PLDNet: real-time Plectropomus leopardus disease recognition Article Swipe
Mengran Liu
,
Runchen Xue
,
Cun Wei
,
Jingjie Hu
,
Zhenmin Bao
,
Gang Xu
,
Junwei Zhou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmars.2025.1507104
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmars.2025.1507104
In Plectropomus leopardus , Vibrio disease and Hirudo parasitic disease are relatively common. Timely recognition of these diseases can improve the survival rate of Plectropomus leopardus and prevent their spread. However, early-stage diseases are difficult to distinguish due to their small size and subtle characteristics. Traditional manual recognition methods rely on personal experience and subjective judgment, leading to time-consuming and error-prone diagnoses. To address the challenges in detecting and classifying Plectropomus leopardus diseases, this paper proposes PLDNet (Plectropomus Leopardus Disease Detection Network), a real-time detection and recognition method that provides faster and more accurate diagnoses for fish farms. PLDNet incorporates two significant advancements: First, it employs FocalModulation, which enhances the model’s ability to identify key disease characteristics in images. Second, it introduces the MPDIoU (Minimum Point Distance-based Intersection over Union) for bounding box similarity comparison, optimizing the loss function and improving recognition accuracy. This paper also presents the PLDD (Plectropomus Leopardus Disease Dataset), a newly developed dataset that includes comprehensive images of healthy and diseased specimens. PLDD addresses the scarcity of data for this species and serves as a valuable resource for advancing research in marine fish health. Empirical validation of PLDNet was conducted using the PLDD dataset and benchmarked against leading models, including YOLOv8-n, YOLOv9-m, and YOLOv9-c. The results show that PLDNet achieves superior detection performance, with an average detection accuracy of 84.5%, a recall rate of 86.6%, an [email protected] of 88.1%, and a real-time inference speed of 45 FPS. These metrics demonstrate that PLDNet significantly outperforms other models in both accuracy and efficiency, providing practical solutions for real-time fish disease management.
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Metadata
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- https://doi.org/10.3389/fmars.2025.1507104
- OA Status
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- 30
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- 10
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407613449Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmars.2025.1507104Digital Object Identifier
- Title
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PLDNet: real-time Plectropomus leopardus disease recognitionWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-02-17Full publication date if available
- Authors
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Mengran Liu, Runchen Xue, Cun Wei, Jingjie Hu, Zhenmin Bao, Gang Xu, Junwei ZhouList of authors in order
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https://doi.org/10.3389/fmars.2025.1507104Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.3389/fmars.2025.1507104Direct OA link when available
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Leopardus, Business, Fishery, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.practical | 256 |
| abstract_inverted_index.providing | 255 |
| abstract_inverted_index.real-time | 83, 235, 259 |
| abstract_inverted_index.solutions | 257 |
| abstract_inverted_index.challenges | 65 |
| abstract_inverted_index.diagnoses. | 61 |
| abstract_inverted_index.experience | 52 |
| abstract_inverted_index.introduces | 121 |
| abstract_inverted_index.optimizing | 135 |
| abstract_inverted_index.relatively | 11 |
| abstract_inverted_index.similarity | 133 |
| abstract_inverted_index.specimens. | 165 |
| abstract_inverted_index.subjective | 54 |
| abstract_inverted_index.validation | 189 |
| abstract_inverted_index.Traditional | 45 |
| abstract_inverted_index.benchmarked | 199 |
| abstract_inverted_index.classifying | 69 |
| abstract_inverted_index.comparison, | 134 |
| abstract_inverted_index.demonstrate | 243 |
| abstract_inverted_index.distinguish | 36 |
| abstract_inverted_index.early-stage | 31 |
| abstract_inverted_index.efficiency, | 254 |
| abstract_inverted_index.error-prone | 60 |
| abstract_inverted_index.management. | 262 |
| abstract_inverted_index.outperforms | 247 |
| abstract_inverted_index.recognition | 14, 47, 86, 141 |
| abstract_inverted_index.significant | 101 |
| abstract_inverted_index.Intersection | 127 |
| abstract_inverted_index.Plectropomus | 1, 24, 70 |
| abstract_inverted_index.incorporates | 99 |
| abstract_inverted_index.performance, | 216 |
| abstract_inverted_index.(Plectropomus | 77, 149 |
| abstract_inverted_index.advancements: | 102 |
| abstract_inverted_index.comprehensive | 159 |
| abstract_inverted_index.significantly | 246 |
| abstract_inverted_index.Distance-based | 126 |
| abstract_inverted_index.time-consuming | 58 |
| abstract_inverted_index.characteristics | 116 |
| abstract_inverted_index.FocalModulation, | 106 |
| abstract_inverted_index.characteristics. | 44 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.03280715 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |