Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods
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Chu Zhang | Susu Zhu | Yong He | Baohua Wu | Fei Liu | Zhenzhu Su | Lei Feng | Junmin Wang | Fei Liu | Yong He | Susu Zhu | Chu Zhang | Lei Feng | Zhenzhu Su | Junmin Wang | Baohua Wu | Chu Zhang
[1] Chu Zhang,et al. Detection of Sclerotinia Stem Rot on Oilseed Rape (Brassica napus L.) Based on Laser- Induced Breakdown Spectroscopy , 2019, Transactions of the ASABE.
[2] A. A. Gomes,et al. Simultaneous Classification of Teas According to Their Varieties and Geographical Origins by Using NIR Spectroscopy and SPA-LDA , 2014, Food Analytical Methods.
[3] Chu Zhang,et al. Detection of Subtle Bruises on Winter Jujube Using Hyperspectral Imaging With Pixel-Wise Deep Learning Method , 2019, IEEE Access.
[4] Budiman Minasny,et al. Convolutional neural network for soil microplastic contamination screening using infrared spectroscopy. , 2019, The Science of the total environment.
[5] Kutubuddin A Molla,et al. Understanding sheath blight resistance in rice: the road behind and the road ahead , 2019, Plant biotechnology journal.
[6] Debashree Sengupta,et al. Deployment of Genetic and Genomic Tools Toward Gaining a Better Understanding of Rice-Xanthomonas oryzae pv. oryzae Interactions for Development of Durable Bacterial Blight Resistant Rice , 2020, Frontiers in Plant Science.
[7] Teresa Flores,et al. Rapid identification of Huanlongbing-infected citrus plants using laser-induced breakdown spectroscopy of phloem samples. , 2018, Applied optics.
[8] Narangerel Altangerel,et al. In vivo diagnostics of early abiotic plant stress response via Raman spectroscopy , 2017, Proceedings of the National Academy of Sciences.
[9] Ricard Boqué,et al. Data fusion methodologies for food and beverage authentication and quality assessment - a review. , 2015, Analytica chimica acta.
[10] Yidan Bao,et al. Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties , 2019, Molecules.
[11] Zhou Jianmin,et al. Responses of Leaf Cuticles to Rice Blast: Detection and Identification Using Depth-Profiling Fourier Transform Mid-Infrared Photoacoustic Spectroscopy. , 2020, Plant disease.
[12] C Baumgartner,et al. Prediction model optimization using full model selection with regression trees demonstrated with FTIR data from bovine milk. , 2019, Preventive veterinary medicine.
[13] Ricardo N M J Páscoa,et al. Varietal discrimination of hop pellets by near and mid infrared spectroscopy. , 2018, Talanta.
[14] Yong He,et al. Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging , 2019, Foods.
[15] Chu Zhang,et al. Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging , 2018, Molecules.
[16] Chen Yuan,et al. Identification of Stable Quantitative Trait Loci for Sheath Blight Resistance Using Recombinant Inbred Line , 2019, Rice Science.
[17] Chu Zhang,et al. Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral imaging. , 2020, Food chemistry.
[18] A. Smilde,et al. On the increase of predictive performance with high-level data fusion. , 2011, Analytica Chimica Acta.
[19] Federico Castanedo,et al. A Review of Data Fusion Techniques , 2013, TheScientificWorldJournal.
[20] Xing Chen,et al. Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images , 2016, J. Sensors.
[21] Peijun Du,et al. Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging , 2016, Neurocomputing.
[22] Roberto Oberti,et al. Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps , 2005, Real Time Imaging.
[23] 이창기,et al. Convolutional Neural Network를 이용한 한국어 영화평 감성 분석 , 2016 .
[24] Charles Farber,et al. Advanced spectroscopic techniques for plant disease diagnostics. A review , 2019, TrAC Trends in Analytical Chemistry.
[25] Da-Wen Sun,et al. Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds , 2015, Food Analytical Methods.
[26] Jifeng Shen,et al. Nondestructive identification of green tea varieties based on hyperspectral imaging technology , 2018 .
[27] Ping Jiang,et al. Research on optimal predicting model for the grading detection of rice blast , 2019 .
[28] Christian Nansen,et al. Hyperspectral imaging to characterize plant–plant communication in response to insect herbivory , 2018, Plant Methods.
[29] K. Moffett,et al. Remote Sens , 2015 .
[30] Andrea D. Magrì,et al. Data-fusion for multiplatform characterization of an Italian craft beer aimed at its authentication. , 2014, Analytica chimica acta.
[31] Marco Mazzotti,et al. ATR‐FTIR Spectroscopy , 2012 .
[32] Anne-Katrin Mahlein,et al. Fusion of sensor data for the detection and differentiation of plant diseases in cucumber , 2014 .
[33] Hanping Mao,et al. Portable Rice Disease Spores Capture and Detection Method Using Diffraction Fingerprints on Microfluidic Chip , 2019, Micromachines.
[34] Fei Liu,et al. Mid-infrared spectroscopy combined with chemometrics to detect Sclerotinia stem rot on oilseed rape (Brassica napus L.) leaves , 2017, Plant Methods.
[35] Martin R. McAinsh,et al. ATR-FTIR spectroscopy non-destructively detects damage-induced sour rot infection in whole tomato fruit , 2018, Planta.
[36] Bosoon Park,et al. Detection of Citrus Huanglongbing by Fourier Transform Infrared—Attenuated Total Reflection Spectroscopy , 2010, Applied spectroscopy.
[37] Alan K. Knapp,et al. LEAF OPTICAL PROPERTIES IN HIGHER PLANTS , 2001 .
[38] G. Carter,et al. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. , 2001, American journal of botany.
[39] Tariq Umer,et al. Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion , 2019, Sustain. Comput. Informatics Syst..
[40] Chu Zhang,et al. Discrimination of Chrysanthemum Varieties Using Hyperspectral Imaging Combined with a Deep Convolutional Neural Network , 2018, Molecules.
[41] Chu Zhang,et al. Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis , 2018, Scientific Reports.
[42] U. Knauer,et al. Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images , 2017, Plant Methods.
[43] Marco Orlandi,et al. Chemical profiling and multivariate data fusion methods for the identification of the botanical origin of honey. , 2018, Food chemistry.
[44] Paul Scheunders,et al. Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform , 2018 .
[45] Yong He,et al. Information fusion of emerging non-destructive analytical techniques for food quality authentication: A survey , 2020 .
[46] Syed Aziz Shah,et al. Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves , 2019, Plant Methods.
[47] Gang Wang,et al. Spectral-spatial classification of hyperspectral image using autoencoders , 2013, 2013 9th International Conference on Information, Communications & Signal Processing.
[48] Yong He,et al. Challenging applications for multi-element analysis by laser-induced breakdown spectroscopy in agriculture: A review , 2016 .
[49] Stefan Thomas,et al. Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform , 2018, Plant Methods.