Species Identification of Caterpillar Eggs by Machine Learning Using a Convolutional Neural Network and Massively Parallelized Microscope

John Efromson, Roger Lawrie, Thomas Jedidiah Jenks Doman, Matthew Bertone, Aurélien Bègue, Mark Harfouche, Dominic Reisig, R. Michael Roe

Rapid, accurate insect identification is the first and most critical step of pest management and vital to agriculture for determining optimal management strategies. In many instances, classification is necessary within a short developmental window. Two examples, the tobacco budworm, Chloridea virescens, and bollworm, Helicoverpa zea, both have <5 days from oviposition until hatching. H. zea has evolved resistance to Bt-transgenic crops and requires farmers to decide about insecticide application during the ovipositional window. The eggs of these species are small, approximately 0.5 mm in diameter, and often require a trained biologist and microscope to resolve morphological differences between species. In this work, we designed, built, and validated a machine learning approach to insect egg identification with >99% accuracy using a convolutional neural architecture to classify the two species of caterpillars. A gigapixel scale parallelized microscope, referred to as the Multi-Camera Array Microscope (MCAM™), and automated image-processing pipeline allowed us to rapidly build a dataset of ~5500 images for training and testing the network. In the future, applications could be developed enabling farmers to photograph eggs on a leaf and receive an immediate species identification before the eggs hatch.