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August 20, 2025TechnologiesOpen Access

FCNet: A Transformer-Based Context-Aware Segmentation Framework for Detecting Camouflaged Fruits in Orchard Environments

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Authors

IEIvan Roy S. EvangelistaSultan Kudarat State UniversityABArgel A. BandalaPhilippine Rice Research InstituteEDElmer P. DadiosDe La Salle University

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Implication

A novel framework improves fruit segmentation accuracy in orchards, indicating significant advancements over traditional methods.

Key Points

  • Our model achieved superior detection performance for camouflaged fruits, enhancing segmentation accuracy.
  • Notable performance gains of 2.43% to 6.85% were observed in various metrics including S-measure and IoU.
  • The study utilized a context-aware segmentation framework incorporating PVTv2 architecture and Attention techniques.
  • The results may inform agricultural practices, supporting better yield estimation and disease prevention.

Cite This Study

Evangelista et al. (2025) studied this question.

synapsesocial.com/papers/68af4cdfad7bf08b1ead677ehttps://doi.org/10.3390/technologies13080372
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