TrichANet: An Attentive Network for Trichogramma Classification

Agniv Chatterjee, Snehashis Majhi, Vincent Calcagno, François Brémond

2023

Abstract

Trichogramma wasp classification has a significant application in agricultural research, thanks to their massive usage and production in cropping as a bio-control agent. However, classifying these tiny species is a challenging task due to two factors: (i) Detection of these tiny wasps (barely visible with the naked eyes), (ii) Less inter-species discriminative visual features. To combat this, we propose a robust method to detect and classify the wasps from high-resolution images. The proposed method is enabled by a trich detection module that can be plugged into any competitive object detector for improved wasp detection. Further, we propose a multi-scale attention block to encode the inter-species discriminative representation by exploiting the coarse and fine-level morphological structure of the wasps for enhanced wasps classification. The proposed method along with its two key modules is validated in an in-house Trich dataset and a classification performance gain of 4% compared to recently reported baseline approaches outlines the robustness of our method. The code is available at https://github.com/ac5113/TrichANet.

Paper Citation

in Harvard Style

Chatterjee A., Majhi S., Calcagno V. and Brémond F. (2023). TrichANet: An Attentive Network for Trichogramma Classification. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 864-872. DOI: 10.5220/0011677700003417

in Bibtex Style

@conference{visapp23,
author={Agniv Chatterjee and Snehashis Majhi and Vincent Calcagno and François Brémond},
title={TrichANet: An Attentive Network for Trichogramma Classification},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={864-872},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011677700003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - TrichANet: An Attentive Network for Trichogramma Classification
SN - 978-989-758-634-7
AU - Chatterjee A.
AU - Majhi S.
AU - Calcagno V.
AU - Brémond F.
PY - 2023
SP - 864
EP - 872
DO - 10.5220/0011677700003417
PB - SciTePress