Sang Qiong, Zhao Xiaoyu, Zhao Yue, Cai Lijing, Liu Jinming, Tong Liang, Zhai Zhe
Heilongjiang Bayi Agricultural University, China.
Qiqihar University, China.
Anal Methods. 2024 Oct 17;16(40):6888-6898. doi: 10.1039/d4ay01202j.
The excessive use of fertilizers can lead to increased production costs, degraded soil quality, diminished product excellence, and environmental contamination. To address this issue, a solution involving soil testing and customizing fertilizer application has been proposed. The current standard methodology for soil parameter assessment relies on chemical analysis performed by trained laboratory technicians, which only allows for the measurement of one indicator at a time. Hence, a novel approach utilizing the fusion of near-infrared (NIR) and Raman dual-spectral features has been suggested to simultaneously determine five crucial indicators (hydrolyzed N, available P, quick-release K, OM, and pH) in soil with a single scan. In this research, seven preprocessing techniques and four feature extraction methods were initially explored to optimize the composite NIR and Raman feature variables. Subsequently, a regressor with a two-layer network structure (RF, LR, SVR; ELM, and PLS) was developed using the stacking algorithm. This methodology synergizes the strengths of the five base learners, minimizes the risk of overfitting, and demonstrates high computational efficiency for linear data correlations and robust fitting capabilities for nonlinear data correlations. Additionally, it showcases strong generalization capabilities, noise resilience, and robustness. The model produced relevant results for hydrolyzed N, available P, quick release K, OM, and pH measurements, with values of 0.9966, 0.9722, 0.9855, 0.9557, and 0.9951, RMSEP values of 2.9547, 2.9972, 7.6550, 0.0765, and 0.0313, and RPD values of 6.0855, 2.4655, 3.0511, 8.3084, and 10.6977. This work delivers a twofold contribution by presenting a swift method for simultaneous measurement of multiple soil parameters, enabling concurrent ploughing, soil surveying, and fertilizer application. Furthermore, it introduces a stacking measurement model based on dual fusion features, showcasing detailed model parameters. The stacking model outperformed mono-spectral models (NIR and Raman) and the dual PLS model in terms of , RPD, and RMSEP values, and fluctuation ranges, demonstrating enhanced stability, predictive prowess, and reliable observations. Overall, the stacking model offers a cost-effective, rapid, and precise solution for online evaluation of soil physicochemical conditions, catering to the requirements of modern agricultural production well. This innovative approach signifies a significant leap forward and provides a solid theoretical foundation for the enhancement of associated online monitoring systems and tools.
过度使用化肥会导致生产成本增加、土壤质量下降、产品品质降低以及环境污染。为解决这一问题，已提出一种涉及土壤测试和定制施肥的解决方案。当前用于土壤参数评估的标准方法依赖于由训练有素的实验室技术人员进行的化学分析，该方法一次仅能测量一项指标。因此，有人建议采用一种利用近红外（NIR）和拉曼双光谱特征融合的新方法，通过单次扫描同时测定土壤中的五个关键指标（水解氮、有效磷、速效钾、有机质和pH值）。在本研究中，首先探索了七种预处理技术和四种特征提取方法，以优化复合近红外和拉曼特征变量。随后，使用堆叠算法开发了一种具有两层网络结构的回归器（随机森林、逻辑回归、支持向量回归；极限学习机和偏最小二乘法）。该方法综合了五个基础学习器的优势，将过拟合风险降至最低，对于线性数据相关性具有较高的计算效率，对于非线性数据相关性具有强大的拟合能力。此外，它还具有很强的泛化能力、抗噪声能力和鲁棒性。该模型在水解氮、有效磷、速效钾、有机质和pH值测量方面产生了相关结果，其决定系数值分别为0.9966、0.9722、0.9855、0.9557和0.9951，均方根误差值分别为2.9547、2.9972、7.6550、0.0765和0.0313，相对分析误差值分别为6.0855、2.4655、3.0511、8.3084和10.6977。这项工作有两方面的贡献，一是提出了一种快速同时测量多种土壤参数的方法，能够实现同步耕作、土壤勘测和施肥。此外，它还引入了一种基于双融合特征的堆叠测量模型，并展示了详细的模型参数。在决定系数、相对分析误差和均方根误差值以及波动范围方面，堆叠模型优于单光谱模型（近红外和拉曼）和双偏最小二乘法模型，显示出更高的稳定性、预测能力和可靠的观测结果。总体而言,堆叠模型为土壤理化条件的在线评估提供了一种经济高效、快速且精确的解决方案，很好地满足了现代农业生产的需求。这种创新方法标志着向前迈出了重要一步，为相关在线监测系统和工具的改进提供了坚实的理论基础。
