Accurate soybean sorting is an important but time- and labor-intensive process in soybean production. Therefore, inexpensive and accurate sorting machines are needed. Many of the currently reported models for discriminating external defects in soybeans have used deep learning, but the high cost of deep learning is an issue. Therefore, this study aimed to discriminate external defects in soybeans using fluorescent images in addition to color images, using a model that is less expensive than deep learning. Color images and fluorescence images of soybeans at an excitation wavelength of 365 nm were taken, and visually labeled into six categories: normal, wrinkled, peeled, pests, denatured, and insect-damaged grains. Image features were extracted for both color and fluorescence images, and classification models were constructed using Support Vector Machine (SVM) with three input patterns: (a) color image features alone, (b) fluorescence image features alone, and (c) color and fluorescence image features input. The test accuracy was 75.06%, 58.28%, and 76.91%, respectively. In addition, two and four classifications were devised in order to anticipate the needs of the field. The accuracy reached 82.06% and 94.99% for four category and two category simultaneous input of color and fluorescent images, respectively. Especially in the two categories, the discrimination accuracy exceeded 90%, indicating that a highly accurate discrimination model could be created. Furthermore, as a result of visualizing the features important for discrimination using the ablation study, it was found that fluorescent images were effective in addition to color images for discrimination in the six categories. The importance of shape information, namely, perimeter and circularity, was high in the discrimination model for all categories, indicating that shape information is the most important information for discriminating external defects in soybeans. From those results, it can be concluded that the combination of conventional color images and fluorescence images is effective for classifying soybean external defects.
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