论文标题
樱桃番茄的高光谱成像
Hyperspectral Imaging for cherry tomato
论文作者
论文摘要
樱桃番茄（Solanum lycopersicum）由于其特殊的风味，在世界各地的消费者中很受欢迎。可溶性固体含量（SSC）和牢固性是评估产品质量的两个关键指标。在这项工作中，我们根据高光谱图像和相应的深度学习回归模型开发了SSC和水果牢固性的非破坏性测试技术。 200多个番茄水果的高光谱反射率图像衍生出400至1000 nm的光谱。校正了获得的高光谱图像并提取光谱信息。将一种新型的一维（1D）基于卷积重新NET（CON1DRESNET）的回归模型与最先进的技术进行了比较。实验结果表明，对于相对较大的样品，我们的技术比SSC的最先进技术好26.4％，坚定性33.7 \％。这项研究的结果表明，高光谱成像技术在SSC和坚硬检测中的应用潜力，这为将来对樱桃番茄果实质量的非破坏性测试提供了新的选择。
Cherry tomato (Solanum Lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and a corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with spectrum ranging from 400 to 1000 nm. The acquired hyperspectral images are corrected and the spectral information is extracted. A novel one-dimensional(1D) convolutional ResNet (Con1dResNet) based regression model is prosed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4\% better than state of art technique for SSC and 33.7\% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future.
