Background: Cattle are of great importance in terms of economy, food and livelihood of Jammu and Kashmir, as well as Ladakh, owing to the varied agro-climatic conditions that favor the existence of varied cow breeds of indigenous, crossbred and even exotic types. The cattle breeding practices, which were followed in a traditional manner, have been an important part of the local environment and are largely dependent on local adaptation and local wisdom that have been accumulated over the years. The cattle are adversely affected by breed identification problems, overcrossbreeding and various infectious and parasitic diseases. 
 
Methods: Taking into account all these factors, this research aims at providing an analytical overview of cow breeds, their population, as well as diseases faced, all of this concerning the state of Jammu and Kashmir, as well as Ladakh. An experimental analysis has been conducted on how artificial intelligence can be made use of for identifying cow breeds, where a computer vision-related model has been developed that utilizes the combined power of a pre-trained model of YOLOv8x for efficient extraction of cow-related regions, followed by the use of an EfficientNetV2-S model for the identification of various cow breeds on a dataset of Indian cow images. 
 
Result: The model achieved an accuracy of 75.79%, a Top-5 accuracy of 98.11% and a Macro F1-score of approximately 0.78, indicating consistent performance across varying conditions. The results suggest that the model has potential for use in cattle breed identification as a supportive tool alongside traditional methods. The integration of conventional and AI-based approaches may contribute to improved livestock management practices. 
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An Analytical Study on Indigenous and Crossbred Cow Breeds of Jammu and Kashmir and Ladakh