Lettuce (Lactuca sativa L.) is highly susceptible to drought and water deficits, resulting in lower crop yields, unharvested areas, reduced crop health and quality. To address this, we developed a High-Throughput Phenotyping platform using Deep Learning and infrared images to detect stress stages in lettuce seedlings, which could help to apply real-time agronomical decisions from data using variable rate irrigation systems. Accordingly, a comprehensive database comprising infrared images of lettuce grown under drought-induced stress conditions was built. In order to capture the required data, we deployed a Raspberry Pi robot to autonomously collect infrared images of lettuce seedlings during an 8-day drought stress experiment. This resulted in the generation of a database containing 2119 images through augmentation. Leveraging this data, a YOLOv8 model was trained (WS-YOLO), employing instance segmentation for accurate stress level detection. The results demonstrated the efficacy of our approach, with WS-YOLO achieving a mean Average Precision (mAP) of 93.62% and an F1 score of 89.31%. Particularly, high efficiency in early stress detection was achieved, being a critical factor for improving food security through timely interventions. Therefore, our proposed High-Throughput Phenotyping platform holds the potential for high-yield lettuce breeding, enabling early stress detection and supporting informed decision-making to mitigate losses. This interdisciplinary approach highlights the potential of AI-driven solutions in addressing pressing challenges in food production and sustainability. This work contributes to the field of precision agricultural technology, providing opportunities for further research and implementation of cutting-edge Deep Learning techniques for stress detection in crops.