Mr. R. Bhanu Sankar, D. Vasanth, Yuvaraj Singh, T. Harish, Y. Kumar 03 03 Dog breed identification is a challenging finegrained visual classification task due to high interbreed similarity and substantial intrabreed variation in appearance pose and lighting Accurate automated identification is valuable for pet management veterinary care lost pet recovery and animal research This paper proposes a Hybrid Deep Learning approach that combines the complementary strengths of ResNet101 InceptionV3 and Xception architectures through ensemble feature fusion and weighted majority voting The hybrid system leverages transfer learning from ImageNet pretrained weights and applies extensive data augmentation to improve generalization on the Stanford Dogs dataset 120 breeds 20580 images A comparative analysis evaluates individual and hybrid models Experimental results demonstrate that the proposed hybrid approach achieves 914 top1 accuracy outperforming individual models by 37 offering a robust solution for automated dog breed recognition Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026