Efficient AI Driven Health Monitoring Facial Recognition in Cattle Farm

This research aims to compare the effectiveness of a Convolutional Neural Network cattle farming. Materials and Methods: Resources and Procedures: Two sets of health monitoring systems set up in a cattle farm setting were examined in this study. Group 1 made use of a dataset of 5,000 distinct cattle faces and medical records to run an A CNN-based system that tracked individual cattle health metrics like weight, activity level, and behavioral differences. A AI-driven facial recognition system was used for the same objective by Group 2. The accuracy, processing speed, and scalability of both methods were evaluated in actual farm situations. The analysis was conducted using a 95% confidence interval, a G Power value of 80%, and a significance threshold of 0.05%. Results: In terms of monitoring cattle health, the AI-powered facial recognition system outperformed the CNN-based model by a significant margin. In contrast to the CNN model, which only got 86% to 87.5% accuracy, the AI system's accuracy ranged from 92.3% to 94%. The AI system also demonstrated improved scalability and quicker processing times for large-scale cattle farms. The enhanced performance of the AI system was statistically significant at 0.0049, demonstrating its efficacy in real-time livestock monitoring. Conclusion: n terms of accuracy, speed, and scalability, the AI-powered health monitoring system that used facial recognition performed better than the CNN-based strategy. By addressing inefficiencies, streamlining health monitoring procedures, and facilitating proactive decision-making, its implementation in cattle farms can eventually improve farm productivity and animal welfare.