Application of hyperspectral imaging and chemometrics for variety classification of maize seeds

Seed variety classification is important for assessing variety purity and increasing crop yield. A hyperspectral imaging system covering the spectral range of 874–1734 nm was applied for variety classification of maize seeds. A total of 12 900 maize seeds including 3 different varieties were evaluated. Spectral data of 975.01–1645.82 nm were extracted and preprocessed. Discriminant models were developed using a radial basis function neural network (RBFNN). The influence of calibration sample size on classification accuracy was studied. Results showed that with the expansion of calibration sample size, calibration accuracy varied slightly, but prediction accuracy changed from the increasing form to the stable form. Accordingly, the optimal size of the calibration set was determined. Optimal wavelength selection was conducted by loading of principal components (PCs). The RBFNN model developed on optimal wavelengths with the optimal size of the calibration set obtained satisfactory results, with calibration accuracy of 93.85% and prediction accuracy of 91.00%. Visualization of classification map of seed varieties was achieved by applying this RBFNN model on the average spectra of each sample. Besides, the procedure to determine the optimal sample quantity proposed in this study was verified by support vector machine (SVM). The overall results indicated that hyperspectral imaging was a potential technique for variety classification of maize seeds, and would help to develop a real-time detection system for maize seeds as well as other crop seeds.

[1]  M Daszykowski,et al.  Near-infrared reflectance spectroscopy and multivariate calibration techniques applied to modelling the crude protein, fibre and fat content in rapeseed meal. , 2008, The Analyst.

[2]  Alberto Guillén,et al.  Duroc and Iberian pork neural network classification by visible and near infrared reflectance spectroscopy , 2009 .

[3]  A. Mouazen,et al.  Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms , 2011 .

[4]  Abdul Mounem Mouazen,et al.  Influence of the number of samples on prediction error of visible and near infrared spectroscopy of selected soil properties at the farm scale , 2011 .

[5]  J. S. Ribeiro,et al.  Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy. , 2011, Talanta.

[6]  Gamal ElMasry,et al.  Application of NIR hyperspectral imaging for discrimination of lamb muscles , 2011 .

[7]  T. Zhao,et al.  Classification and differentiation of the genus Peganum indigenous to China based on chloroplast trnL-F and psbA-trnH sequences and seed coat morphology. , 2011, Plant biology.

[8]  Da-Wen Sun,et al.  Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat , 2012 .

[9]  Yong He,et al.  Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds , 2012, Sensors.

[10]  Silvia Serranti,et al.  Hyperspectral Imaging for Process and Quality Control in Recycling Plants of Polyolefin Flakes , 2012 .

[11]  Yan Wang,et al.  Genetic purity testing of F1 hybrid seed with molecular markers in cabbage (Brassica oleracea var. capitata) , 2013 .

[12]  Cheng Zhong,et al.  Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods. , 2014, Food chemistry.

[13]  Da-Wen Sun,et al.  Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds , 2015, Food Analytical Methods.

[14]  Fang Cheng,et al.  Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification , 2015, Sensors.

[15]  G. Lu,et al.  On-line identification of biomass fuels based on flame radical imaging and application of radical basis function neural network techniques , 2015 .

[16]  Chu Zhang,et al.  Application of Near-Infrared Hyperspectral Imaging with Variable Selection Methods to Determine and Visualize Caffeine Content of Coffee Beans , 2016, Food and Bioprocess Technology.

[17]  Huang Min,et al.  Maize Seed Variety Classification Using the Integration of Spectral and Image Features Combined with Feature Transformation Based on Hyperspectral Imaging , 2016 .

[18]  Byoung-Kwan Cho,et al.  Assessment of seed quality using non-destructive measurement techniques: a review , 2016, Seed Science Research.

[19]  K. Lima,et al.  Near-infrared spectroscopy and variable selection techniques to discriminate Pseudomonas aeruginosa strains in clinical samples , 2016 .

[20]  Lalit Mohan Kandpal,et al.  High speed measurement of corn seed viability using hyperspectral imaging , 2016 .

[21]  Chu Zhang,et al.  Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine , 2016 .

[22]  Min Huang,et al.  Classification of maize seeds of different years based on hyperspectral imaging and model updating , 2016, Comput. Electron. Agric..

[23]  Chu Zhang,et al.  Rapid and non-destructive measurement of spinach pigments content during storage using hyperspectral imaging with chemometrics , 2017 .