ArticleInsects2025
Multi-Strategy Improved Cantaloupe Pest Detection Algorithm.
Article in Insects, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Research on lung nodule detection in X-ray plain films based on improved YOLOv12 model.Scientific reports · 2026Article
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Authors and funding
4 authors.
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Abstract
This study aimed at the problems of dense distribution and different sizes of pests in cantaloupe fields, as well as the complex and variable characteristics of some pests. To improve the detection accuracy of pests in cantaloupe leaves, a multi-strategy dynamic feature fusion detection algorithm is proposed, which provides a reference for technological paths for pest control in cantaloupe fields. First, the Melon Cantaloupe Pest dataset, containing five types of cantaloupe pests from the Robotflow website, was used and processed for image culling and expansion. Second, the YOLOv12 model is improved by (1) adding the EMA attention mechanism at the end of its backbone network, (2) changing the fusion strategy of the Concat layer in the Neck network as well as the Detect layer in the Head network, (3) introducing the WIoU v3 loss function, and (4) improving the C3k2 module. The experiments showed that the model proposed in this study achieved 85.06%, 86.76%, 79.94%, 54.15% and 83.08% for mAP50, P, R, mAP50-95, and F
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.