| 摘要 |
With breakthroughs in data processing and pattern recognition through deep learning technologies, the use of advanced algorithmic models for analyzing and interpreting soil spectral information has provided an efficient and economical method for soil quality assessment. However, traditional single-output networks exhibit limitations in the prediction process, particularly in their inability to fully utilize the correlations among various elements. As a result, single-output networks tend to be optimized for a single task, neglecting the interrelationships among different soil elements, which limits prediction accuracy and model generalizability. To overcome this limitation, in this study, a multi-task learning architecture with a progressive extraction network was implemented for the simultaneous prediction of multiple indicators in soil, including nitrogen (N), organic carbon (OC), calcium carbonate (CaCO₃), cation exchange capacity (CEC), and pH. Furthermore, while incorporating the Pearson correlation coefficient, convolutional neural networks, long short-term memory networks and attention mechanisms were combined to extract local abstract features from the original spectra, thereby further improving the model. This architecture is referred to as the Relevance-sharing Progressive Layered Extraction Network. The model employs an adaptive joint loss optimization method to update the weights of individual task losses in the multi-task learning training process. |
