Advanced Imaging and Phenotyping for Sustainable Plant Science and Precision Agriculture 4.0

Advancements in plant imaging and phenotyping are vital in the ongoing quest for sustainable plant science. As global food systems face increasing pressure and the need for climate-resilient crops becomes critical, integrating next-generation imaging techniques with artificial intelligence (AI) emerges as a promising route for non-invasive, high-throughput plant analysis. Recent developments, such as high-resolution multispectral, hyperspectral, and 3D imaging, have dramatically increased our ability to capture detailed plant traits. Coupled with AI-driven object detection models, these technologies enable rapid identification of phenotypic features like leaf structure, growth patterns, and stress responses. Despite these innovations, gaps remain in harnessing the full potential of these approaches to meet the complex and evolving demands of precision agriculture.

The goal of this Research Topic is to explore interdisciplinary approaches that bridge plant biology with computational advancements, aiming to deliver more precise, scalable, and sustainable solutions for precision agriculture 4.0. This involves examining how innovative paradigms like Generative AI and transfer learning can significantly improve plant phenotyping capabilities. The research seeks to address essential questions about the application of these technologies in improving crop productivity, adaptive capacity, and resource efficiency.

This Research Topic encompasses a broad scope, welcoming insights into advanced imaging and phenotyping methods that empower sustainable plant science and support food security and climate-resilient crop development. Themes of interest include, but are not limited to:

• Next-generation imaging technologies for plant analysis

• High-throughput phenotyping systems in precision agriculture

• Deep learning and computer vision for plant trait analysis

• Transfer learning and domain adaptation in phenotyping

• Generative AI for synthetic data generation and virtual phenotyping

• Data fusion and multi-modal integration for comprehensive plant modeling

Authors are invited to submit articles that explore these and other relevant areas, contributing to the broader discussion on sustainable agriculture innovations.

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: Plant Phenotyping, Imaging Technologies, Precision Agriculture, Artificial Intelligence, Climate-resilient Crops

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.