Climate-Smart Agriculture (CSA) aims to enhance the productivity, resilience, and sustainability of farming systems in the face of climate variability and change. As environmental stresses like drought, heatwaves, erratic rainfall, and soil degradation intensify, data-driven innovations have become essential for adaptive and informed decision-making in agriculture. Artificial Intelligence (AI), including machine learning and deep learning, is increasingly recognized as a transformative force in this domain.

AI techniques are revolutionizing how we analyze complex agroecological data by enabling dynamic modelling of soil characteristics, crop performance, and climate interactions. These technologies help develop predictive systems for crop yield forecasting, climate risk modelling, adaptive irrigation, and sustainable resource allocation. Deep learning, especially when applied to image analysis and spatio-temporal datasets, offers scalable solutions for monitoring climate impacts, assessing soil variability, and managing crop health in real-time.

This Research Topic seeks to present the latest advancements and practical applications of AI in the context of Climate-Smart Agriculture. The objective is to explore how AI technologies are driving resilient and adaptive agronomic strategies that align with CSA goals—namely, increasing productivity, enhancing resilience to climate risks, and reducing greenhouse gas emissions.

We welcome contributions that offer methodological innovations, integrated frameworks, or field-based case studies demonstrating AI's potential in shaping climate-resilient agricultural systems. This issue aims to serve as a platform for cross-disciplinary collaboration and to inform policy, research, and practice toward sustainable digital agriculture.

We are especially interested in original research and applied studies addressing, but not limited to, the following areas:

• AI-enabled assessment and monitoring of soil health under changing climate conditions

• Machine learning models for yield prediction in variable climatic regimes

• Deep learning applications for early detection of climate-induced crop stress and disease

• Precision agriculture techniques using AI for adaptive irrigation, fertilization, and pest management in CSA contexts

• Integration of multisource data (e.g., satellite, sensor, UAV, and weather data) using AI for agroecological modelling

• Predictive analytics for climate risk assessment, mitigation, and adaptation strategies

• AI-driven decision support systems tailored for smallholders and climate-vulnerable regions

• Explainable AI (XAI) for transparent and trustworthy decision-making in CSA

• Case studies on operationalizing AI in climate-resilient farming practices

• Interdisciplinary approaches linking AI with agroecology, environmental science, and climate policy

Are you interested in contributing a manuscript to this Research Topic?

AI-Powered Soil, Crop, and Climate Analytics: Advances and Applications in Climate-Smart Agriculture