Classification of Healthy and Frozen Pomegranates Using Hyperspectral Imaging and Deep Learning

Abstract

Pomegranate is a temperature-sensitive fruit during postharvest storage. If exposed to cold temperatures above its freezing point for a long time, it will suffer from cold stress. Failure to pay attention to the symptoms that may occur during storage will result in significant damage. Identifying pomegranates susceptible to cold damage in a timely manner requires considerable skill, time and cost. Therefore, non-destructive and real-time methods offer great benefits for commercial producers. To this end, the purpose of this study is the non-destructive identification of healthy frozen pomegranates. First, healthy pomegranates were collected, and hyperspectral images were acquired using a hyperspectral camera. Then, to ensure that enough frozen pomegranates were collected for model training, all samples were kept in cold storage at 0 °C for two months. They were then transferred to the laboratory and hyperspectral images were taken from all of them again. The dataset consisted of frozen and healthy images of pomegranates in a ratio of 4:6. The data was divided into three categories, training, validation and test, each containing 1/3 of the data. Since there is a class imbalance in the training data, it was necessary to increase the data of the frozen class by the amount of its difference with the healthy class. Deep learning networks with ResNeXt, RegNetX, RegNetY, EfficientNetV2, VisionTransformer and SwinTransformer architectures were used for data analysis. The results showed that the accuracies of all models were above 99%. In addition, the accuracy values of RegNetX and EfficientNetV2 models are close to one, which means that the number of false positives is very small. In general, due to the higher accuracy of EfficientNetV2 model, as well as its relatively high precision and recall compared to other models, the F1 score of this model is also higher than the others with a value of 0.9995.