Deep Learning in Plant Abiotic Stress Management

In recent decades, abiotic stress factors, including drought, salinity, heat, and nutrient deficiencies, have emerged as major challenges in global agriculture, significantly reducing crop yields and threatening food security. The advent of deep learning (DL) technologies presents a transformative opportunity for improving the detection, prediction, and management of these stresses. This chapter explores the application of DL in abiotic stress management, focusing on various architectures, such as convolutional neural networks and recurrent neural networks, for stress detection in crops using high-dimensional data from sensors, imagery, and environmental inputs. This chapter reviews case studies and real-world implementations where DL has been successfully applied to identify early stress indicators, optimize resource use, and support precision agriculture practices. Furthermore, future directions in DL, including the integration of multimodal data, edge artificial intelligence (AI) for real-time processing, climate-adaptive models, and explainable AI, are discussed. By enhancing early detection and enabling targeted interventions, DL has the potential to revolutionize how we manage abiotic stress and build climate-resilient, sustainable agricultural systems.