Work

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

Year: 2023

Type: article

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.

Source: Journal of Hymenoptera Research

DOI: 10.3897/jhr.95.95964

Authors: Darren Ward, Brent Martin

Institution: Manaaki Whenua – Landcare Research

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $754

Funders: Ministry of Business, Innovation and Employment, Serono Symposia International Foundation