Summary
This comprehensive review addresses the underexploited potential of integrating optical sensing with epidemiological modelling for plant disease management. The authors establish a common framework for both research communities and delineate how optical sensing can improve model inputs and accuracy whilst epidemiological models can enhance sensor interpretation and deployment strategy. Key recommendations include standardising protocols and developing open-access databases combining optical sensing data with epidemiological models.
UK applicability
These methodologies are directly applicable to UK crop production and disease management systems, particularly for cereal and horticultural crops affected by economically important diseases. The emphasis on standardised protocols and open-access data aligns with UK agricultural research infrastructure and could support evidence-based crop protection strategies under evolving climatic conditions.
Key measures
Optical sensing capabilities across spatial and temporal scales; epidemiological model parameterisation and validation; disease detection accuracy; sensor deployment optimisation
Outcomes reported
The paper reviews the state of the art in combining optical sensing technologies (multispectral, hyperspectral, thermal imaging, LiDAR) with epidemiological models to improve plant disease detection and management. It identifies opportunities for mutual enhancement between the two fields and outlines outstanding challenges in disease identification, data quality, integration, and emerging pathogen detection.
Topic tags
Dig deeper with Pulse AI.
Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.