title: Classification of maize kernels using NIR hyperspectral imaging.
abstract: NIR hyperspectral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and soft. Two approaches, pixel-wise and object-wise, were investigated to group kernels according to hardness. The pixel-wise classification assigned a class to every pixel from individual kernels and did not give acceptable results because of high misclassification. However by using a predefined threshold and classifying entire kernels based on the number of correctly predicted pixels, improved results were achieved (sensitivity and specificity of 0.75 and 0.97). Object-wise classification was performed using two methods for feature extraction - score histograms and mean spectra. The model based on score histograms performed better for hard kernel classification (sensitivity and specificity of 0.93 and 0.97), while that of mean spectra gave better results for medium kernels (sensitivity and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale.
authors.affiliation: Department of Food Science, Stellenbosch University, Private Bag X1, Matieland (Stellenbosch) 7602, South Africa. Electronic address: pauljw@sun.ac.za.
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Williams
authors.affiliation: Department of Chemistry and Bioscience, Aalborg University, Esbjerg, Denmark.
authors.forename: Sergey
authors.initials: S
authors.lastname: Kucheryavskiy
mesh_terms: D006244:Hardness; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D003313:Zea mays / Q000737:chemistry* / Q000145:classification*
keywords: Maize; NIR hyperspectral imaging; Object-wise classification; Pixel-wise classification