Food Quality Assessment Based on Deep Learning Models

Abstract

Objective. In this paper, a novel dataset has been collected in accordance with Pakistani needs and is used to develop an architecture for the quality assessment of fruits and vegetables. Approach. The dataset contains images captured under uncontrolled conditions with respect to illumination, temperature, humidity, image resolution, image aspect ratio, angle of capturing images and background. Images captured contain items individually as well as in groups. To the best of the knowledge gathered, this is the first of its kind dataset. This dataset is then preprocessed. Among usual preprocessing techniques, an aspect ratio adjustment algorithm has been introduced. After preprocessing, the data is used to train multiple models (AlexNet, VGG-16, ResNet-50, Fruits-360 Model and a proposed model with relatively lesser depth). This performs recognition of fruits and vegetables and endorse the validity of the dataset. Going further, the dataset is then prepared for quality assessment with three quality labels for each fruit/vegetable: Eatable, Partially Rotten and Rotten. Quality assessment is then performed using pre-trained VGG- 16 through transfer learning, adding a fully connected network and fine-tuning the model. Main Results. The highest recognition accuracy on the validation set is 98.9% and the highest validation accuracy for quality assessment is 92.9%. Significance. Outcomes of this research demonstrate that dataset collected under an uncontrolled environment can be used for recognition of fruits/vegetables with remarkable accuracies. Moreover, quality assessment of fruits/vegetables is performed accurately with the same dataset using deep learning and three quality labels.