Deep Learning in Plant Water Stress Assessment

Plant water stress is a critical factor limiting agricultural productivity and sustainability, particularly under changing climatic conditions. Traditional methods for assessing water stress are labor-intensive, localized, and often fail to capture the spatial and temporal variability across fields. Deep learning (DL), a rapidly evolving subfield of artificial intelligence (AI), offers transformative potential for automated, accurate, and scalable water stress detection. This chapter explores the application of DL techniques, including convolutional neural networks, recurrent neural networks, and transformers, using diverse data sources, such as RGB, hyperspectral, thermal imagery, and physiological sensors. We review the current state of research, practical applications in field and greenhouse environments, and the challenges related to data quality, model generalization, and interpretability. Furthermore, we discuss emerging trends such as explainable AI, domain adaptation, and edge computing for real-time stress monitoring. The integration of DL with remote sensing technologies is poised to revolutionize precision agriculture and water resource management.