Fig. 3: Confusion matrices of model prediction accuracies and transfer-learning power.

DL-MIRS was trained using mosquitoes from laboratory larvae reared in the lab (LV, laboratory variation), larvae from the field reared in the lab (GV, genetic variation), and laboratory larvae reared in semi-field (EV, environmental variation). To improve model generalisation from lab to field-reared mosquitoes, we used transfer learning by freezing the convolutional layers of a model trained on LV+GV datasets only and calibrated using a smaller number of EV mosquitoes (here, 1294 examples) to train only the dense layers, resulting in highly accurate identification of (a) mosquito age and (b) mosquito species. c Classification accuracy improved from ~50% to 94% for both age group and species with a training set comprising 0 (i.e. effects of increasing sampling of lab-reared mosquitoes only) through 1452 semi-field (EV) mosquitoes used to re-train the transfer learned model. The solid and shaded lines indicate the mean and standard deviation of the mean of 20 trained models, respectively.