Transformative AI-Driven Platforms: Revolutionizing Crop Phenotyping With UAV, Proximal, And Ground Technologies

Recent advancements in agricultural technology have invigorated the field of crop phenotyping, particularly through the integration of AI-driven optical sensing methods. These innovations leverage state-of-the-art cameras and spectrometers to acquire spectral fingerprints that reveal detailed biochemical and structural crop traits. Such data, previously obtainable only through destructive sampling or labor-intensive wet-lab processes, can now be swiftly interpreted using AI advances like transformer networks, physics-guided learning, and edge inference. These developments herald real-time insights into crop health indicators such as disease onset, nutrient status, and drought stress. However, despite these promising technologies, there remains a fragmentation in research due to challenges like radiometric drift and illumination variability.

This Research Topic aims to consolidate current advancements and cultivate an integrated understanding of AI-enhanced optical sensing technologies across various deployment scales. It seeks to address current fragmented efforts by encouraging multi-seasonal, multi-site, and cross-scale studies that emphasize robust processes, domain adaptation, and uncertainty quantification. The initiative aspires to translate these optical AI tools into actionable agronomic applications that would benefit large-scale production as well as research endeavors. Crucially, the exploration will extend beyond technology, focusing also on creating synergies among diverse methodologies to amplify the agronomic impact of these innovations.

To gather further insights in the field of AI-driven crop phenotyping, we welcome articles addressing, but not limited to, the following themes:

• UAV systems for high-resolution stress and trait mapping

• Proximal sensors providing detailed phenotypic insights

• Ground-based platforms combining precision and throughput

• Comprehensive radiometric and geometric correction techniques

• Innovative transfer-learning and domain-adaptation strategies

• Synergistic workflows integrating multi-modal sensing data

• Advanced time-series analytics and edge-AI for real-time management

• Physiological assessments linking spectral data to plant health

• Protocols for reproducibility, open-source innovations, and dataset sharing

• Evaluations of economic and environmental repercussions of adoption

Contributions may be submitted as Original Research, Methods, Technology & Code, Data Reports, or Review/Perspective articles.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Digital Agriculture, Optical Sensing, UAV, Proximal Phenotyping, Hyperspectral Imaging, Machine Learning, Crop Stress Detection, Precision Agriculture

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.