This book introduces the fundamentals of deep learning models and their current applications in a diverse range of subtopics in plant sciences and agriculture. Areas covered include: functional genomics, multiple omics, stress physiology, plant disease diagnosis, plant-microbe interactions, and high-throughput technologies (phenotyping and synthetic biology). Deep-learning models are also being used in developing pest and weed control strategies, the use of unmanned aerial vehicles in agriculture, greenhouse management and strategies for stress-smart and sustainable precision agriculture. This book offers an ideal reference for readers in a range of research fields in plant biology, plant pathology, and agricultural crop sciences. It is designed to be an inspiration for new ideas and future experiments to advance the progress of stress-smart and sustainable agriculture.