- Oceanic plankton have both a large impact on our oceans health and atmo- spheric balance of Co2. The overall health of planktonic life is determined by many physical, chemical, and ecological factors, that drive taxonomic abundances and the relative amount of non-living biomass called detritus. Recent advances in microscopic imaging allow censusing of thousands of particles, plankton or detritus, at high spatial or temporal resolution, which necessitates machine learning models to rapidly clas- sify images into appropriate categories. Convolutional Neural Networks fulfill these criteria for aiding in the predictions of oceanic health and diversity. However, little has been researched to improve these models performance by processing input before model training. This study focuses on image pre-processing techniques for optimiz- ing the accuracy of classifying detritus and non-detritus. Findings suggest that less processed input trained on by the model results in less overfitting, but little to no cor- relation with higher accuracy or higher F1-score. Similarly, the more dense the input trained on by the model, the faster the model overfits.