To increase productivity in agricultural production, speed, and accuracy is the key requirement for long-term economic growth, competitiveness, and sustainability. Traditional manual paddy rice seed classification operations are costly and unreliable because human decisions in identifying objects and issues are inconsistent, subjective, and slow. Machine vision technology provides an alternative for automated processes, which are nondestructive, cost-effective, fast, and accurate techniques. In this work, we presented a study that utilized machine vision technology to classify 14 Oryza sativa rice varieties. Each cultivar used over 3,500 seed samples, a total of close to 50,000 seeds. There were three main processes, including preprocessing, feature extraction, and rice variety classification. We started the first process using a seed orientation method that aligned the seed bodies in the same direction. Next, a quality screening method was applied to detect unusual physical seed samples. Their physical information including shape, color, and texture properties was extracted to be data representations for the classification. Four methods (LR, LDA, k-NN, and SVM) of statistical machine learning techniques and five pretrained models (VGG16, VGG19, Xception, InceptionV3, and InceptionResNetV2) on deep learning techniques were applied for the classification performance comparison. In our study, the rice dataset were classified in both subgroups and collective groups for studying ambiguous relationships among them. The best accuracy was obtained from the SVM method at 90.61%, 82.71%, and 83.9% in subgroups 1 and 2 and the collective group, respectively, while the best accuracy on the deep learning techniques was at 95.15% from InceptionResNetV2 models. In addition, we showed an improvement in the overall performance of the system in terms of data qualities involving seed orientation and quality screening. Our study demonstrated a practical design of rice classification using machine vision technology.

中文翻译：

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基于自动分级机的机器学习技术开发水稻种子分类过程

提高农业生产的生产率，速度和准确性是长期经济增长，竞争力和可持续性的关键要求。传统的人工水稻种子分类操作成本高昂且不可靠，因为人类在确定对象和问题时的决策不一致，主观且缓慢。机器视觉技术为自动化过程提供了一种替代方法，这是一种无损，经济高效，快速而准确的技术。在这项工作中，我们提出了一项利用机器视觉技术对14种水稻品种进行分类的研究。每个品种使用了3500多个种子样本，总共接近50,000种子。共有三个主要过程，包括预处理，特征提取和水稻品种分类。我们使用种子定向方法启动了第一个过程，该方法将种子体按相同方向对齐。接下来，采用质量筛选方法来检测异常的物理种子样品。他们的物理信息包括形状，颜色和纹理属性被提取出来作为分类的数据表示。统计机器学习技术的四种方法（LR，LDA，k-NN和SVM）和深度学习技术的五个预训练模型（VGG16，VGG19，Xception，InceptionV3和InceptionResNetV2）用于分类性能比较。在我们的研究中，水稻数据集分为亚组和集体组，以研究它们之间的歧义关系。通过SVM方法获得的最佳准确度在小组1和小组2和集体小组中分别为90.61％，82.71％和83.9％，深度学习技术的最佳准确性分别为InceptionResNetV2模型的95.15％。此外，我们在涉及种子定位和质量筛选的数据质量方面显示了系统整体性能的改进。我们的研究证明了使用机器视觉技术进行水稻分类的实用设计。