## 5 Conclusions and Future Directions  
Emerging technologies in data sciences are reshaping many cumbersome processes. AI provides a state-of-the-art solution for the automated identification of livestock. Accurate and faster biometric identification of animals has made smart farming smarter than ever before. In conclusion, the proposed system, DTL-AFI-VHA, is a system designed to identify and register animals through the detection and recognition of their faces and noses. The system is divided into three main modules: training an object detection model, registering the animal, and identifying the animal. The object detection model is trained using deep transfer learning to accurately detect the faces and noses of animals, and the features and descriptors of the animal's nose are extracted and stored in a database. When an animal is to be identified, FLANN is used to perform approximate nearest neighbor matching of the animal's nose features with those in the database. The purpose of this study was to develop a scalable and efficient system for identifying and registering animals using deep learning and computer vision techniques. The proposed system has demonstrated high accuracy in detecting and recognizing the faces and noses of animals, and it has the potential to be used in a variety of applications, such as livestock management, animal tracking, and conservation efforts. Overall, the proposed system has the potential to greatly improve the efficiency and accuracy of animal identification and registration. By automating the process of detecting and recognizing animals, the system can reduce the reliance on manual methods, which can be time-consuming and prone to error. In addition, the use of deep learning and computer vision techniques allows the system to scale to large datasets and handle a wide variety of animal species. One of the main benefits of the proposed system is the ability to identify and register animals in real-time or using prerecorded videos or images. This flexibility allows the system to be used in a variety of settings and situations, such as on farms, in zoos, or in conservation areas. The achievement of optimum identification accuracy of cattle would be helpful to reduce significant losses for livestock insurers. They can better control fake claims. Moreover, cattle farmers may use the proposed model to enhance the operational efficiency of their businesses and improve animal healthcare management. The proposed system represents a significant advancement in the field of animal identification and registration, and it has the potential to be used in a wide range of practical applications. By accurately and efficiently identifying and registering animals, the system can help to improve animal welfare, facilitate livestock management, and support conservation efforts. Future studies should focus on how to improve the process by eliminating the limitations of data scarcity so that the relevant data for the livestock animals are available. We have to utilize an approximate nearest neighbor search with the use of a threshold distance and a minimum number of good points to reduce the likelihood of incorrect matches, but more research work is required so that this limitation can be overcome. [PERSON], [PERSON], and [PERSON] collected data from different resources. [PERSON], [PERSON], and [PERSON] performed formal analysis and simulation. [PERSON], [PERSON], and [PERSON] contributed to writing--original draft preparation. [PERSON], [PERSON], and [PERSON]: writing--review and editing. [PERSON] and [PERSON] performed supervision. [PERSON], [PERSON], [PERSON], and [PERSON] drafted pictures and tables. [PERSON] and [PERSON] performed revision and improved the quality of the draft. All authors have read and agreed to the published version of the manuscript. This research program is partially funded by the United Insurance Company of Pakistan Ltd. in collaboration with United Software and Technologies International (Pvt) Ltd. under Grant No. UICL-06-2022/USTI-RND-006/11/2022. Not applicable.