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Opportunities and Challenges in Combining Optical Sensing and Epidemiological Modeling.

Alexey Mikaberidze1, C D Cruz2, Ayalsew Zerihun3

  • 1School of Agriculture, Policy and Development, University of Reading, Reading, U.K.

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|May 28, 2025
PubMed
Summary
This summary is machine-generated.

Integrating optical sensing with plant disease epidemiological models improves crop yield prediction. This synergy enhances disease management by leveraging detailed data from technologies like hyperspectral imaging and light detection and ranging.

Keywords:
artificial intelligencedata capture standardsdata fusiondisease identifiabilitydisease surveillanceerror propagationmachine learningmodel parametrizationremote sensingspectral signature

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Area of Science:

  • Agricultural science and remote sensing
  • Plant pathology and epidemiology
  • Data science and ecological modeling

Background:

  • Plant diseases severely impact crop yield and quality, necessitating effective management strategies.
  • Traditional disease assessment methods are labor-intensive, limiting data acquisition for epidemiological models.
  • Optical sensing technologies offer scalable data collection for plant health monitoring.

Purpose of the Study:

  • To review and bridge the gap between optical sensing and epidemiological modeling for plant disease management.
  • To explore opportunities and challenges in integrating these two fields.
  • To propose recommendations for advancing cross-disciplinary research and practice.

Main Methods:

  • Comprehensive literature review of optical sensing technologies (multispectral, hyperspectral, thermal imaging, LiDAR) and epidemiological modeling.
  • Analysis of synergistic potential and challenges in combining data acquisition and modeling approaches.
  • Development of a common framework and language for researchers in both fields.

Main Results:

  • Optical sensing can enhance epidemiological models through improved parameterization and host plant mapping.
  • Epidemiological modeling can refine optical sensing by boosting measurement accuracy and optimizing deployment.
  • Key challenges include disease identification, data quality/resolution, and linking the two methodologies.

Conclusions:

  • Integrating optical sensing and epidemiological modeling offers significant potential for accurate plant disease prediction and management.
  • Standardizing optical sensing protocols and creating open-access databases are crucial for fostering collaboration.
  • Further research is needed to address challenges related to data integration and emerging diseases.