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.

本文基于高光谱图像和相应的深度学习回归模型，开发了一种用于测量甜樱桃番茄 SSC 和果实硬度的无损检测技术，实验结果表明，该技术相对于现有技术，可以使 SSC 和硬度检测的准确性分别提高 26.4％和 33.7％，这表明了高光谱成像技术在检测甜樱桃番茄果实质量方面的应用潜力，为未来的非破坏性测试提供了新的选择。