Classification of pepper seed quality based on internal structure using X-ray CT imaging

Abstract

The internal structure of a seed plays a vital role during germination. Hence, before planting the seed internal quality inspection is advantageous to produce healthy seedlings. In this study, an X-ray CT scanner was used to generate CT images of five-year-old naturally aged pepper seeds. Several processing techniques such as reslicing, feature extraction, and classification were performed on these images. The reslicing process was applied to construct three different planes, namely, transaxial, sagittal, and coronal plane images from raw CT images. Then, three images were selected from each sample (one from each plane) for feature extraction. Using a pattern recognition algorithm, the gray-level co-occurrence matrix (GLCM) was created for each image, and twenty-two types of statistical derivations were performed to generate GLCM textural features. A supervised data matrix was constructed based on the germination results of the seed samples from the images, where the seeds were divided into two classes: normal viable seeds (class-1) and nonviable & abnormal viable seeds (class-2). Supervised classification methods, such as partial least-squares discriminant analysis (PLS-DA), support vector machine (SVM), and K-nearest neighbor (KNN) were used to evaluate the best outcome. Among the tested classifiers, PLS-DA provided the highest accuracy of 88.7% with five-fold cross validation, where seven important features extracted from different angles (θ) were found from the beta coefficient to be significant in the classification of the seed. Results from this study show that X-ray CT imaging incorporated with a pattern recognition system is a robust technique to classify seeds based on their internal quality attributes.

Plant phenotyping relevance

X線CT画像と画像特徴抽出・分類を組み合わせ、種子の生存性・異常性という植物状態を推定する手法が研究の中心であり、交差検証による技術評価も行っている。

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