文献类型：期刊文献

英文题名：Diagnosis of Plant Cold Damage Based on Hyperspectral Imaging and Convolutional Neural Network

作者：Yang, Wei[1];Yang, Ce[2];Hao, Ziyuan[1];Xie, Chuanqi[3];Li, Minzan[1]

机构：[1]China Agr Univ, Key Lab Modern Precis Agr Syst Integrat Res, Beijing 100083, Peoples R China;[2]Univ Minnesota, Dept Bioprod & Biosyst Engn, St Paul, MN 55108 USA;[3]East China Univ Sci & Technol, Dept Food Sci & Engn, Shanghai 200237, Peoples R China

年份：2019

卷号：7

起止页码：118239

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20205009614331);WOS:【SCI-EXPANDED(收录号:WOS:000484355000001)】；

基金：This work was supported in part by the Chinese National Key Research and Development Plan under Grant 2016YFD0700300-2016YFD0700304, in part by the Basic Research Funding of the China Agricultural University under Grant 2019TC049, and in part by the MNDrive Robotics, Sensors and Advanced Manufacturing Initiative at the University of Minnesota.

语种：英文

外文关键词：CNN; deep learning; cold damage; hyperspectral imaging; high-throughput phenotyping; corn

摘要：Cold damage is one of the disasters that cause significant loss and irreversible damage in crop production. To avoid yield loss, high-throughput phenotyping can be used to select the crop varieties with cold stress resistance. Nowadays, non-destructive spectral image analysis has become an effective way and is widely used in high-throughput phenotyping, which reflects the structural, physiological, biochemical characteristics and traits of plant structure and composition, plant growth and development processes and outcomes. This study used convolutional neural network (CNN) model to extract spectral features in the visible-near-infrared range to estimate cold damage of corn seedlings. The hyperspectral images of cold treated corn seedlings from five varieties were used as research objects in this study. The spectral range of the images was 450-885nm. Gaussian low-pass filter and the Savitzky-Golay smoothing method combined with the first-order derivative was used to do pre-processing for spectral data. For each corn variety, 3600 pixel samples obtained from the selected region of interests in each variety of corn seedlings were used for the CNN modeling. After the CNN modeling, 400 pixel samples extracted from the hyperspectral images were used as the testing set for each variety. Finally, a 10-layer knot CNN model was determined by analyzing the classification accuracy and computational efficiency. CNN detected the cold damage level of different types of corn seedlings as W22 (41.8 %), BxM (35%), B73 (25.6%), PH207 (20%), Mo17 (14%), which had high correlation with the ranking given by chemical method. The coefficient of correlation between cold damage detection results of CNN and results from chemical method is 0.8219. Therefore, it proves that spectral analysis based on CNN modeling can provide reference for detecting cold damage in corn seedlings.