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 and R. Michael Roe ()
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John Efromson: Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA
Roger Lawrie: Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA
Thomas Jedidiah Jenks Doman: Ramona Optics Inc., Durham, NC 27701, USA
Matthew Bertone: Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA
Aurélien Bègue: Ramona Optics Inc., Durham, NC 27701, USA
Mark Harfouche: Ramona Optics Inc., Durham, NC 27701, USA
Dominic Reisig: Department of Entomology and Plant Pathology, North Carolina State University, The Vernon James Center, Plymouth, NC 27962, USA
R. Michael Roe: Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA

Agriculture, 2022, vol. 12, issue 9, 1-11

Abstract: 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.

Keywords: insect identification; Helicoverpa zea; Chloridea virescens; machine learning; microscope photography; Bt resistance; neural network; precision pest control; insect eggs (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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