Continued mining operations have resulted in substantial soil degradation, necessitating the effective restoration of ecological functions in soils. Accurate and rapid measurement of soil organic matter (SOM) is essential for boosting soil fertility, supporting ecological restoration, and facilitating effective environmental management. Combining visible to near-infrared spectroscopy with machine learning algorithms is a promising technique for quantitative analysis of SOM. Firstly, the paper utilized a spectral pre-processing method that integrates fractional order differentiation transformation (FOD) and optimal band combination (OBC) algorithm. OBC algorithm was used to construct six three-band spectral indices to obtain optimal spectral combination parameters. Then, the HOVD-TELM model was constructed based on the hybrid model of two-hidden-layer extreme learning machine and Harris hawk optimizer. The opposition-based learning, vertical crossover operator and disruption operator were added to prevent the model from converging prematurely. The experimental results showed that: (1) compared with the pre-processing methods such as integer order differentiation and two-band spectral index, the FOD and OBC methods used in this paper obtained more ideal spectral pre-processing effects. (2) compared with models such as Partial least square regression and Extreme gradient boosting, the HOVD-TELM model proposed in this paper obtained the optimal prediction performance, with the minimum RMSE of 6.7874 g·kg and the maximum R of 0.9209, indicating good prediction reliability. In summary, this paper proposed a fast and accurate method for detecting soil organic matter content and improves the estimation accuracy of SOM content.

中文翻译：

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一种基于可见-近红外光谱的土壤有机质含量定量检测新方法


持续的采矿作业导致土壤严重退化，需要有效恢复土壤的生态功能。准确快速测量土壤有机质（SOM）对于提高土壤肥力、支持生态恢复和促进有效的环境管理至关重要。将可见光到近红外光谱与机器学习算法相结合是一种很有前途的 SOM 定量分析技术。首先，本文采用了分数阶微分变换（FOD）和最佳波段组合（OBC）算法相结合的光谱预处理方法。采用OBC算法构建6个三波段光谱指数以获得最优光谱组合参数。然后，基于两隐层极限学习机和Harris hawk优化器的混合模型构建了HOVD-TELM模型。添加了基于对立的学习、垂直交叉算子和破坏算子，以防止模型过早收敛。实验结果表明：（1）与整数阶微分、两波段光谱指数等预处理方法相比，本文采用的FOD和OBC方法获得了更为理想的光谱预处理效果。 (2) 与偏最小二乘回归和Extreme梯度提升等模型相比，本文提出的HOVD-TELM模型获得了最优的预测性能，最小RMSE为6.7874 g·kg，最大R为0.9209，表现良好预测的可靠性。综上所述，本文提出了一种快速、准确的土壤有机质含量检测方法，提高了SOM含量的估算精度。