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August 13, 2026Machine Vision and ApplicationsOpen Access

Automated counting of Spodoptera frugiperda eggs using deep learning in multilayer egg mass images

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Authors

MRMurilo Semolini RovedaRPRodrigo Palucci Pantoni

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Overview

Randomized trial demonstrates efficient egg counting in Spodoptera frugiperda populations, supporting pest management efforts.

Key Points

  • This study aims to develop an automated system for detecting and counting Spodoptera frugiperda egg masses using deep learning techniques.
  • Utilized the YOLOv11 architecture for detection and counting of egg masses.
  • Trained six deep learning models under different configurations with synthetic data augmentation.
  • Evaluated model performance using standard object detection metrics including Precision, Recall, and mAP.
  • Achieved a Precision of 88.90% and Recall of 92.03% with optimal model configuration.
  • Best model delivered a Mean Absolute Percentage Error (MAPE) of only 4.08% across the test dataset.
  • Incorporating synthetic samples significantly improved detection performance, particularly in crowded images.

Cite This Study

Roveda et al. (2026) studied this question.

synapsesocial.com/papers/6a7d76aa2b0e0cff3f640248https://doi.org/10.1007/s00138-026-01882-1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Study on the Image Recognition of Field-Trapped Adult Spodoptera frugiperda Using Sex Pheromone Lures2025
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  4. 4Deep learning-based detection and counting of wheat seeds: Comparative benchmarking of YOLO models2026
  5. 5Lightweight YOLOv8 for Non-Destructive Detection of Fertilized Eggs2026