Rapid and non-destructive seed viability prediction using near-infrared hyperspectral imaging coupled with a deep learning approach
Rapid and non-destructive seed viability prediction using near-infrared hyperspectral imaging coupled with a deep learning approach
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DOI：
10.1016/j.compag.2020.105683
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发表时间：
2020-10-01
影响因子：
8.3
通讯作者：
Inagaki, Tetsuya
中科院分区：
文献类型：
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作者：
Ma, Te;Tsuchikawa, Satoru;Inagaki, Tetsuya
种子是农业食品工业的基础，在播种前更好地了解种子的生活力可以改善储存管理和田间表现。在本研究中，我们的目标是通过使用高成本效益的近红外高光谱成像（NIR-HSI）和卷积神经网络（CNN）深度学习方法来解决这个问题。使用NIR-HSI相机，因为它可以识别分子振动信息（即化学成分差异）及其在每个种子样品中的空间分布;这种相机比常规RGB数码相机信息量大得多。利用这项技术，本研究的重点首先是通过对NIR-HSI数据的主成分分析（PCA）和支持向量机（SVM）活力分类分析，提供一种增强有活力和无活力种子的可解释性的方法。然后构建一个CNN来“认知”可行和非无效种子的差异，并自动对其进行分类。实验结果表明，该方法产生了类似于90%的分类准确率为五倍交叉验证集和测试集的自然老化的日本芥菜菠菜种子。因此，本研究为种子生活力预测提供了一种新的有效和实用的策略。
Seeds are the basis of the agricultural food industry, greater insights into seed viability before sowing could improve storage management and field performance. In the present study, we aimed to address this issue by using highly cost-efficient near-infrared hyperspectral imaging (NIR-HSI) and a convolutional neural network (CNN) deep learning approach. An NIR-HSI camera was used because it can recognize both molecular vibration information (i.e. chemical component differences) and its spatial distribution in each seed sample; this camera is much more informative than a regular RGB digital camera. Using this technology, the emphasis of this study was firstly to provide a methodology for enhancing the interpretability of viable and non-viable seeds via principal component analysis (PCA) and support vector machine (SVM) viability classification analysis of NIR-HSI data. A CNN was then constructed to"cognize" the differences in viable and non-inviable seeds and classify them automatically. Experimental results indicate that the methodology produces a similar to 90% classification accuracy for both a five-fold cross-validation set and a test set of naturally aged Japanese mustard spinach seeds. Therefore, this study provides a new strategy for effective and practical seed viability prediction.