Deep Learning in Managing Crop Production Under a Changing Climate

Climate change presents escalating challenges to global agriculture, necessitating advanced strategies to enhance resilience in crop production. This chapter explores how deep learning (DL) technologies are revolutionizing climate-adaptive farming by integrating diverse data streams—such as satellite imagery, Internet of Things (IoT) sensors, and climate models—to predict and optimize crop yields amid environmental volatility. Autonomous systems, equipped with computer vision and robotics, further enable real-time monitoring and precision tasks, such as irrigation and pest management, adapting dynamically to shifting weather patterns. Complementing these innovations, edge computing brings lightweight artificial intelligence (AI) models directly to farms, allowing rapid, localized decision-making without dependence on centralized infrastructure, while generative AI addresses data gaps by simulating realistic climate scenarios to improve predictive accuracy. A critical focus is placed on explainable AI (XAI), which demystifies complex algorithms to foster trust among farmers and policymakers, ensuring transparent and actionable insights. Emerging quantum-inspired DL methods are also examined for their potential to tackle large-scale challenges, such as optimizing water distribution and creating digital twins (DTs) of agroecosystems. However, these advancements are not without hurdles: high costs, interoperability issues, and ethical concerns about equitable access pose significant barriers. Looking ahead, interdisciplinary collaboration is essential to refine AI models, democratize their adoption, and align technological progress with sustainable, agroecological practices. By synthesizing cutting-edge innovations and persistent challenges, this chapter outlines a roadmap for leveraging DL to build adaptive, equitable, and climate-resilient agricultural systems in an uncertain future.