苏科技大学粮食学院,江苏镇江212100

出　　处：《河南工业大学学报（自然科学版）》2023年第1期104-111,共8页Journal of Henan University of Technology：Natural Science Edition

基　　金：国家重点研发计划项目(2019YFC1605304)。

摘　　要：对粮食中隐蔽性害虫的早期诊断和检测,不仅可以减少因害虫取食造成的粮食产后损失,还可以减少化学药剂的使用,对于保证粮食品质和减少环境污染具有重要的意义。基于近红外光谱技术与极限学习机(ELM)构建小麦中不同生长阶段米象的分类识别模型,采集未感染小麦和感染米象小麦的近红外光谱数据,选择SNV+De-trending的组合对原始光谱数据进行预处理,使用主成分分析(PCA)方法对光谱数据进行降维特征提取,利用ELM和支持向量机(SVM)建立分类识别模型。结果表明:ELM模型训练时间仅需0.062 5 s,总体分类准确率为90%,0、6、24和27 d的识别率为100%,10~20 d的幼虫期识别率偏低,20 d时识别率最低,为65%;SVM模型运行时间为3.38 s,分类准确率为85.42%,ELM模型较SVM模型的运行时间和分类准确率都有所提高。因此,ELM分类识别模型能够快速准确地判断小麦有无米象,以及分类识别小麦中不同生长发育阶段的米象。Early diagnosis and detection of hidden pests in grain could not only reduce the post-production losses of grain caused by pest feeding, but also reduce the use of chemicals, which are important for maintaining grain quality and reducing environmental pollution. In this paper, a classification and identification model of Sitophilus oryzae(S. oryzae) in wheat at different growth stages was constructed based on near-infrared spectroscopy and extreme learning machine(ELM), and the near-infrared spectral data of uninfected wheat and infected S. oryzae wheat were collected. The images of S. oryzae in different growth and development stages were obtained by X-ray imaging technology, and the development period of S. oryzae was obtained through the images(egg stage at 0-9 d, larval stage at 10-20 d, pupa stage at 21-26 d, and adult stage at 27-30 d), the wheat with full grains was selected and collected by near infrared spectroscopy to obtain the spectral data of uninfected samples, and then wheat was infested with S. oryzae adults. After 48 hours, the S. oryzae adults were taken out, and the samples were collected by near-infrared spectrum on 6, 10, 14, 17, 20, 24, and 27 day of the experiment to obtain uninfected wheat and near-infrared spectral data of wheat infected with S. oryzae wheat. When modeling using the original spectral data, the classification accuracy of the ELM model was 78.75%. After preprocessing, the classification accuracy of the ELM model reached 85%, and then the principal component analysis(PCA) method was used to perform dimension reduction feature extraction on the spectral data. When the target dimension was 120 dimensions, the accuracy of the ELM classification and recognition model was 90%, the classification recognition rate increased by 12.5%. The experimental results showed that the appropriate preprocessing method and PCA dimensionality reduction feature extraction could effectively improve the classification accuracy of ELM model, and the training time was only 0.062 5 s, the overall clas

关 键 词：近红外 隐蔽性害虫 极限学习机 分类 米象 早期诊断 

分 类 号：TS210[轻工技术与工程—粮食、油脂及植物蛋白工程]