AI-Driven Plant Intelligence: Bridging Multimodal Sensing, Adaptive Learning, and Ecological Sustainability in Precision Plant Protection Volume II

The escalating global demand for food security, coupled with environmental pressures such as climate change and biodiversity loss, underscores the urgency for innovative agricultural solutions. Traditional plant protection methods often lack the precision and adaptability needed to address the spatiotemporal heterogeneity of plant growth and complex ecological interactions. Artificial Intelligence (AI) offers a transformative opportunity by integrating multimodal data—such as hyperspectral imagery, soil sensor readings, and UAV-based monitoring—with advanced algorithms to enhance decision-making in precision plant protection. This Research Topic explores how AI-driven “plant intelligence” can move beyond isolated applications (e.g., single-mode image recognition) toward holistic frameworks that fuse diverse data streams, adapt to dynamic field conditions, and prioritize ecological sustainability. By bridging AI with agronomy and ecology, this collection seeks to redefine plant protection strategies that are both scientifically robust and practically viable for sustainable agriculture.

Despite its promise, significant research challenges remain before AI-driven precision plant protection can fully align with ecological sustainability. This Research Topic aims to address these challenges by exploring how AI can be harnessed to protect crops while preserving environmental balance. Key questions include how multimodal data (from imagery, ground sensors, drones, etc.) can be fused for timely, accurate plant health diagnoses and which adaptive learning techniques enable AI models to stay robust against evolving threats, such as new pests or climate shifts. Another critical challenge is ensuring AI-driven interventions minimize chemical use and avoid disrupting beneficial species and ecosystems. By confronting these issues, the special article collection seeks to advance AI frameworks that deliver effective plant-protection solutions harmonized with eco-friendly practices.

This Research Topic encourages interdisciplinary contributions from experts in artificial intelligence, plant science, agronomy, plant pathology, ecology, and related disciplines. It invites research that addresses a range of themes, including but not limited to:

• Multimodal sensing and intelligent reasoning for plant protection – integrating visual, spectral, IoT sensor, environmental, and other data sources through multimodal AI and vision-language models to support advanced perception and reasoning in crop health monitoring.
• Biologically grounded, explainable, and adaptive learning – developing interpretable AI models that can adapt to changing field conditions, evolving pest and disease pressures, and complex crop–environment interactions while providing biologically meaningful predictions.
• Edge AI for real-time plant protection – deploying AI directly on drones, autonomous robots, and other field-based platforms to enable real-time monitoring, decision-making, and precision intervention.
• AI-enabled precision intervention and ecological sustainability – applying intelligent systems to targeted spraying, pest and disease management, resource optimization, and other actions that reduce chemical inputs and minimize environmental impacts.
• Plant-to-AI signals and emerging data sources – exploring biochemical, volatile, electrical, acoustic, and other plant-derived signals to improve the understanding and detection of plant health, stress responses, and crop–environment interactions.
• AI-driven decision support for sustainable agriculture – using predictive analytics and intelligent decision tools to support integrated pest management, ecological balance, and resilient plant production systems.

This Research Topic is the second volume of Research Topic "AI-Driven Plant Intelligence: Bridging Multimodal Sensing, Adaptive Learning, and Ecological Sustainability in Precision Plant Protection". Please see the first volume here. The proposal keeps the original focus on ecological sustainability while refreshing the scope to reflect current directions in AI for agriculture.

Article types and fees

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

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• 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
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence (AI), Precision Plant Protection, Multimodal AI, Vision-Language Models, Explainable AI, Adaptive Learning, Edge AI, Plant-to-AI Signaling, Integrated Pest Management, Sustainable Agriculture

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.