Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea.
Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA.
Sensors (Basel). 2020 Nov 26;20(23):6753. doi: 10.3390/s20236753.
In this study, conventional machine learning and deep leaning approaches were evaluated using X-ray imaging techniques for investigating the internal parameters (endosperm and air space) of three cultivars of watermelon seed. In the conventional machine learning, six types of image features were extracted after applying different types of image preprocessing, such as image intensity and contrast enhancement, and noise reduction. The sequential forward selection (SFS) method and Fisher objective function were used as the search strategy and feature optimization. Three classifiers were tested (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbors algorithm (KNN)) to find the best performer. On the other hand, in the transfer learning (deep learning) approaches, simple ConvNet, AlexNet, VGG-19, ResNet-50, and ResNet-101 were used to train the dataset and class prediction of the seed. For the supervised model development (both conventional machine learning and deep learning), the germination test results of the samples were used where the seeds were divided into two classes: (1) normal viable seeds and (2) nonviable and abnormal viable seeds. In the conventional classification, 83.6% accuracy was obtained by LDA using 48 features. ResNet-50 performed better than other transfer learning architectures, with an 87.3% accuracy which was the highest accuracy in all classification models. The findings of this study manifested that transfer learning is a constructive strategy for classifying seeds by analyzing their morphology, where X-ray imaging can be adopted as a potential imaging technique.
在这项研究中，使用 X 射线成像技术评估了传统机器学习和深度学习方法，以研究三种西瓜品种种子的内部参数（胚乳和气腔）。在传统机器学习中，在应用不同类型的图像预处理（例如图像强度和对比度增强以及降噪）后，提取了六种类型的图像特征。顺序前向选择（SFS）方法和 Fisher 目标函数被用作搜索策略和特征优化。测试了三种分类器（线性判别分析（LDA），二次判别分析（QDA）和 k-最近邻算法（KNN））以找到最佳分类器。另一方面，在迁移学习（深度学习）方法中，使用简单的 ConvNet、AlexNet、VGG-19、ResNet-50 和 ResNet-101 来训练数据集和种子的分类预测。对于有监督的模型开发（传统机器学习和深度学习），使用样本的发芽测试结果，其中种子分为两类：（1）正常有活力的种子和（2）无活力和异常有活力的种子。在传统分类中，使用 48 个特征的 LDA 获得了 83.6％的准确率。ResNet-50 的表现优于其他迁移学习架构，准确率达到 87.3％，是所有分类模型中最高的准确率。这项研究的结果表明，迁移学习是一种通过分析种子形态对种子进行分类的有效策略，其中 X 射线成像可以作为一种潜在的成像技术。
