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The global economy greatly relies on the cattle industry, which serves as a crucial provider of food and resources for billions of individuals. Nevertheless, the industry grapples with significant challenges, including disease, climate change, and population growth. Artificial intelligence (AI) offers a potential solution to these challenges by enhancing the precision and efficiency of cattle management practices. This proposed system leverages AI, utilizing the state-of-the-art InceptionResNetV2 architecture within a Convolutional Neural Network (CNN) algorithm, to accurately differentiate between cows and buffaloes. In addition to cattle identification, it has the capacity to expand its scope to include cattle healthcare and insurance claim assessment. The system relies on a dataset containing high-resolution images of cattle from diverse regions and environments for training and validation. By fine-tuning and optimizing the InceptionResNetV2 architecture, recognized for its excellence in image classification tasks, the system becomes an invaluable tool for livestock management. Notably, this AI-driven cattle identification and healthcare assessment system possesses real-time capabilities, rendering it suitable for deployment in agricultural settings, farms, and insurance companies. The proposed system has the potential to significantly improve livestock management, thereby enhancing productivity and animal welfare, while also addressing healthcare and insurance-related aspects.
Singh et al. (Fri,) studied this question.