BACKGROUND Wheat, which is an essential nutrient, is an important food source for human beings because it is used in flour and feed production. As in many nutrients, wheat plays an important role in macaroni and bread production. The types of wheat used for both foods are different, namely bread and durum wheat. A strong separation of these two wheat types is important for product quality. This paper differs from the traditional methods available for the identification of bread and durum wheat species. In this study, ultraviolet (UV) and White Light (WL) images of wheat are obtained for both species. Wheat types in these images are classified by various Machine Learning (ML) methods. Afterwards, these images are fused by wavelet based image fusion method. RESULTS The highest accuracy value calculated using only UV and only WL image is 94.8276% and these accuracies are obtained by SVM and MLP algorithms, respectively. However, this accuracy value is 98.2759% for the fusion image and both MLP and SVM achieved the same success. CONCLUSION Wavelet based fusion has increased the classification accuracy of all three learning algorithms. It is concluded that the identification ability in the resulting fusion image is higher than the other two raw images. This article is protected by copyright. All rights reserved.

中文翻译：

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基于小波图像融合的面包和硬粒小麦分类

背景小麦是一种必需的营养物质，它是人类重要的食物来源，因为它用于面粉和饲料生产。与许多营养素一样，小麦在通心粉和面包生产中起着重要作用。两种食物使用的小麦种类不同，即面包和硬粒小麦。这两种小麦类型的强区分对产品质量很重要。本文不同于可用于鉴定面包和硬粒小麦品种的传统方法。在这项研究中，获得了两种小麦的紫外线 (UV) 和白光 (WL) 图像。这些图像中的小麦类型通过各种机器学习 (ML) 方法进行分类。然后，这些图像通过基于小波的图像融合方法进行融合。结果 仅使用 UV 和仅使用 WL 图像计算的最高准确度值为 94。8276%，这些准确率分别是通过 SVM 和 MLP 算法获得的。然而，融合图像的准确率为 98.2759%，MLP 和 SVM 都取得了同样的成功。结论基于小波的融合提高了所有三种学习算法的分类精度。得出的结论是，所得融合图像的识别能力高于其他两个原始图像。本文受版权保护。版权所有。得出的结论是，所得融合图像的识别能力高于其他两个原始图像。本文受版权保护。版权所有。得出的结论是，所得融合图像的识别能力高于其他两个原始图像。本文受版权保护。版权所有。