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A Precise and Autonomous System for the Detection of Insect Emergence Patterns
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AI-based UAV pest and disease detection: Time for a reset?

Eline Eeckhout1, Pieter Spanoghe2, Wouter H Maes3

  • 1Laboratory for Crop Protection Chemistry, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Gent 9000, Belgium; UAV Research Centre, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Gent 9000, Belgium.

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Most studies using drones and AI for crop pest detection lack robust testing, overestimating model performance. Improved validation is crucial for real-world agricultural applications.

Keywords:
deep learningdronemachine learningprecision agricultureremote sensing

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Remote Sensing

Background:

  • Uncrewed aerial vehicles (UAVs) combined with artificial intelligence (AI), machine learning (ML), and deep learning (DL) are increasingly used for crop pest and disease detection.
  • The practical robustness and generalizability of these AI-driven remote sensing models in real-world agricultural settings are not well-established.

Purpose of the Study:

  • To conduct a meta-analysis of UAV-based pest and disease detection studies.
  • To evaluate dataset construction and model validation methodologies in existing research.
  • To identify limitations and provide recommendations for improving model robustness and applicability.

Main Methods:

  • Meta-analysis of 121 UAV-based studies published between 2018 and 2024.
  • Examination of dataset construction, including the use of independent test datasets.
  • Assessment of model validation practices and field-level transferability.

Main Results:

  • 89% of studies lacked truly independent test datasets, leading to inflated performance metrics.
  • Only 11% of studies evaluated models on independent fields, with limited successful transferability.
  • Significant methodological limitations were identified in current validation practices.

Conclusions:

  • Current validation practices for UAV-based AI models in agriculture are insufficient.
  • Overestimated performance and poor generalizability hinder real-world adoption.
  • Recommendations are provided to enhance robustness, reproducibility, and practical relevance for improved field applicability.