Fine-tuning ConvNets with Novel Leather Image Data for Species Identification

This paper introduces deep learning (DL) for leather species identification. It exploits the application of transfer learning on the existing Convolutional Neural Networks (ConvNets). The application of transfer learning fine-tunes the ConvNet parameters to learn the novel leather image data. This research investigates the performance of four ConvNets, namely, AlexNet, VGG16, GoogLeNet, and ResNet18, to predict the leather species. The comparative study affirms the efficacy of ResNet18 in learning the complex pore structural behavior of leather images. It efficiently classifies the leather images into four respective species with the highest accuracy (99.69%). It outperforms the existing ML-based prediction with a 7% improvement. Therefore, ConvNet is the best solution to deal with inter-species similarity and intra-species variability, the practical challenges of the leather images. It thus develops a fully-automated leather species identification technique that paves the way for biodiversity preservation and consumer protection.