Temporal germination of lettuce under salinity and nano-silicon: A high-throughput phenomics framework with deep learning.

Accurate high-throughput evaluation of seed germination under abiotic stress is often hindered by subjective manual scoring and insufficient temporal resolution. This study introduces an integrated phenomics framework leveraging an explainable deep learning model for real-time monitoring of lettuce (Lactuca sativa L.) germination dynamics under salinity stress and nano-silicon priming. Utilizing a custom X-Y motorized imaging system, we captured continuous time-lapse data across 16 treatment combinations (0-60 mM NaCl × 0-300 mg L⁻¹ nano-SiO₂). We developed an ultra-lightweight architecture, YOLO26n-Ghost-EMA, which integrates Ghost convolutions and Efficient Multi-scale Attention. This model achieved 99.46% mAP@50 with a 4.5 ms inference time, providing a high detection accuracy while maintaining a lightweight architecture and favorable accuracy-efficiency trade-off compared with standard YOLO variants. while reducing computational demand by 35-50%. To ensure biological validity, Explainable AI (XAI) via Grad-CAM confirmed that the model precisely targets radicle protrusion zones, eliminating 'black-box' opacity. Response Surface Methodology (RSM) quantified the potent ameliorative effect of nano-SiO₂, identifying 100 mg L⁻¹ as the optimal concentration to recover germination from 58.57% to 84.28% under severe salinity (60 mM NaCl). By bridging real-time computer vision and plant stress physiology, this framework provides a scalable, high-resolution solution for precision seed biology and rapid assessment of abiotic stress.