Data-driven Approaches to Horticultural Crop Protection under Temperature and Salinity Stress: Methods, Systems, and Applications

Horticultural crops are increasingly challenged by abiotic stresses—particularly temperature extremes (including heat, frost, and freezing) and heightened soil salinity. These factors have emerged as major constraints to sustainable crop production, particularly in the context of ongoing climate change and land degradation. There remains a critical need to develop and implement innovative, data-driven methodologies for crop protection that not only minimize chemical inputs and environmental impact but also enhance crop resilience and productivity.

The advent of modern sensing technologies, integration of sensor networks, remote and aerial imagery, and the application of artificial intelligence have opened new frontiers for precise, timely assessment and management of temperature and salinity stress in horticultural systems. Such interdisciplinary approaches facilitate the development of decision support tools and targeted interventions, ultimately supporting sustainable horticultural production.

This Research Topic seeks to consolidate current advances in data-driven intelligent crop protection, with a particular focus on temperature and salinity stress management in horticultural crops. Manuscripts from a wide range of disciplines—including but not limited to plant physiology, agricultural engineering, computer science, and meteorology—are welcomed. Both original research and comprehensive review papers are encouraged to foster cross-disciplinary knowledge integration.

The scope of this Research Topic includes, but is not limited to, the following themes:

• Data-driven and computational approaches for sustainable and intelligent phytoprotection against temperature and salinity stress in horticultural crops
• Applications of machine learning and AI for early detection, diagnosis, and prediction of horticultural crop responses to abiotic stresses
• Development and integration of intelligent sensors, remote sensing platforms, and UAV-based monitoring systems for optimizing phytoprotection in horticultural production
• Case studies and real-world examples showcasing sustainable, data-driven solutions for stress management in horticultural crop systems

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: Data-driven crop protection, temperature stress, salinity stress, climate adaptation, phenotyping, stress monitoring, machine learning, remote sensing, UAV, advanced sensors, sustainable horticulture

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.