Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey.
Civil, Environmental and Geodetic Engineering, Ohio State University, OH, Columbus, USA.
J Sci Food Agric. 2020 Dec;100(15):5577-5585. doi: 10.1002/jsfa.10610. Epub 2020 Jul 24.
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 article 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.
The highest accuracy value calculated using only UV and only WL image is 94.8276% and these accuracies are obtained by Support Vector Machine (SVM) and multilayer perceptron (MLP) algorithms, respectively. However, this accuracy value is 98.2759% for the fusion image and both MLP and SVM achieved the same success.
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. © 2020 Society of Chemical Industry.
小麦是一种重要的营养物质，也是人类的重要食物来源，因为它被用于面粉和饲料生产。与许多营养物质一样，小麦在通心粉和面包生产中起着重要作用。这两种食品所用的小麦种类不同，分别是面包小麦和硬粒小麦。这两种小麦类型的严格分离对产品质量很重要。本文与传统的面包小麦和硬粒小麦种鉴定方法不同。在这项研究中，获取了这两种小麦的紫外（UV）和白光（WL）图像。使用各种机器学习（ML）方法对这些图像中的小麦类型进行分类。然后，通过基于小波的图像融合方法对这些图像进行融合。
仅使用 UV 和仅使用 WL 图像计算得到的最高精度值分别为 94.8276%和 98.2759%，这两个精度值分别是通过支持向量机（SVM）和多层感知器（MLP）算法获得的。然而，融合图像的精度值为 98.2759%，MLP 和 SVM 均取得了相同的成功。
基于小波的融合提高了所有三种学习算法的分类精度。可以得出结论，融合后图像的识别能力高于其他两个原始图像。 © 2020 化学工业协会。
