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基于中紅外光譜特征增強和集成學(xué)習(xí)的土壤有機碳含量估算模型研究
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云南省科技計劃項目(202202AE090013)、黑龍江省“揭榜掛帥”科技攻關(guān)項目(2021ZXJ05A0502)和重慶市技術(shù)創(chuàng)新與應(yīng)用發(fā)展專項(cstc2021jscx-gksbX0064)


Estimation Model of Soil Organic Carbon Content Based on Mid-infrared Spectral Characteristics Enhancement and Ensemble Learning
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    摘要:

    中紅外光譜數(shù)據(jù)在實現(xiàn)土壤有機碳含量的準(zhǔn)確、低成本快速預(yù)測方面具有巨大潛力。為提高光譜數(shù)據(jù)估算模型的普適性,本研究利用光譜特征增強策略,并基于Stacking算法結(jié)合多種機器學(xué)習(xí)方法構(gòu)建了一種高魯棒性的土壤有機碳含量估算模型。采用多種光譜特征增強方法及其組合對土壤中紅外光譜進(jìn)行特征增強,篩選最佳策略;通過應(yīng)用Stacking算法結(jié)合多種機器學(xué)習(xí)方法構(gòu)建集成模型,以提高模型泛化能力;將集成模型估算性能與偏最小二乘回歸模型(PLSR)、梯度提升樹(GBT)和一維卷積神經(jīng)網(wǎng)絡(luò)(1D-CNN)模型進(jìn)行比較分析。研究結(jié)果表明,最佳光譜特征增強策略可以顯著提高土壤光譜數(shù)據(jù)與土壤有機碳含量的相關(guān)性,最佳Pearson相關(guān)系數(shù)達(dá)到 -0.82;相較于PLSR、GBT和1D-CNN等模型,集成模型在各光譜數(shù)據(jù)下均表現(xiàn)出較高的估算精度,特別是在一階導(dǎo)變換結(jié)合多元散射校正的光譜特征增強策略下,集成模型展現(xiàn)出優(yōu)良的估算性能(決定系數(shù)R2=0.92,均方根誤差為1.18g/kg,相對分析誤差為3.52)。本研究方法能夠快速、準(zhǔn)確地估算土壤有機碳含量,可為現(xiàn)代農(nóng)業(yè)管理提供科學(xué)依據(jù)。

    Abstract:

    Mid-infrared spectral data holds immense potential for accurate, cost-effective, and rapid prediction of soil organic carbon (SOC) content. To enhance the universality of spectroscopic data estimation models, a spectroscopic feature enhancement strategy was employed and combined multiple machine learning methods by using the Stacking algorithm to construct a robust model for estimating SOC content. Various spectroscopic feature enhancement methods and their combinations were applied to enhance the features of mid-infrared soil spectra and select the optimal strategies. The Stacking algorithm was used in conjunction with multiple machine learning methods to build an ensemble model, aiming to improve the model’s generalization ability. The estimation performance of the ensemble model was compared with that of partial least squares regression (PLSR), gradient boosting trees (GBT), and 1-dimensional convolutional neural network (1D-CNN) models. The results demonstrated that the optimal spectral characteristics enhancement strategy can significantly improve the correlation between soil spectra and soil organic carbon content, and the optimal Pearson correlation coefficient reached -0.82. Compared with PLSR, GBT, and 1D-CNN models, the ensemble model exhibited higher estimation accuracy and robustness across various spectral datasets. In particular, under the spectral characteristic enhancement strategy of first derivative combined with multivariate scatter correction, the ensemble model demonstrated excellent estimation performance (R2=0.92, RMSE was 1.18g/kg, RPD was 3.52). The proposed method enabled timely and accurate estimation of SOC, which can provide a scientific basis for modern agricultural management.

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唐澳華,楊貴軍,楊悅,陳偉男,徐新剛,徐波,高美玲,張靜.基于中紅外光譜特征增強和集成學(xué)習(xí)的土壤有機碳含量估算模型研究[J].農(nóng)業(yè)機械學(xué)報,2024,55(8):382-390. TANG Aohua, YANG Guijun, YANG Yue, CHEN Weinan, XU Xin’gang, XU Bo, GAO Meiling, ZHANG Jing. Estimation Model of Soil Organic Carbon Content Based on Mid-infrared Spectral Characteristics Enhancement and Ensemble Learning[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(8):382-390.

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  • 收稿日期:2023-11-23
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  • 在線發(fā)布日期: 2024-08-10
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