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September 5, 2025TechnologiesOpen Access

Improved YOLOv8 Segmentation Model for the Detection of Moko and Black Sigatoka Diseases in Banana Crops with UAV Imagery

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Authors

BOByron OviedoUniversidad Técnica Estatal de QuevedoCZCristian Zambrano‐VegaUniversidad Técnica Estatal de QuevedoRVRonald Oswaldo Villamar-TorresUniversidad Técnica Estatal de Quevedo

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Overview

Deep learning-based segmentation improves detection of moko and black sigatoka diseases, suggesting better monitoring strategies for banana crops.

Key Points

  • The optimized YOLOv8 model achieved a mean precision of 79.6%, significantly enhancing disease detection in banana crops.
  • Recall of 80.3% and mAP@0.5 of 84.9% highlight the model's effectiveness against traditional methods for detecting moko disease.
  • Multiple YOLOv8 configurations were tested to determine the best setup for accurately segmenting black sigatoka in UAV imagery.
  • The study's findings support more efficient and objective disease management strategies in agriculture, benefiting local economies.

Cite This Study

Oviedo et al. (2025) studied this question.

synapsesocial.com/papers/68bb3d622b87ece8dc956781https://doi.org/10.3390/technologies13090382
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Also Consider

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