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

Background:

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.


Goals:

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.


Scope and Information for Authors:

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

• Multimodal sensor fusion for crop health monitoring – combining visual, spectral, and IoT sensor data for early and accurate detection of plant diseases, pests, and other stressors.
• Adaptive learning algorithms for dynamic conditions – enabling AI models to adjust to evolving pest and disease pressures and changing environmental conditions.
• Precision pest and disease intervention – targeted AI-guided actions (e.g., smart spraying drones, robotic systems) that minimize chemical use and reduce unintended environmental impact.
• AI-driven decision support for sustainable agriculture – predictive analytics and decision tools to inform integrated pest management strategies, optimize resource use, and maintain ecological balance.

Keywords: Artificial Intelligence (AI), Precision Plant Protection, Multimodal Sensing, Adaptive Learning, 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.