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Pest infestation is a major cause of crop damage and lost revenues worldwide.Automatic identification of invasive insects would greatly speedup theidentification of pests and expedite their removal. In this paper, we generateensembles of CNNs based on different topologies (ResNet50, GoogleNet,ShuffleNet, MobileNetv2, and DenseNet201) altered by random selection from asimple set of data augmentation methods or optimized with different Adamvariants for pest identification. Two new Adam algorithms for deep networkoptimization based on DGrad are proposed that introduce a scaling factor in thelearning rate. Sets of the five CNNs that vary in either data augmentation orthe type of Adam optimization were trained on both the Deng (SMALL) and thelarge IP102 pest data sets. Ensembles were compared and evaluated using threeperformance indicators. The best performing ensemble, which combined the CNNsusing the different augmentation methods and the two new Adam variants proposedhere, achieved state of the art on both insect data sets: 95.52% on Deng and73.46% on IP102, a score on Deng that competed with human expertclassifications. Additional tests were performed on data sets for medicalimagery classification that further validated the robustness and power of theproposed Adam optimization variants. All MATLAB source code is available athttps://github.com/LorisNanni/.

High performing ensemble of convolutional neural networks for insect pest image detection