Deep learning-assisted portable fluorescence device for dynamic monitoring of mercury absorption in rice

Rice is a major pathway for mercury exposure in inland China, making dynamic monitoring of Hg²⁺ absorption essential. In this study, nitrogen-doped carbon dots (N-CDs) were synthesized via a one-step hydrothermal method using melamine and ammonium citrate, achieving a quantum yield of 20.92%. Leveraging their high sensitivity to Hg²⁺, a fluorescent detection method with a detection limit (DL) of 85 nM was established. Building upon this, a portable device combining a 3D-printed device and smartphone was developed to capture fluorescence images under varying Hg²⁺ concentrations. A convolutional neural network extracted RGB and HSV features to quantitatively map Hg²⁺ levels (R² = 0.998) across 1-50 μM, with a DL of 0.2 μM. The device showed excellent recovery (96.33%-113.67%) in tap water, river water and wastewater samples. Application to rice demonstrated enhanced Hg²⁺ absorption after mercury adaptation. This study provides a simple, effective strategy for field detection of Hg²⁺ in the environment.