Traditional McMaster and flotation techniques for quantifying Eimeria oocysts in poultry and livestock feces suffer from low sensitivity at low shedding levels, high user-to-user variability, and inefficient data management. Objective: To evaluate a novel image analysis system, "Snoopy Coccovision Better" (SCB), which combines high-resolution microscopy, automated pattern recognition, and AI-driven differential counting. Methods: Fifty fecal samples from broiler chickens were analyzed in parallel using the standard McMaster technique, a modified Wisconsin flotation, and the SCB system. Sensitivity, specificity, turnaround time, and inter-operator agreement were compared. Results: SCB demonstrated 98.4% sensitivity (vs. 74.2% for McMaster, p<0.01) for low-level infections (<500 OPG). Turnaround time was reduced by 63% per sample. Inter-operator Cohen’s kappa improved from 0.62 (McMaster) to 0.96 (SCB). Conclusion: Snoopy Coccovision Better provides superior detection limits, reproducibility, and workflow integration, making it a transformative tool for coccidiosis monitoring and vaccine efficacy trials.