Food security is a worldwide concern that aims to ensure everyone has physical, social, and financial access to sufficient, nutritious food that meets their dietary needs for an active and healthy life. At the core of achieving and maintaining food security is crop compatibility, which determines how well a crop can thrive in specific environmental conditions. As we face the challenges posed by rapidly changing climates, growing populations, and diminishing natural resources, it becomes crucial to understand and optimize crop suitability. Emerging technologies provide significant opportunities to bridge knowledge gaps and develop sustainable agricultural solutions.

Recent advancements in remote sensing technology, utilizing satellites and drones, have transformed the way we monitor agricultural landscapes. These tools collect vast amounts of data on land cover, vegetation health, and soil moisture, allowing for early detection of plant health issues through hyperspectral and multispectral imaging. This early detection facilitates timely interventions and resource optimization. Geographic Information Systems (GIS) complement these efforts by integrating and visualizing this data, creating detailed suitability maps through complex spatial analysis. The combination of machine learning and AI analytics further enhances our understanding of agricultural data, optimizing crop selection and reducing climate impacts. Moreover, real-time, hyper-local data from Internet of Things (IoT) sensors enables precision farming practices, optimizing resource use and yield potential, thereby making agricultural production more sustainable.

This Research Topic aims to investigate how cutting-edge technologies can enhance crop suitability assessments and increase food security, particularly in regions vulnerable to climate change. We aspire to compile state-of-the-art research addressing resource scarcity, land degradation, and climate challenges, while presenting innovative solutions for resilient agricultural practices. To gather further insights in technologically-enhanced crop suitability assessment, we welcome articles addressing, but not limited to, the following themes:

o Advanced geospatial data integration for suitability modeling

o AI and machine learning applications in crop suitability

o Climate change adaptation and resilience through suitability

o Precision agriculture for enhanced food security

o Impact on Livelihoods and Policy Implications stability, and food access, alongside analyses of policy frameworks that can facilitate the adoption of these new technologies for food security.

This collection welcomes a variety of manuscript types, including Original Research Articles that present novel methodologies and significant advancements, Methodology Articles that detail innovative approaches to crop suitability assessments, Review Articles offering comprehensive literature syntheses, and Perspective Articles presenting new insights or examining challenges and opportunities in this field.

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Crop Suitability and Food Security: The Evolution of New Technologies