Fruit Defect Prediction Model (FDPM) based on Three-Level Validation

It is a known fact that infrared radiation is produced by all objects with a temperature above absolute zero. In fruits, IR light sensor senses the invisible areas and exposes obscure objects in the image. In normal RGB images, it is very difficult to predict the internal defect of the fruit accurately without chopping it into pieces. In this paper, a thermal IR imaging-based fruit defect detection technique is proposed to identify and estimate the internal defect in the pome fruits. The technique is non-invasive and non-destructive which helps in minimizing the fruit wastage during the quality check. To achieve high accuracy, a three-level validation process is adopted. First level involves the prediction made by the proposed Deep learning-based expert system using RGB and thermal images of apple respectively. In second level, the validation of results is done using Fourier Transform Infrared spectroscopy technique. And finally, in third level, an invasive destructive method is used for inspection of the fruit quality by cutting them into pieces. The apple defect detection accuracy by the proposed Naive Bayes classifier is observed to be 97.6% for thermal IR imaging samples whereas true color based achieved only 59% for the same sample. An apple seems to be healthy externally but internally there is a probability of a defect. Thermal IR imaging detects the heat from the surface of the fruit. Due to defective tissues of fruit pulp, the non-uniform temperature difference is observed and sensed on the surface of the fruit.