Affiliations: [a]
School of Information Engineering, Tianjin University of Commerce, Tianjin, China
| [b]
College of Management and Economics, Tianjin University, Tianjin, China
| [c]
School of Science, Tianjin University of Commerce, Tianjin, China
| [d]
Tianjin Key Laboratory of Food Biotechnology, Tianjin University of Commerce, Tianjin, China
Correspondence:
[*]
Corresponding authors. Yancong Zhou, School of Information Engineering, Tianjin University of Commerce, Tianjin, China. E-mail: zycong78@126.com and Xiaochen Sun, College of Management and Economics, Tianjin University, Tianjin, China. E-mail: xcsun@tju.edu.cn.
Abstract: Kiwifruit has a high decay rate, in part because quality changes during storage cannot be easily monitored in real time. In order to better monitor the shelf life of kiwifruit and understand the quality changing process during storage, internal quality indexes such as hardness, respiratory intensity and TSS(Total Soluble Solid) were considered into the prediction models. The prediction models were constructed based on BPNN (Back Propagation Neural Network), Random Forest (RF) and XGBoost (eXtreme Gradient Boosting) respectively. And transfer learning algorithm was used to construct the quality prediction models with BPNN, RF, and XGBoost algorithms as the base learner. In the experiments, sample data were augmented by adding Gaussian noise, which effectively prevented the model from over-fitting. The experimental results showed that the prediction accuracy of each index based on transfer learning was better than that of individual BPNN, RF and XGBoost. Moreover, the average prediction accuracy of the models based on transfer learning was 96.2%, and that of respiratory intensity was as high as 99.4%. Therefore transfer learning can be used to effectively analyze and predict changes of kiwifruit quality indexes during storage.
Keywords: BPNN, RF, XGBoost, transfer learning, kiwifruit, quality prediction
