Varietal Classification of Rice Seeds Using RGB and Hyperspectral Images
Varietal Classification of Rice Seeds Using RGB and Hyperspectral Images
复制标题
基于RGB和高光谱图像的水稻种子品种分类
DOI：
10.1109/access.2020.2969847
复制
发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Marshall, Stephen
中科院分区：
文献类型：
--
作者：
Fabiyi, Samson Damilola;Vu, Hai;Marshall, Stephen
水稻种子检验对苗圃和农民来说是一项至关重要的任务，因为它在育苗时确保了种子质量。传统上，这一过程由专业检验员执行，他们手动筛选大量水稻种子样本以识别其品种并评估该批次的纯净度。在通过机器视觉实现筛选过程自动化的探索中，多种方法利用从RGB图像中提取的基于外观的特征，而其他方法则利用高光谱成像（HSI）系统获取的光谱信息。关于这一主题的大多数文献仅使用少量品种来对新的鉴别模型的性能进行基准测试。因此，不清楚模型性能的差异是证实了所提出算法和特征的有效性，还是仅仅可归因于数据集本身的类间/类内差异。在本文中，提出了一种利用从高分辨率RGB和高光谱图像中提取的空间和光谱特征相结合的自动筛选和分类水稻种子样本的新方法。所提出的系统使用从90个不同品种中采集的8640颗水稻种子的大型数据集进行评估。该数据集公开发布，以便于对其他现有和新提出的技术进行稳健的比较和基准测试。所提出的算法在这个大型数据集上进行了评估，实验结果表明，通过结合从高空间分辨率图像中提取的空间特征和从高光谱数据立方体中提取的光谱特征，该算法在去除不纯品种方面是有效的。
Inspection of rice seeds is a crucial task for plant nurseries and farmers since it ensures seed quality when growing seedlings. Conventionally, this process is performed by expert inspectors who manually screen large samples of rice seeds to identify their species and assess the cleanness of the batch. In the quest to automate the screening process through machine vision, a variety of approaches utilise appearance-based features extracted from RGB images while others utilise the spectral information acquired using Hyperspectral Imaging (HSI) systems. Most of the literature on this topic benchmarks the performance of new discrimination models using only a small number of species. Hence, it is unclear whether or not model performance variance confirms the effectiveness of proposed algorithms and features, or if it can be simply attributed to the inter-class/intra-class variations of the dataset itself. In this paper, a novel method to automatically screen and classify rice seed samples is proposed using a combination of spatial and spectral features, extracted from high resolution RGB and hyperspectral images. The proposed system is evaluated using a large dataset of 8,640 rice seeds sampled from a variety of 90 different species. The dataset is made publicly available to facilitate robust comparison and benchmarking of other existing and newly proposed techniques going forward. The proposed algorithm is evaluated on this large dataset and the experimental results show the effectiveness of the algorithm to eliminate impure species by combining spatial features extracted from high spatial resolution images and spectral features from hyperspectral data cubes.