Hyperspectral Imaging for cherry tomato
This work addresses quality assessment for cherry tomato producers and consumers, but it is incremental as it applies a novel deep learning method to an existing agricultural problem.
The researchers tackled non-destructive testing of soluble solids content (SSC) and firmness in cherry tomatoes using hyperspectral imaging and a deep learning model, achieving improvements of 26.4% for SSC and 33.7% for firmness compared to state-of-the-art techniques.
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.