Precision and Digital Agriculture

To improve the effectiveness, sustainability, and resilience of crop production and agronomic processes, a variety of digital and precision technologies—such as AI, IoT, deep learning, imaging, and machine learning—are being integrated into key areas such as soil and nutrient management, agronomic crop protection, plant breeding, post-harvest handling, and resource optimization. This section highlights research that translates these innovations into practical solutions for crop and yield prediction, production, and agronomic management. Preference is given to studies where digital and precision technologies are applied within the context of crop science and agronomic systems, rather than those focused primarily on algorithm development, robotics design, or hardware engineering, without clear agricultural relevance. Focused on bridging cutting-edge advancements in digital agriculture with real-world agronomic practices, this section welcomes original research, reviews, mini-reviews, perspectives, and methodology papers that support sustainable intensification, environmental stewardship, and greater resilience in crop production systems.