Fecal egg counts are emphasized for guiding equine helminth parasite control regimens due to the rise of anthelmintic resistance. This, however, poses further challenges, since egg counting results are prone to issues such as operator dependency, method variability, equipment requirements, and time commitment. The use of image analysis software for performing fecal egg counts is promoted in recent studies to reduce the operator dependency associated with manual counts. In an attempt to remove operator dependency associated with current methods, we developed a diagnostic system that utilizes a smartphone and employs image analysis to generate automated egg counts. The aims of this study were (1) to determine precision of the first smartphone prototype, the modified McMaster and ImageJ; (2) to determine precision, accuracy, sensitivity, and specificity of the second smartphone prototype, the modified McMaster, and Mini-FLOTAC techniques. Repeated counts on fecal samples naturally infected with equine strongyle eggs were performed using each technique to evaluate precision. Triplicate counts on 36 egg count negative samples and 36 samples spiked with strongyle eggs at 5, 50, 500, and 1000 eggs per gram were performed using a second smartphone system prototype, Mini-FLOTAC, and McMaster to determine technique accuracy. Precision across the techniques was evaluated using the coefficient of variation. In regards to the first aim of the study, the McMaster technique performed with significantly less variance than the first smartphone prototype and ImageJ (p<0.0001). The smartphone and ImageJ performed with equal variance. In regards to the second aim of the study, the second smartphone system prototype had significantly better precision than the McMaster (p<0.0001) and Mini-FLOTAC (p<0.0001) methods, and the Mini-FLOTAC was significantly more precise than the McMaster (p=0.0228). Mean accuracies for the Mini-FLOTAC, McMaster, and smartphone system were 64.51%, 21.67%, and 32.53%, respectively. The Mini-FLOTAC was significantly more accurate than the McMaster (p<0.0001) and the smartphone system (p<0.0001), while the smartphone and McMaster counts did not have statistically different accuracies. Overall, the smartphone system compared favorably to manual methods with regards to precision, and reasonably with regards to accuracy. With further refinement, this system could become useful in veterinary practice.