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

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.

[1]  Huseyin Seker,et al.  Wavelet denoising and reconstruction of a microneedle embedded in human skin ex-vivo using terahertz pulsed reflectance , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  M. Diago,et al.  On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties , 2018, Front. Plant Sci..

[3]  Yibin Ying,et al.  Discrimination of Transgenic Rice containing the Cry1Ab Protein using Terahertz Spectroscopy and Chemometrics , 2015, Scientific Reports.

[4]  N. Kondo,et al.  Quantification of starch content in germinating mung bean seedlings by terahertz spectroscopy. , 2019, Food chemistry.

[5]  Chu Zhang,et al.  Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network , 2018 .

[6]  Jianjun Liu,et al.  Determination of transgenic organisms from non-transgenic using terahertz spectroscopy and chemometrics , 2017 .

[7]  Yibin Ying,et al.  The Detection of Agricultural Products and Food Using Terahertz Spectroscopy: A Review , 2013 .

[8]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[9]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[10]  Fartash Vasefi,et al.  Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques , 2020 .

[11]  Yi-Zeng Liang,et al.  Baseline correction using adaptive iteratively reweighted penalized least squares. , 2010, The Analyst.

[12]  M. Kim,et al.  Rapid assessment of corn seed viability using short wave infrared line-scan hyperspectral imaging and chemometrics , 2018 .

[13]  Jinbao Jiang,et al.  An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts , 2020 .

[14]  S. Marín,et al.  Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples , 2020 .

[15]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[16]  Yong He,et al.  Application of hyperspectral imaging and chemometrics for variety classification of maize seeds , 2018, RSC advances.

[17]  Peng Liu,et al.  Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1 Active Deep Learning for Classification of Hyperspectral Images , 2022 .

[18]  Nikolaos Doulamis,et al.  Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..

[19]  J. Liu,et al.  Identification of Transgenic Organisms Based on Terahertz Spectroscopy and Hyper Sausage Neuron , 2015 .

[20]  Chu Zhang,et al.  Practicability investigation of using near-infrared hyperspectral imaging to detect rice kernels infected with rice false smut in different conditions , 2020 .

[21]  Cyril C. Renaud,et al.  Advances in terahertz communications accelerated by photonics , 2016, Nature Photonics.

[22]  Thomas Dekorsy,et al.  Hydration dynamics of oriented DNA films investigated by time-domain terahertz spectroscopy , 2007 .

[23]  Madan Gopal,et al.  Least squares twin support vector machines for pattern classification , 2009, Expert Syst. Appl..

[24]  Qingshan Liu,et al.  Cascaded Recurrent Neural Networks for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Nikolay A. Kolchanov,et al.  Impact of Terahertz Radiation on Stress-Sensitive Genes of E.Coli Cell , 2016, IEEE Transactions on Terahertz Science and Technology.

[26]  Hao Wu,et al.  Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification , 2018, IEEE Transactions on Image Processing.

[27]  S LewMichael,et al.  Deep learning for visual understanding , 2016 .

[28]  G. Venkataraman,et al.  CRISPR for Crop Improvement: An Update Review , 2018, Front. Plant Sci..

[29]  Silvia Serranti,et al.  Classification of oat and groat kernels using NIR hyperspectral imaging. , 2013, Talanta.

[30]  Antonis Nikitakis,et al.  Tensor-Based Classification Models for Hyperspectral Data Analysis , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Kim-Kwang Raymond Choo,et al.  Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification , 2016, Soft Computing.

[32]  Pengcheng Nie,et al.  Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning , 2019, Sensors and Actuators B: Chemical.

[33]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[34]  U. Braun,et al.  High-throughput NIR spectroscopic (NIRS) detection of microplastics in soil , 2019, Environmental Science and Pollution Research.

[35]  Li Guo Wang,et al.  Least Squares Twin Support Vector Machines Based on Sample Reduction for Hyperspectral Image Classification , 2015 .

[36]  Wei Liu,et al.  Application of terahertz spectroscopy imaging for discrimination of transgenic rice seeds with chemometrics. , 2016, Food chemistry.

[37]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[38]  Marcin Woźniak,et al.  Multi-Level Features Extraction for Discontinuous Target Tracking in Remote Sensing Image Monitoring , 2019, Sensors.

[39]  Wei Liu,et al.  A Non-destructive Terahertz Spectroscopy-Based Method for Transgenic Rice Seed Discrimination via Sparse Representation , 2017 .

[40]  Xiao Xiang Zhu,et al.  Deep Recurrent Neural Networks for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[41]  P. Taday,et al.  Detection and identification of explosives using terahertz pulsed spectroscopic imaging , 2005 .

[42]  A. Mukherjee,et al.  Priming with salicylic acid induces defense against bacterial blight disease by modulating rice plant photosystem II and antioxidant enzymes activity , 2019 .

[43]  Longping Yuan,et al.  Development of Hybrid Rice to Ensure Food Security , 2014 .

[44]  Susanne Jacobsen,et al.  Near infrared spectra indicate specific mutant endosperm genes and reveal a new mechanism for substituting starch with (1→3,1→4)-β-glucan in barley , 2004 .

[45]  Yong He,et al.  Discrimination of CRISPR/Cas9-induced mutants of rice seeds using near-infrared hyperspectral imaging , 2017, Scientific Reports.

[46]  Jianping Chen,et al.  Quantitative Trait Loci Mapping for Bacterial Blight Resistance in Rice Using Bulked Segregant Analysis , 2014, International journal of molecular sciences.

[47]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[48]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[49]  Douglas Fernandes Barbin,et al.  Hyperspectral imaging as a powerful tool for identification of papaya seeds in black pepper , 2019, Food Control.

[50]  Yuan Zhang,et al.  Identification of Transgenic Ingredients in Maize Using Terahertz Spectra , 2017, IEEE Transactions on Terahertz Science and Technology.

[51]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[52]  Junwen Zhang,et al.  Screening of maize haploid kernels based on near infrared spectroscopy quantitative analysis , 2019, Comput. Electron. Agric..