Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardisation of smear inspection that retains capacity for expert confirmation and image archiving. Here we present a machine-learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells and parasite life stage categorisation from unprocessed, heterogeneous smear images. Based on a pre-trained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network (ResNet)-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Lastly, combining our method with a regression model successfully recapitulates intra-erythrocytic developmental cycle with accurate lifecycle stage categorisation. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardising assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab but also future field-based automated malaria diagnosis.