Deep learning-based analysis of insect life stages using a repurposed dataset

Fatin Faiaz Ahsan; Melissa L. Thomas; Hamid Laga; Ferdous Sohel

doi:10.1016/j.ecoinf.2025.103202

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Deep learning-based analysis of insect life stages using a repurposed dataset

Journal article

Open access

Peer reviewed

Deep learning-based analysis of insect life stages using a repurposed dataset

Fatin Faiaz Ahsan, Melissa L. Thomas, Hamid Laga and Ferdous Sohel

Ecological informatics, Vol.90, 103202

2025

DOI: https://doi.org/10.1016/j.ecoinf.2025.103202

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Abstract

Agricultural pest

Insect pest classification

Life-stage classification

Insect pests pose a significant risk to agriculture and biosecurity, reducing crop yields and requiring effective management. Accurate identification of early life stages is often required for effective management but is generally reliant on expert evaluation, which is both costly and time-consuming. To address this, we use a deep learning-based approach for insect species and life-stage classification from digital images. We repurposed the IP102 dataset by adding detailed annotations for four life stages — egg, larva, pupa, and adult — alongside the original species categories. Two deep learning models, based on ResNet50 and EfficientNetV2M, were tested for classification accuracy in this dual-layered identification task. Although both models accomplished the task well, the EfficientNetV2M model performed slightly better than the ResNet50, achieving 72.4% precision, 72.1% recall, and an F1-score of 72.0%. Our results demonstrate the potential of deep learning for automated insect species and life-stage classification, providing a high throughput and efficient solution towards agricultural monitoring and pest management.

Details

Title: Deep learning-based analysis of insect life stages using a repurposed dataset
Authors/Creators: Fatin Faiaz Ahsan - School of Information Technology, Murdoch University, Murdoch, 6150, WA, Australia
Melissa L. Thomas - Murdoch University
Hamid Laga - Murdoch University
Ferdous Sohel
Publication Details: Ecological informatics, Vol.90, 103202
Publisher: Elsevier B.V.; AMSTERDAM
Number of pages: 12
Identifiers: 991005783424807891
Murdoch Affiliation: Centre for Biosecurity and One Health; Centre for Crop and Food Innovation; School of Information Technology; Centre for Healthy Ageing
Language: English
Resource Type: Journal article

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Citation topics: 7 Engineering & Materials Science; 7.226 Electrical - Sensors & Monitoring; 7.226.2419 Ultrasonic Flow Measurement
Web Of Science research areas: Ecology
ESI research areas: Environment/Ecology