Efficient Pomegranate Segmentation with UNet: A Comparative Analysis of Backbone Architectures and Knowledge Distillation

Abstract

This work examines the segmentation of on-field images of pomegranate fruit using UNet model with different backbones. Precise and effective segmentation of pomegranate fruits on the field is essential for automating yield estimation, disease detection, and quality evaluation in the agricultural industry. The models have been trained and validated using actual images captured in a pomegranate field. The study assesses the performance of many backbones, including ResNet50, Inception ResNetV2, MobileNetv2, DenseNet121, EfficientNet, VGG16, and VGG19. The VGG19 backbone achieved the highest F1 score, 90.35%, according to the data. In addition, we employed feature-based knowledge distillation to move the knowledge from the VGG19 backbone to the lighter MobileNetv2 backbone (45x smaller than VGG19 in number of parameters), which increased the F1 score of MobileNetv2 from 86.97% to 89.91%. Our findings show that the effectiveness of the UNet model for pomegranate fruit segmentation is greatly impacted by the selection of the backbone architecture, and that knowledge distillation can improve the accuracy of UNet models with lighter backbones without significantly increasing their computational complexity.