Hyperspectral Imaging for cherry tomato

TL;DR

This study develops a non-destructive hyperspectral imaging method combined with a deep learning model to accurately assess cherry tomato quality metrics like SSC and firmness, outperforming existing techniques.

Contribution

Introduces a novel 1D convolutional ResNet regression model for hyperspectral data, improving accuracy in non-destructive cherry tomato quality assessment.

Findings

26.4% improvement in SSC prediction accuracy

33.7% improvement in firmness prediction accuracy

Demonstrates potential for hyperspectral imaging in fruit quality testing

Abstract

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.