Related references
Note: Only part of the references are listed.Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds
Gamal ElMasry et al.
PLANT METHODS (2019)
Changes in morphology, chlorophyll fluorescence performance and Rubisco activity of soybean in response to foliar application of ionic titanium under normal light and shade environment
Sajad Hussain et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2019)
The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy
Yi Liu et al.
REMOTE SENSING (2019)
Identification of soybean varieties by terahertz spectroscopy and integrated learning method
Hui Luo et al.
OPTIK (2019)
Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize
F. Zhang et al.
BMC ECOLOGY (2019)
Hyperspectral imaging as a powerful tool for identification of papaya seeds in black pepper
Imer Orrillo et al.
FOOD CONTROL (2019)
Innovative and rapid analysis for rice authenticity using hand-held NIR spectrometry and chemometrics
Ernest Teye et al.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY (2019)
Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties
Susu Zhu et al.
MOLECULES (2019)
Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network
Susu Zhu et al.
SENSORS (2019)
Current and future applications of statistical machine learning algorithms for agricultural machine vision systems
Tanzeel U. Rehman et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)
Classification of Frozen Corn Seeds Using Hyperspectral VIS/NIR Reflectance Imaging
Jun Zhang et al.
MOLECULES (2019)
Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis
Insuck Baek et al.
SENSORS (2019)
Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network
Na Wu et al.
RSC ADVANCES (2019)
Quantitative detection of mixed pesticide residue of lettuce leaves based on hyperspectral technique
Jun Sun et al.
JOURNAL OF FOOD PROCESS ENGINEERING (2018)
Near-infrared spectroscopy used to predict soybean seed germination and vigour
Maythem Al-Amery et al.
SEED SCIENCE RESEARCH (2018)
Using Image Texture and Spectral Reflectance Analysis to Detect Yellowness and Esca in Grapevines at Leaf-Level
Hania Al-Saddik et al.
REMOTE SENSING (2018)
Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network
Zhengjun Qiu et al.
APPLIED SCIENCES-BASEL (2018)
Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging
Lei Feng et al.
MOLECULES (2018)
Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging
Lei Feng et al.
MOLECULES (2018)
Fluorescence Hyperspectral Imaging of Oil Samples and Its Quantitative Applications in Component Analysis and Thickness Estimation
Wentao Jiang et al.
SENSORS (2018)
Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis
Aimin Miao et al.
SENSORS (2018)
Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology
Jinnuo Zhang et al.
APPLIED SCIENCES-BASEL (2018)
CLASSIFICATION OF SUGAR BEETS BASED ON HYPERSPECTRAL AND EXTREME LEARNING MACHINE METHODS
R. Yang et al.
APPLIED ENGINEERING IN AGRICULTURE (2018)
Identification of Wine According to Grape Variety Using Near-Infrared Spectroscopy Based on Radial Basis Function Neural Networks and Least-Squares Support Vector Machines
Jing Yu et al.
FOOD ANALYTICAL METHODS (2017)
Discrimination of gluten-free oats from contaminants using near infrared hyperspectral imaging technique
Chyngyz Erkinbaev et al.
FOOD CONTROL (2017)
Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance
Oksana Sytar et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2017)
Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds
Lu Wang et al.
FOOD ANALYTICAL METHODS (2016)
Neighborhood mutual information and its application on hyperspectral band selection for classification
Yao Liu et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2016)
Predicting Soybean Relative Maturity and Seed Yield Using Canopy Reflectance
Brent S. Christenson et al.
CROP SCIENCE (2016)
Application of denaturing high-performance liquid chromatography for rice variety identification and seed purity assessment
Maren Livaja et al.
MOLECULAR BREEDING (2016)
Discrimination of transgenic soybean seeds by terahertz spectroscopy
Wei Liu et al.
SCIENTIFIC REPORTS (2016)
Plant Seed Species Identification from Chemical Fingerprints: A High-Throughput Application of Direct Analysis in Real Time Mass Spectrometry
Ashton D. Lesiak et al.
ANALYTICAL CHEMISTRY (2015)
Classification of contaminants from wheat using near-infrared hyperspectral imaging
Lankapalli Ravikanth et al.
BIOSYSTEMS ENGINEERING (2015)
Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification
Xiaoling Yang et al.
SENSORS (2015)
Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades
Qingxi Tong et al.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2014)
Effects of divergent selection for seed protein content in high-protein vs. food-grade populations of early maturity soybean
Takashi Sato et al.
PLANT BREEDING (2014)
Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)
G. R. Mahajan et al.
PRECISION AGRICULTURE (2014)
Identification of Wheat Varieties with a Parallel-Plate Capacitance Sensor Using Fisher's Linear Discriminant Analysis
C. V. K. Kandala et al.
JOURNAL OF SENSORS (2014)
Application of hyperspectral imaging technology to discriminate different geographical origins of Jatropha curcas L. seeds
Junfeng Gao et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2013)
Identification of earl millet cultivars using both microsatellites and enzymatic markers
R. P. Mendonca Neto et al.
GENETICS AND MOLECULAR RESEARCH (2013)
The Identification of Single Soybean Seed Variety by Laser Light Backscattering Imaging
Dazhou Zhu et al.
SENSOR LETTERS (2012)
Comparisons of two hand-held, multispectral field radiometers and a hyperspectral airborne imager in terms of predicting spring wheat grain yield and quality by means of powered partial least squares regression
Stein Ivar Overgaard et al.
JOURNAL OF NEAR INFRARED SPECTROSCOPY (2010)
Share Paper
