文献类型：期刊文献

英文题名：Rapid Vitality Estimation and Prediction of Corn Seeds Based on Spectra and Images Using Deep Learning and Hyperspectral Imaging Techniques

作者：Pang, Lei[1];Men, Sen[2,3];Yan, Lei[1];Xiao, Jiang[1]

第一作者：Pang, Lei

通讯作者：Yan, L[1]

机构：[1]Beijing Forestry Univ, Sch Technol, Beijing 100083, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100020, Peoples R China;[3]Beijing Union Univ, Beijing Engn Res Ctr, Smart Mech Innovat Design Serv, Beijing 100020, Peoples R China

第一机构：Beijing Forestry Univ, Sch Technol, Beijing 100083, Peoples R China

通讯机构：[1]corresponding author), Beijing Forestry Univ, Sch Technol, Beijing 100083, Peoples R China.

年份：2020

卷号：8

起止页码：123026-123036

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20203108992040);Scopus(收录号：2-s2.0-85088646814);WOS:【SCI-EXPANDED(收录号:WOS:000553723400001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 31770769, in part by the National Key Research and Development Program of China under Grant 2017YFC0504403, in part by the Fundamental Research Funds for the Central Universities under Grant 2015ZCQ-GX-03, and in part by the General Program of Science and Technology Development Project of Beijing Municipal Education Commission of China under Grant KM201911417008.

语种：英文

外文关键词：Convolutional neural network; hyperspectral image; image; seed vitality; spectrum

摘要：Highly viable seeds are of great significance for agricultural development, and the traditional corn seed vigor detection method is time-consuming and laborious. In this paper, the spectral and image information of hyperspectral imaging was used, and a distinction between seed vigor detection and prediction was proposed. The potential of hyperspectral imaging technology and convolutional neural networks (CNNs) to identify and predict maize seed vitality was evaluated. The hyperspectral information in 10 hours before the germination of four vigor level seeds (144 samples each) was collected. A support vector machine, extreme learning machine, and one-dimensional convolutional neural network (1DCNN) were used to model the spectral data set, comparing the effects of multidimensional scattering correction and principal component analysis. 1DCNN performed best on the original spectral data, reaching an accurate recognition of 90.11%. According to the spectral changes of the seed germination, the first three hours of data were selected for prediction, which had higher recognition accuracy than the test set. The image-based 2DCNN model achieved 99.96% accurate recognition at a fast convergence speed. By differentiating the spectra and image information, the various CNN models can achieve accurate detection and prediction, providing a framework to advance research on seed germination.