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Published (Version of Record)CC BY-NC V4.0, Open Access
Journal article
A semi-supervised approach for classifying insect developmental phases from repurposed IP102
Computers and electronics in agriculture, Vol.242, 111337
2026
Abstract
Identifying insect pests, whether as adults, larvae, or eggs, is critical in pest management. Computational learning algorithms have demonstrated strong potential in achieving high identification performance, but these methods typically require large, balanced, and well-annotated datasets. This creates a challenge for insect pest identification, as rare species, despite often being the most damaging to crops, are underrepresented in available datasets. Moreover, annotating large-scale datasets is both costly and labour-intensive. To address this issue, we develop a semi-supervised learning approach, Cost-Focal FixMatch, which extends the widely used FixMatch framework by integrating class-aware reweighting and focal loss to better handle class imbalance. Specifically, we introduce a simple yet robust method for applying class weighting in cross-entropy and focal loss functions. The proposed method generates higher-quality pseudo labels compared to the baseline, ensuring better learning. We evaluate our approach using a repurposed IP102 dataset, which comprises four primary insect life stages, and a mixed IP102 dataset, where the class labels jointly represent insect species and their corresponding life stages. Our method considerably improves the classification of minority classes, achieving a notable increase in recall for the Larva class from 64% under the baseline FixMatch to 82% using MobileNetV3Small backbone. On the Mixed IP102 dataset, our approach achieves almost 9% better improved average recall than the baseline FixMatch built upon the EfficientNetV2S network.
Details
- Title
- A semi-supervised approach for classifying insect developmental phases from repurposed IP102
- Authors/Creators
- Fatin Faiaz Ahsan - Murdoch Univ, Sch Informat Technol, 90 South St, Murdoch, WA 6150, AustraliaMelissa L. Thomas - Murdoch UniversityHamid Laga - Murdoch Univ, Sch Informat Technol, 90 South St, Murdoch, WA 6150, AustraliaFerdous Sohel - Murdoch Univ, Sch Informat Technol, 90 South St, Murdoch, WA 6150, Australia
- Publication Details
- Computers and electronics in agriculture, Vol.242, 111337
- Publisher
- Elsevier
- Number of pages
- 14
- Grant note
- Australian Government Research Training Program (RTP) Scholarship; Australian Government; Department of Industry, Innovation and Science
- Identifiers
- 991005841254907891
- Copyright
- © 2025 The Authors
- Murdoch Affiliation
- Centre for Biosecurity and One Health; School of Information Technology; Centre for Healthy Ageing
- Language
- English
- Resource Type
- Journal article
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