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Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning
Infrared Physics & Technology ( IF 3.3 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.infrared.2020.103550
Chu Zhang , Yiying Zhao , Tianying Yan , Xiulin Bai , Qinlin Xiao , Pan Gao , Mu Li , Wei Huang , Yidan Bao , Yong He , Fei Liu

Abstract Tracing the varieties of seeds is of great importance for the seed industry. Maize kernels for planting are generally coated to protect kernels from fungi and insects. In this study, near-infrared hyperspectral imaging ranging from 874 nm to 1734 nm was used to identify the varieties of coated maize kernels. Spectral data were extracted and preprocessed. Logistic regression (LR) and support vector machine (SVM), convolutional neural network (CNN), recurrent neural network (RNN) and Long Short-Term Memory (LSTM) were used to build classification models. Furthermore, principal component analysis (PCA), CNN, RNN and LSTM were adopted to extract features. The extracted features were fused as the inputs of the classification models. Classification models using full spectra, extracted features and fused features obtained performances with the classification accuracy over 90% in the calibration, validation and prediction sets of most models. Models using extracted features obtained equivalently or slightly worse results than those using full spectra. The models using fused features all obtained good performances, with the classification accuracy over 90% in all sets. The overall results illustrated that near-infrared hyperspectral imaging with deep learning methods was a useful alternative for identifying coated maize varieties.

中文翻译:

近红外高光谱成像在深度学习包衣玉米籽粒品种鉴定中的应用

摘要 种子品种溯源对种业具有重要意义。用于种植的玉米粒通常经过涂层处理,以保护玉米粒免受真菌和昆虫的侵害。在这项研究中,从 874 nm 到 1734 nm 的近红外高光谱成像用于识别包衣玉米粒的品种。光谱数据被提取和预处理。使用逻辑回归 (LR) 和支持向量机 (SVM)、卷积神经网络 (CNN)、循环神经网络 (RNN) 和长短期记忆 (LSTM) 来构建分类模型。此外,采用主成分分析(PCA)、CNN、RNN和LSTM来提取特征。提取的特征融合为分类模型的输入。使用全光谱的分类模型,在大多数模型的校准、验证和预测集中,提取特征和融合特征获得了超过 90% 的分类准确率的性能。使用提取特征的模型获得的结果与使用全光谱的模型相同或略差。使用融合特征的模型都获得了良好的性能,所有集合的分类准确率均超过 90%。总体结果表明,采用深度学习方法的近红外高光谱成像是识别包膜玉米品种的有用替代方法。在所有集合中分类准确率均超过 90%。总体结果表明,采用深度学习方法的近红外高光谱成像是识别包膜玉米品种的有用替代方法。在所有集合中分类准确率均超过 90%。总体结果表明,采用深度学习方法的近红外高光谱成像是识别包膜玉米品种的有用替代方法。
更新日期:2020-12-01
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