文献类型：期刊文献

英文题名：Deep Convolutional Neural Network for Detection and Prediction of Waxy Corn Seed Viability Using Hyperspectral Reflectance Imaging

作者：Zhao, Xiaoqing[1];Pang, Lei[2];Wang, Lianming[1];Men, Sen[3,4];Yan, Lei[1]

第一作者：Zhao, Xiaoqing

通讯作者：Yan, L[1]

机构：[1]Beijing Forestry Univ, Sch Technol, Beijing 100083, Peoples R China;[2]Capital Univ Phys Educ & Sports, Inst Artificial Intelligence Sports, Beijing 100191, Peoples R China;[3]Beijing Union Univ, Coll Robot, Beijing 100020, Peoples R China;[4]Beijing Union Univ, Beijing Engn Res Ctr Smart Mech Innovat Design Ser, 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：2022

卷号：27

期号：6

外文期刊名：MATHEMATICAL AND COMPUTATIONAL APPLICATIONS

收录：WOS:【ESCI(收录号:WOS:000902699900001)】；

基金：This research was funded by (1) the Opening Foundation of Key Lab of State Forestry Administration on Forestry Equipment and Automation (Grant BFUKF202220); (2) the General Program of Science and Technology Development Project of the Beijing Municipal Education Commission of China (Grant KM201911417008); (3) the National Natural Science Foundation of China (Grant No. 31770769).

语种：英文

外文关键词：hyperspectral imaging; deep convolutional neural network; waxy corn seeds; viability detection

摘要：This paper aimed to combine hyperspectral imaging (378-1042 nm) and a deep convolutional neural network (DCNN) to rapidly and non-destructively detect and predict the viability of waxy corn seeds. Different viability levels were set by artificial aging (aging: 0 d, 3 d, 6 d, and 9 d), and spectral data for the first 10 h of seed germination were continuously collected. Bands that were significantly correlated (SC) with moisture, protein, starch, and fat content in the seeds were selected, and another optimal combination was extracted using a successive projection algorithm (SPA). The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and deep convolutional neural network (DCNN) approaches were used to establish the viability detection and prediction models. During detection, with the addition of different levels, the recognition effect of the first three methods decreased, while the DCNN method remained relatively stable (always above 95%). When using the previous 2.5 h data, the prediction accuracy rate was generally higher than the detection model. Among them, SVM + full band increased the most, while DCNN + full band was the highest, reaching 98.83% accuracy. These results indicate that the combined use of hyperspectral imaging technology and the DCNN method is more conducive to the rapid detection and prediction of seed viability.