Opportunities and Challenges in Combining Optical Sensing and Epidemiological Modeling

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.