Non-Destructive Phenotyping from Seeds to Plants: Advancements in Sensing Technologies, Algorithms, and Applications

Recent years have brought remarkable advances in plant phenotyping, with non-destructive and high-throughput technologies revolutionizing how researchers assess traits from seeds and mature plants. Tools such as hyperspectral imaging, X-ray computed tomography (CT), nuclear magnetic resonance (NMR), and fluorescence sensing now allow for in-depth characterization of viability, vigor, and physiological status without damaging samples. These innovations help accelerate breeding programs, precision agriculture, and phenomics research by enabling repeated, scalable measurements throughout a plant’s life cycle. The integration of such sensing technologies with advanced data analytics and automation is paving the way for more precise, efficient, and standardized trait evaluation, offering enormous potential for crop improvement and stress resilience studies.

Despite impressive progress, major challenges remain in the development and deployment of non-destructive phenotyping. Diverse plant architectures, large data volumes, and the need to connect early seed traits with field performance call for innovative sensor designs and robust computational algorithms. Many current methods face limitations such as insufficient throughput, lack of dynamic mapping across developmental stages, or difficulties in transferring lab-based advances to field environments. The main goal of this Research Topic is to showcase state-of-the-art technological solutions and interdisciplinary collaborations that address these bottlenecks. We invite researchers to present novel sensors, imaging modalities, and analytical pipelines for actionable, non-invasive phenotyping. Another central aim is to foster a dialogue between technology developers, plant biologists, and breeders to facilitate the field-to-lab integration of these tools, ultimately supporting the broader adoption of precision agriculture practices and the acceleration of crop improvement cycles.

This Research Topic covers, but is not limited to:

• Development and validation of novel non-destructive sensors for seed and plant traits

• Hyperspectral, X-ray CT, NMR, and fluorescence imaging applications in phenomics

• AI, machine learning, and advanced analytics for high-throughput trait extraction

• Dynamic mapping of development: Linking seed traits to plant growth and stress responses

• Integrating non-destructive approaches from lab to field environments

• Case studies of non-destructive phenotyping in breeding and precision agriculture

• Sensor fusion and multi-modal phenotyping strategies

• Standardization, data sharing, and open platforms for phenomics innovation

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
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• ... 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
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Non-Destructive Phenotyping, Plant Sensing Technologies, High-Throughput Phenotyping, Precision Agriculture, Seed-to-Plant Trait Mapping, Phenomics, UAV, deep learning

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.