Soil heavy metal contamination poses a serious threat to agricultural product safety and public health, which urgently calls for the development of rapid and accurate in situ detection techniques.

The accurate detection of burn wounds is a key research direction in the field of burn medicine, as diagnostic results directly influence the risk of wound infection and the formation of hypertrophic scars. Currently, burn diagnosis is primarily dependent on the clinical judgment of physicians, but its accuracy is typically only between 65% and 70%. Therefore, a non-invasive, efficient method for burn severity assessment is urgently needed. Hyperspectral imaging (HSI), as a non-invasive and contactless spectral detection technique, has been shown to precisely monitor structural changes in burn-affected skin tissue and holds significant potential for burn depth diagnosis. However, research on the application of burn severity detection remains relatively limited, which restricts its widespread use in clinical settings. A burn severity detection classification network (MBNet) based on the Mamba model is proposed in this paper. Through a bidirectional scanning strategy, MBNet effectively captures the long-term dependencies of spectral features, accurately establishes the relationships between bands, and efficiently distinguishes subtle spectral differences under different burn conditions. MBNet provides a reliable and efficient method for clinical burn severity assessment. A comparison of MBNet with seven typical machine learning algorithms on a custom dataset demonstrates that MBNet significantly outperforms these methods in terms of accuracy.