Development of deep learning based user-friendly interface for fruit quality detection

Abstract

The implementation of deep learning algorithms has contributed to various applications related to the detection of fruit quality. The quality attributes of fruit such as total soluble solids, moisture content, pH, colour changes, and firmness at different varieties can be predicted according to different storage conditions with reliable classification accuracy. The advances in non-destructive techniques have led to the rapid utilisation of the imaging approach in order to monitor the fruit quality. These image datasets encompass diverse information which requires extensive data extraction. To overcome this issue, a deep learning approach using convolutional neural network was used to evaluate the fruit quality. A graphical user interface-based software (DLFRUIT-GUI) for data processing of fruit quality is developed. The toolbox allows the model training and selection based on the image datasets of the fruit. The software offers a push-button approach to establish deep learning models for monitoring fruit quality. The adoption of convolutional neural network model successfully improves the model performance which demonstrated efficient results in predicting the fruit quality at different varieties according to various storage conditions. The DLFRUIT-GUI toolbox provides rapid monitoring of fruit quality detection that can easily be accessible by users who have no programming skills and tedious data analysis.