An Intelligent Deep Learning Framework for Au-tomated Cattle Breed Recognition Using Image-Based Feature Analysis
Authors-S.Venkateswara Rao, Udari Santhoshma
Keyword-Deep Learning, Cattle Breed Classification, Convolutional Neural Network, DenseNet201, MobileNetV2, InceptionV3, Xception, Image Processing, Transfer Learning, Livestock Moni-toring, Precision Agriculture.
The identification of cattle breeds is an important task in precision livestock farming because it assists farmers in maintaining breed quality, improving breeding strategies, and enhancing over-all farm productivity. Conventional breed identification methods generally rely on manual obser-vation, making the process labor-intensive, time-consuming, and susceptible to human error. Recent advances in artificial intelligence have enabled image-based automated systems capable of performing accurate breed classification with minimal human intervention. This study presents an automated cattle breed recognition framework based on deep convolutional neural networks. The proposed approach employs image preprocessing techniques including resizing, normaliza-tion, background enhancement, and augmentation to improve image quality before classification. Four well-established transfer learning architectures—DenseNet201, MobileNetV2, Incep-tionV3, and Xception—are utilized to extract discriminative visual features from cattle images belonging to multiple breeds. The trained models are evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC analysis. Ex-perimental evaluation demonstrates that the Xception architecture provides superior classification performance among the evaluated models while maintaining strong generalization capability. The proposed framework offers an efficient, scalable, and reliable solution for automatic cattle breed identification, thereby supporting intelligent livestock management, genetic conservation, and sustainable agricultural practices.
Doi-[https://doi.org/10.5281/zenodo.21735820]