Trialling a convolution neural network for the identification of Braconidae in New Zealand

By

Ward, Darren F.
Martin, Brent

Type

Article

Date of Publication

2023

Original Publication

Journal of Hymenoptera research

Volume

95

Pages

95--101

Subjects

Braconidae , computer vision , diagnositics , Identification , Model

Contributed by

Pensoft Publishers

Language

English

Published by

Washington, D.C International Society of Hymenopterists

Identifiers

DOI: https://doi.org/10.3897/jhr.95.95964

 

Abstract

Computer vision approaches, such as deep learning, potentially offer a range of benefits to entomology, particularly for the image-based identification of taxa. An experiment was conducted to gauge the ability of a convolution neural network (CNN) to identify genera of Braconidae from images of forewings. A deep learning CNN was trained via transfer learning from a small set of 488 images for 57 genera. Three-fold cross-validation achieved an accuracy of 96.7%, thus demonstrating that identification to genus using forewings is highly predictive. Further work is needed to increase both the coverage to species level and the number of images available.