Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Frontiers in Plant Science
Jinnuo ZhangYong He

Abstract

Because bacterial blight (BB) disease seriously affects the yield and quality of rice, breeding BB resistant rice is an important priority for plant breeders but the process is time-consuming. The feasibility of using terahertz imaging technology and near-infrared hyperspectral imaging technology to identify BB resistant seeds has therefore been studied. The two-dimensional (2D) spectral images and one-dimensional (1D) spectra provided by both imaging methods were used to build discriminant models based on a deep learning method, the convolutional neural network (CNN), and traditional machine learning methods, support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The highest classification accuracy was achieved by the discriminate model based on CNN using the terahertz absorption spectra. Confusion matrixes were pictured to show the identification details. The t-distributed stochastic neighbor embedding (t-SNE) method was used to visualize the process of CNN data processing. Terahertz imaging technology combined with CNN has great potential to quickly identify BB resistant rice seeds and is more accurate than using near-infrared hyperspectral imaging.

References

Nov 25, 2003·Journal of Chemical Information and Computer Sciences·Vladimir SvetnikBradley P Feuston
Dec 4, 2012·Talanta·Silvia SerrantiGiuseppe Bonifazi
Jul 6, 2014·International Journal of Molecular Sciences·Xueying HanJianping Chen
Mar 1, 2018·Computational Intelligence and Neuroscience·Athanasios VoulodimosEftychios Protopapadakis
May 14, 2018·Environmental Science and Pollution Research International·Andrea PaulUlrike Braun
Jul 11, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Hao Wu, Saurabh Prasad
Aug 2, 2018·Frontiers in Plant Science·Deepa JaganathanGayatri Venkataraman

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Methods Mentioned

BETA
transgenic
features extraction

Software Mentioned

MATLAB
CNN
DA
Jupyter Notebook
Keras
Python
Win10
PLS
SVM
SNE

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