Classification of maize kernels using NIR hyperspectral imaging

• Maize kernel hardness was studied by NIR hyperspectral imaging.
• Pixel-wise and object-wise classification was used to categorise kernels.
• Pixel-wise approach did not give acceptable results due to high misclassification.
• Using a threshold on correctly predicted pixels gave improved results.
• Object-wise classification was superior and can be recommended for industry.

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.