Prediction Model of Thermal Properties of Fruits and Vegetables Based on Random Forest and Fusion Model

摘要

This study is aimed to model the complicated correlation between physicochemical property and thermal property of fruits and vegetables. And this study predicts thermal property values with higher accuracy based on fusion model and Pearson correlation analysis. Thermal property is important for the storage and transportation of fruits and vegetables. And it's an important factor of fruits and vegetables fresh-keeping. To design a fresh-keeping device, it's necessary to measure the thermal properties of fruits and vegetables. Some people use complicated devices to measure thermal properties directly or establish physical thermal model to analyze thermal properties. But it's difficult to use only physical thermal model to express the complicated correlation between physicochemical properties and thermal properties. Leaning machine is a new way to model the complicated correlation among multiple dimensions attributes. At first, this study uses Pearson correlation analysis to analyze the correlation between physicochemical property and thermal property. This step is to choose predictors which will be used to predict the thermal properties. This study uses BP neural network, random forest and GBDT algorithm to predict thermal properties of fruits and vegetables. And after testing models with 700 sets of original data, the test result shows that all of these three models have good performance. To get higher accuracy of prediction, this study uses BP NN and random forest to establish fusion model which means it uses random forest to fuse the prediction result of random forest, BP neural network and the original predictors. The performance of fusion model is 89.3% (R2) for prediction of Thermal conductivity and 96.3% (R-square) for prediction of Freezing point. The test result shows that fusion model has better performance and higher accuracy to predict thermal properties of fruits and vegetables. This study was conducted in Electrical and Information School, Jinan University from Apr. 15th 2015 to Mar. 1st 2016.