Remote Sensing for Plant Diseases and Pests

Dear Colleagues,

The impact of plant diseases and pests on crop production, food security, and sustainability of natural ecosystems has increased in recent years due to the globalization of trade and travel, climate change, and the emergence of novel pathogen strains able to overcome innate host resistance. The urgent threat disease and pests pose to modern agriculture demands tools for better detection and monitoring to achieve effective control. The nascent discipline of remote sensing for plant protection has demonstrated great promise for achieving this goal due to its inherent scalability and capacity for passive monitoring, well beyond what is possible from field surveys and laboratory analyses alone. However, remote sensing for plant disease and pests remains an underdeveloped discipline despite its revolutionary potential, with research challenges facing fundamental study and field application.

The advent of a high dynamism in the innovation of sensors (e.g., hyperspectral, and thermography), platforms (e.g., rovers, UAVs, aircraft, and satellites), artificial intelligence, and data interpretation has reinvigorated fundamental study. Despite these promising advances, reliable and practical pre-symptomatic disease detection and differentiation remains elusive since the underlying pathosystem processes that result in distinguishable spectral features across types and stages of the disease are not yet fully understood. Choosing the appropriate sensor, platform, and spatial and temporal resolution is critical due to the uniqueness of each pathosystem, which differs from others in their interactions and symptomatology. Deciding how to fuse Radiative Transfer Models (RTMs) with chlorophyll fluorescence quantification, as well as, thermal, radar and LIDAR observations to enhance disease and pest detection and differentiation, especially when coupled with advances in deep- and machine-learning adds to the challenge. Linking physiological processes associated with a pathosystem to machine learning features is essential to facilitate the transferability of predictive models to other pathosystems and scenarios with both abiotic and biotic confounding factors. An integrated approach will advance multiple subdisciplines, including the quantification of organic and synthetic chemical bonds of pesticides on the plant surface and its interior for more prescriptive application recommendations.

Field application research fed by fundamental research will lead to monumental advances in cost-effective solutions for operational remote sensing for crop protection at a regional scale by refining our understanding of individual pathosystem spatial, spectral, and temporal resolution requirements for early intervention. How the improved disease and pest distribution remote sensing provides, filling gaps in space and time between scouting campaigns, benefits epidemiological models is another task to face to untangle uncertainties about key aspects of the epidemiology of emerging diseases and pests, as well as guide plant health management decisions in the context of policy regulation. At the global level, remote sensing for plant disease and pest detection needs to advance the foundation for global crop disease surveillance systems that assess regions most at risk for impacts on food security and biodiversity conservation, and warn if novel/emerging risks are identified in the context of climate change and pathogen spread.

This Special Issue addresses recent advances in remote sensing research via fundamental study and translational application in crop protection across scales. Topics are aimed at assessing risks from a biosecurity and endemic pest perspective, from on-farm to global policy-making, as well as in an ever-changing climate, and guiding resource-efficient plant health management responses that ensure the future sustainability of our planet by preserving biodiversity and natural resources.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Detection of crop diseases and insect pests from proximal to large scales.
• Monitoring of infection and severity of crop diseases and insect pests.
• Discrimination of biotic and abiotic stress with remote sensing.
• Deep- and machine-learning in remote sensing for plant disease and pests: new methods for image analysis, linking physiological processes with algorithm features, approaches for plant disease and pest identification.
• Multi-sensor and/or multi-platform approaches for operational remote sensing monitoring.
• Integrative remote sensing approaches to inform epidemiology of plant diseases and pests (e.g., epidemiological models, earth system models)

We look forward to receiving your contributions.

Dr. Rocío Calderón Madrid
Dr. Carlos Camino
Dr. Tomás Poblete
Guest Editors

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• remote sensing
• precision crop protection
• plant disease and pest surveillance
• artificial intelligence
• imaging spectroscopy
• hyperspectral
• thermal
• UAVs
• satellites