Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network
Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network
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DOI：
10.1016/j.optlastec.2020.106861
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发表时间：
2021-01-01
影响因子：
5
通讯作者：
Mehta, Dalip Singh
中科院分区：
文献类型：
--
作者：
Joshi, Deepa;Butola, Ankit;Mehta, Dalip Singh
种子品种的鉴定是保证种子质量和保证作物高产的关键。现有的品种鉴定方法主要依靠目视检查和DNA指纹。虽然DNA指纹图谱的模式可以精确的种子品种的分类，但充满了挑战，如在密切相关的物种之间的多态性率低，破坏性的分析方法和巨大的成本涉及到强大的标记，如简单重复序列（SSR）和单核苷酸多态性的鉴定。在这里，我们提出了一种快速，非接触和非侵入性的技术，深度学习辅助光学相干断层扫描（OCT）用于地下成像，以区分不同的种子品种。利用OCT技术获得了（a）4个水稻品种（PUSA Basmati 1、PUSA 1509、PUSA 44和IR 64）和（B）7个形态相似的水稻地方品种Pokkali种子的体积数据集。实现前馈深度神经网络用于深度特征提取并将OCT图像分类到其相关类别中。所提出的方法提供了89.6%的总158，421 OCT图像的数据集和82.5%的总56，301 OCT图像收集Pokkali种子的数据集分类的分类准确率。目前的技术可以准确地对种子品种进行分类，而不考虑形态相似性，并可用于去除品种重复和评估种子的纯度。
Identification of the seed varieties is essential in the quality control and high yield crop growth. The existing methods of varietal identification rely primarily on visual examination and DNA fingerprinting. Although the pattern of DNA fingerprinting allows precise classification of seed varieties but fraught with challenges such as low rate of polymorphism amongst closely related species, destructive method of analysis and a huge cost involved in identification of robust markers such as simple sequence repeat (SSR) and single nucleotide polymorphisms. Here, we propose a fast, non-contact and non-invasive technique, deep learning assisted optical coherence tomography (OCT) for subsurface imaging in order to distinguish different seed varieties. The volumetric dataset of, (a) four rice varieties (PUSA Basmati 1, PUSA 1509, PUSA 44 and IR 64) and, (b) seven morphologically similar seeds of rice landrace Pokkali was acquired using OCT technique. A feedforward deep neural network is implemented for deep feature extraction and to classify the OCT images into their relevant classes. The proposed method provides the classification accuracy of 89.6% for the dataset of total 158,421 OCT images and 82.5% in classifying the dataset of total 56,301 OCT images collected from Pokkali seeds. The current technique can accurately classify seed varieties irrespective of the morphological similarities and can be adopted for the removal of varietal duplication and assessment of the purity of the seeds.