Analysis of mango fruit surface temperature using thermal imaging and deep learning

Abstract Thermal imaging has the potential to measure the object’s surface temperature. This study investigated the thermal behavior of mango fruit stored in a refrigerated environment. Thermal images of the fruit were collected with sufficient quality by supplying hot air to the acquisition environment. Grey-Level Co-occurrence Matrix (GLCM) features of mango images were determined to distinguish the subtle and noticeable changes. The thermal images were analyzed to find the temperature difference between the different regions of the fruit. The temperature of the bruise boundary (T bd ) was higher than the bruised center (T C ) throughout the storage period. In addition, an enhanced deep-learning model was used to predict the damaged mango. Over 10 days, 3500 thermal images were obtained from the 400 mangoes. In that, 80 % of the images were used for training, 10 % for testing, and 10 % for validation. The model achieved a classification accuracy of 99.6 %.