Prediction of weight loss in cherry tomatoes during storage using color and fluorescence images with deep learning

Abstract

Fruits and vegetables are subject to loss and waste at each stage of the food supply chain from harvest to consumption, and there is a need for technologies that contribute to loss reduction in the post-harvest food supply chain. The objective of this study was to build a model to predict the rate of weight loss using color and fluorescent images as input. Color and 365 nm excitation fluorescence images were captured and excitation emission matrix (EEM) were measured over time in cherry tomatoes of different fruit colors. Three models were constructed with red cherry tomatoes, yellow cherry tomatoes, and both red and yellow cherry tomatoes as input. As a result, RMSE, MAE, and R² for the model with input of multicolor cherry tomato images were 0.853, 0.676, and 0.660, respectively. This result was almost as accurate as the single color model. It was suggested that the rate of weight loss of cherry tomatoes of different fruit colors could be predicted using a single model.