Figure 4.

Species classification across 39 species shows the strength of CNNs for generalized mosquito classification, and elucidates a guideline for the number of specimens required for confident classification. Classification achieved unweighted accuracy of 93.06 ± 0.50% and a macro F1-score of 85.07 ± 1.81%, trained, validated, and tested over an average of 9080, 1945, and 1945 samples over five folds. (A) The majority of the error in this confusion matrix shows confusion between species of the same genera. Some of the confusion outside of genera is more intuitive from an entomologist perspective, such as the 10.2% of Deinocerites cancer samples classified as Culex spp. Other errors are less intuitive, such as the 28.61% of Culiseta incidens samples classified as Aedes atlanticus. (B) This plot of average F1-score of a species against the number of specimens which made up the samples available for training and testing shows the relationship between the available data for a given specimen and classification accuracy. When following the database development methods described in this work, a general guideline of 100 specimens’ worth of data can be extrapolated as a requirement for confident mosquito species classification.