Advances in Deep Learning–Based Intelligent Sensing for Non-Destructive Monitoring of Crop Nutrition and Quality

Crop nutrition and quality formation are complex processes influenced by genotype, environment, and management practices. Remote sensing and proximal sensing technologies have long been used to monitor crop growth and physiological status; however, their effectiveness has been constrained by limited feature representation and simplified modeling assumptions. Recent breakthroughs in deep learning have significantly expanded the potential of intelligent sensing by enabling automated feature extraction, nonlinear modeling, and high-dimensional data fusion. These advances allow researchers to capture subtle spectral, spatial, and temporal patterns associated with crop nutrient dynamics and quality variation. As sensing platforms and data availability continue to grow, there is an urgent need to systematically summarize and advance deep learning–driven approaches for nondestructive crop monitoring.

The primary goal of this Research Topic is to advance the understanding and application of deep learning–based intelligent sensing technologies for non-destructive monitoring of crop nutrition and quality. Accurate, timely, and scalable assessment of crop nutritional status and quality traits is essential for optimizing fertilization strategies, improving product quality, and promoting sustainable agricultural management. However, traditional monitoring methods are often labor-intensive, destructive, or limited in spatial and temporal resolution. By bringing together recent methodological advances and practical case studies, this Research Topic seeks to highlight how deep learning can effectively integrate multi-source sensing data to enhance prediction accuracy, robustness, and automation. The Topic aims to bridge the gap between algorithm development and real-world agricultural applications, fostering interdisciplinary collaboration and accelerating the adoption of intelligent sensing technologies in precision agriculture and smart farming systems.

This Research Topic focuses on deep learning–based intelligent sensing methods for non-destructive monitoring of crop nutrition and quality across multiple scales. Relevant themes include, but are not limited to: intelligent feature extraction from spectral and imaging data; deep learning models for estimating crop nitrogen, chlorophyll, water, and quality-related biochemical traits; multi-source data fusion involving spectral, meteorological, soil, and environmental information; time-series analysis and growth dynamics prediction; model interpretability, lightweight design, and cross-regional transferability. We welcome original research articles, methodological studies, review papers, and application-oriented case studies that contribute novel insights, algorithms, or practical solutions for smart and precision agriculture.

Article types and fees

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

• Brief Research Report
• Data Report
• 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:

• Brief Research Report
• Data Report
• 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: deep learning, intelligent sensing, crop nutrition, crop quality, non-destructive monitoring, precision agriculture, remote sensing

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.