Non-Destructive Nutrient Diagnostics and Precision Fertilization for Climate-Smart Agriculture

Background
Plant nutrition management lies at the heart of climate-smart food systems, bridging productivity, resource efficiency, and environmental protection. Traditional soil and tissue testing methods remain destructive, time-consuming and insufficient for adaptive, within-season decision-making. As agricultural systems are increasingly challenged to sustain yields while mitigating greenhouse gas emissions, nutrient losses and environmental degradation, rapid, accurate and non-destructive approaches have become essential. These technologies enable producers to optimize fertiliser use, minimize input waste and enhance the resilience and sustainability of agroecosystems under changing climatic conditions.

Recent technological advances have significantly accelerated this field. Sensors employing hyperspectral and multispectral imaging, Raman spectroscopy, fluorescence, LiDAR, canopy reflectance sensing and optical vegetation indices have enabled non-invasive nutrient diagnostics across multiple spatial scales from individual leaves to entire landscapes. The integration of UAV- and satellite-based remote sensing with artificial intelligence (AI)-driven analytics now supports high-frequency monitoring and spatially explicit assessment of crop nutrient status. Machine learning and deep learning models can transform spectral and environmental data into actionable decision-support tools for precision and variable-rate fertilisation. Nevertheless, important research gaps remain, including context-specific sensor calibration, standardisation across crops and environments, integration of multi-source datasets into robust decision-support systems, affordability and scalability for smallholder farming systems and validation of these technologies in improving nutrient use efficiency and environmental sustainability.

Goal
This Research Topic aims to consolidate interdisciplinary advances in non-destructive approaches to plant nutrient assessment and their integration into precision fertilisation strategies supporting climate-smart agriculture. It seeks to highlight robust methodologies, transferable algorithms, and cross-system insights that enhance nutrient use efficiency, crop productivity and environmental sustainability. Specifically, it encourages studies that:

Reveal the physiological and biophysical mechanisms underpinning sensor-based nutrient detection.
Identify and address limitations in calibration, validation, and standardisation across crops, soils, climates, and management systems.
Demonstrate effective feedback mechanisms linking sensor-based diagnostics with real-time fertiliser recommendations and variable-rate nutrient management.
Explore the integration of AI, remote sensing, vegetation indices, and IoT frameworks for precision nutrient management.
Assess socio-economic, policy, and infrastructure requirements for the equitable adoption of smart nutrient management technologies.
Scope
The Topic focuses on the intersection of agricultural sensing technologies, artificial intelligence, remote sensing, and sustainable nutrient management within a climate-smart agricultural framework. It welcomes laboratory, field, modelling and review studies across diverse cropping systems and agroecological conditions.

To advance understanding of non-destructive nutrient monitoring and precision fertilisation, we welcome articles addressing, but not limited to, the following themes:

Development and validation of sensors for non-destructive nutrient assessment (hyperspectral, multispectral, Raman, LiDAR, fluorescence, SPAD, canopy reflectance sensors).
Application of vegetation indices (NDVI, NDRE, GNDVI, RVI, SAVI, etc.) for crop nutrient assessment and fertiliser management.
UAV- and satellite-based approaches for large-scale nutrient monitoring and spatial mapping.
AI, machine learning and deep learning models for nutrient prediction, optimisation and decision support.
Sensor-driven precision agriculture and variable-rate fertiliser application strategies.
IoT and data integration frameworks for real-time nutrient monitoring and management.
Decision-support systems for precision nutrient recommendation and climate-smart fertilisation.
Assessment of nutrient use efficiency and environmental co-benefits, including emission reduction and soil health improvement.
Comparative studies across crops, regions, sensing platforms and management practices.
Socio-economic, adoption and policy challenges associated with smart nutrient management technologies.

Article types and fees

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

• Community Case Study
• Conceptual Analysis
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... 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:

• Community Case Study
• Conceptual Analysis
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Non-destructive sensing, Plant nutrient assessment, Precision nutrient management, Remote sensing, Hyperspectral imaging, Multispectral imaging, AI and machine learning, UAV-based monitoring, Nitrogen use efficiency, Variable-rate fertilisation

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.