外文摘要：Cherry tomatoes are cultivated worldwide and favored by consumers of different ages. The soluble solid content (SSC) and pH are two of the most important quality attributes of cherry tomatoes. The rapid and non-destructive measurement of the SSC and pH of cherry tomatoes is of great significance to their production and consumption. In this research, hyperspectral imaging combined with a convolutional neural network with Transformer (CNN-Transformer) was utilized to analyze the SSC and pH of cherry tomatoes. Conventional machine learning and deep learning models were established for the determination of the SSC and pH. The findings demonstrated that CNN-Transformer yielded outstanding results in predicting the SSC, with the coefficient of determination of calibration (R2C), validation (R2V), and prediction (R2P) for the SSC being 0.83, 0.87, and 0.83, respectively. Relatively worse results were obtained for the pH value prediction, with R2C, R2V, and R2P values of 0.74, 0.68, and 0.60, respectively. Furthermore, the visualization of the CNN-Transformer model revealed the wavelength weight distributions, indicating that the 1380-1650 nm range served as the characteristic band for the SSC, while the spectral range at 945-1280 nm was the characteristic band for pH. In conclusion, integrating spectral information features with the attention mechanism of Transformer through a convolutional neural network can enhance the accuracy of predicting the SSC and pH for cherry tomatoes.
外文关键词：non-destructive testing;tomato quality;wavelength weight visualization;deep learning regression
作者：Chen, Liping;Zhang, Chu;Qi, Hengnian;Li, Hongyang;Chen, Fengnong;Luo, Jiahao
作者单位：Hangzhou Dianzi Univ;Huzhou Univ;Huzhou Agr Sci & Technol Dev Ctr
期刊名称：FOODS
期刊影响因子：0.0
出版年份：2024
出版刊次：13（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台