激光与光电子学进展, 2019, 56 (13): 131102, 网络出版: 2019-07-11   

基于梯度提升树的土壤速效磷高光谱回归预测方法 下载: 985次

Predicting Soil Available Phosphorus by Hyperspectral Regression Method Based on Gradient Boosting Decision Tree
作者单位
安徽农业大学信息与计算机学院, 安徽 合肥 230036
引用该论文

金秀, 朱先志, 李绍稳, 王文才, 齐海军. 基于梯度提升树的土壤速效磷高光谱回归预测方法[J]. 激光与光电子学进展, 2019, 56(13): 131102.

Xiu Jin, Xianzhi Zhu, Shaowen Li, Wencai Wang, Haijun Qi. Predicting Soil Available Phosphorus by Hyperspectral Regression Method Based on Gradient Boosting Decision Tree[J]. Laser & Optoelectronics Progress, 2019, 56(13): 131102.

参考文献

[1] Ben-Dor E, Banin A. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties[J]. Soil Science Society of America Journal, 1995, 59(2): 364-372.

[2] 吴茜, 杨宇虹, 徐照丽, 等. 应用局部神经网络和可见/近红外光谱法估测土壤有效氮磷钾[J]. 光谱学与光谱分析, 2014, 34(8): 2102-2105.

    Wu Q, Yang Y H, Xu Z L, et al. Applying local neural network and visible/near-infrared spectroscopy to estimating available nitrogen, phosphorus and potassium in soil[J]. Spectroscopy and Spectral Analysis, 2014, 34(8): 2102-2105.

[3] 李雪莹, 范萍萍, 侯广利, 等. 可见-近红外光谱的土壤养分快速检测[J]. 光谱学与光谱分析, 2017, 37(11): 3562-3566.

    Li X Y, Fan P P, Hou G L, et al. Rapid detection of soil nutrients based on visible and near infrared spectroscopy[J]. Spectroscopy and Spectral Analysis, 2017, 37(11): 3562-3566.

[4] Shao Y N, He Y. Nitrogen, phosphorus, and potassium prediction in soils, using infrared spectroscopy[J]. Soil Research, 2011, 49(2): 166-172.

[5] 贾生尧, 杨祥龙, 李光, 等. 近红外光谱技术结合递归偏最小二乘算法对土壤速效磷与速效钾含量测定研究[J]. 光谱学与光谱分析, 2015, 35(9): 2516-2520.

    Jia S Y, Yang X L, Li G, et al. Quantitatively determination of available phosphorus and available potassium in soil by near infrared spectroscopy combining with recursive partial least squares[J]. Spectroscopy and Spectral Analysis, 2015, 35(9): 2516-2520.

[6] Gatius F, Miralbés C, David C, et al. Comparison of CCA and PLS to explore and model NIR data[J]. Chemometrics and Intelligent Laboratory Systems, 2017, 164: 76-82.

[7] Kawamura K, Tsujimoto Y, Rabenarivo M, et al. Vis-NIR spectroscopy and PLS regression with waveband selection for estimating the total C and N of paddy soils in Madagascar[J]. Remote Sensing, 2017, 9(10): 1081.

[8] Genisheva Z, Quintelas C, Mesquita D P, et al. New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy (NIR)[J]. Food Chemistry, 2018, 246: 172-178.

[9] Sarathjith M C, Das B S, Wani S P, et al. Comparison of data mining approaches for estimating soil nutrient contents using diffuse reflectance spectroscopy[J]. Current Science, 2016, 110(6): 1031-1037.

[10] 张佳佳, 郭熙, 赵小敏. 南方丘陵稻田土壤全磷、有效磷高光谱特征与反演模型[J]. 江苏农业科学, 2016, 44(7): 522-525.

    Zhang J J, Guo X, Zhao X M. Hyperspectral characteristics and inversion models of total phosphorus and available phosphorus in paddy fields in southern hilly China[J]. Jiangsu Agricultural Sciences, 2016, 44(7): 522-525.

[11] 齐海军, 李绍稳, Karnieli Arnon, 等. 基于PLS-BPNN算法的土壤速效磷高光谱回归预测方法[J]. 农业机械学报, 2018, 49(2): 166-172.

    Qi H J, Li S W, Arnon K, et al. Prediction method of soil available phosphorus using hyperspectral data based on PLS-BPNN[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(2): 166-172.

[12] 王文才, 李绍稳, 齐海军, 等. 土壤速效磷含量成像和非成像光谱预测差异性分析[J]. 江苏农业学报, 2018, 34(4): 811-817.

    Wang W C, Li S W, Qi H J, et al. The difference analysis of soil available phosphors content imaging and non-imaging spectra prediction[J]. Jiangsu Journal of Agricultural Sciences, 2018, 34(4): 811-817.

[13] 付忠良. 通用集成学习算法的构造[J]. 计算机研究与发展, 2013, 50(4): 861-872.

    Fu Z L. A universal ensemble learning algorithm[J]. Journal of Computer Research and Development, 2013, 50(4): 861-872.

[14] Kaneko H, Funatsu K. Applicability domain based on ensemble learning in classification and regression analyses[J]. Journal of Chemical Information and Modeling, 2014, 54(9): 2469-2482.

[15] Okujeni A, van der Linden S, Suess S, et al. . Ensemble learning from synthetically mixed training data for quantifying urban land cover with support vector regression[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(4): 1640-1650.

[16] Mesquita D P P, Gomes J P P, Souza Junior A H. Ensemble of efficient minimal learning machines for classification and regression[J]. Neural Processing Letters, 2017, 46(3): 751-766.

[17] 郑曼迪, 熊黑钢, 乔娟峰, 等. 基于宽波段与窄波段综合光谱指数的土壤有机质遥感反演[J]. 激光与光电子学进展, 2018, 55(7): 072801.

    Zheng M D, Xiong H G, Qiao J F, et al. Remote sensing inversion of soil organic matter based on broad band and narrow band comprehensive spectral index[J]. Laser & Optoelectronics Progress, 2018, 55(7): 072801.

[18] 应璐娜, 周卫东. 对比分析多种化学计量学方法在激光诱导击穿光谱土壤元素定量分析中的应用[J]. 光学学报, 2018, 38(12): 1214002.

    Ying L N, Zhou W D. Comparative analysis of multiple chemometrics methods in application of laser-induced breakdown spectroscopy for quantitative analysis of soil elements[J]. Acta Optica Sinica, 2018, 38(12): 1214002.

[19] Sampaio P S, Soares A, Castanho A, et al. Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms[J]. Food Chemistry, 2018, 242: 196-204.

[20] 邹婷婷, 王莹, 宋焕禄. 牛乳清粉掺伪羊乳粉的近红外光谱法快速无损检测[J]. 中国食品学报, 2017, 17(8): 261-267.

    Zou T T, Wang Y, Song H L. Near infrared spectroscopy combined with support vector regression applied for rapid and nondestructive detection of adulterate goat milk powder[J]. Journal of Chinese Institute of Food Science and Technology, 2017, 17(8): 261-267.

[21] Ni W D, Nørgaard L, Mørup M. Non-linear calibration models for near infrared spectroscopy[J]. Analytica Chimica Acta, 2014, 813: 1-14.

[22] Nie P C, Wu D, Yang Y, et al. Fast determination of boiling time of yardlong bean using visible and near infrared spectroscopy and chemometrics[J]. Journal of Food Engineering, 2012, 109(1): 155-161.

[23] Ting J A. D'Souza A, Vijayakumar S, et al. Efficient learning and feature selection in high-dimensional regression[J]. Neural Computation, 2010, 22(4): 831-886.

[24] Kalika D, Morton K D, Collins L M, et al. Hyperbolic and PLSDA filter algorithms to detect buried threats in GPR data[J]. Proceedings of SPIE, 2014, 9072: 90720U.

[25] JainA, SmarraF, MangharamR. Data predictive control using regression trees and ensemble learning[C]∥2017 IEEE 56th Annual Conference on Decision and Control (CDC), December 12-15, 2017, Melbourne, VIC, Australia. New York: IEEE, 2017: 4446- 4451.

[26] Kaneko H. Automatic outlier sample detection based on regression analysis and repeated ensemble learning[J]. Chemometrics and Intelligent Laboratory Systems, 2018, 177: 74-82.

[27] KabirA, RuizC, Alvarez SA, et al. Regression, classification and ensemble machine learning approaches to forecasting clinical outcomes in ischemic stroke[M] ∥Peixoto N, Silveira M, Ali H, et al. Biomedical engineering systems and technologies. Cham: Springer, 2018, 881: 376- 402.

[28] Alazzam I, Alsmadi I, Akour M. Software fault proneness prediction: a comparative study between bagging, boosting, and stacking ensemble and base learner methods[J]. International Journal of Data Analysis Techniques and Strategies, 2017, 9(1): 1-16.

[29] 李盛芳, 贾敏智, 董大明. 随机森林算法的水果糖分近红外光谱测量[J]. 光谱学与光谱分析, 2018, 38(6): 1766-1771.

    Li S F, Jia M Z, Dong D M. Fast measurement of sugar in fruits using near infrared spectroscopy combined with random forest algorithm[J]. Spectroscopy and Spectral Analysis, 2018, 38(6): 1766-1771.

[30] 葛翔宇, 丁建丽, 王敬哲, 等. 基于竞争适应重加权采样算法耦合机器学习的土壤含水量估算[J]. 光学学报, 2018, 38(10): 1030001.

    Ge X Y, Ding J L, Wang J Z, et al. Estimation of soil moisture content based on competitive adaptive reweighted sampling algorithm coupled with machine learning[J]. Acta Optica Sinica, 2018, 38(10): 1030001.

[31] 孔清清, 丁香乾, 宫会丽. 改进的修剪随机森林算法在烟叶近红外光谱产地识别中的应用研究[J]. 激光与光电子学进展, 2018, 55(1): 013006.

    Kong Q Q, Ding X Q, Gong H L. Application of improved random forest pruning algorithm in tobacco origin identification of near infrared spectrum[J]. Laser & Optoelectronics Progress, 2018, 55(1): 013006.

[32] Gao Y, Cui L J, Lei B, et al. Estimating soil organic carbon content with visible-near-infrared (Vis-NIR) spectroscopy[J]. Applied Spectroscopy, 2014, 68(7): 712-722.

[33] Chang C W, Laird D A, Mausbach M J, et al. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[J]. Soil Science Society of America Journal, 2001, 65(2): 480-490.

[34] 石应福, 常淑平. 对碳酸氢钠浸提—钼锑抗比色法测定高含量有机质土壤有效磷的改进试验[J]. 甘肃农大学报, 1984, 19(2): 108-111.

    Shi Y F, Chang S P. A study of determining the available phosphorus in high orgnaic soils by means of NaHCO3 extraction, ammonium molybdate-tartaric emetic-ascrbic acid colorimetry[J]. Journal of Gansu Agricultural University, 1984, 19(2): 108-111.

[35] Claeys D D, Verstraelen T, Pauwels E, et al. Conformational sampling of macrocyclic alkenes using a Kennard-Stone-based algorithm[J]. The Journal of Physical Chemistry A, 2010, 114(25): 6879-6887.

[36] 刘桂松, 郭昊淞, 潘涛, 等. Vis-NIR光谱模式识别结合SG平滑用于转基因甘蔗育种筛查[J]. 光谱学与光谱分析, 2014, 34(10): 2701-2706.

    Liu G S, Guo H S, Pan T, et al. Vis-NIR spectroscopic pattern recognition combined with SG smoothing applied to breed screening of transgenic sugarcane[J]. Spectroscopy and Spectral Analysis, 2014, 34(10): 2701-2706.

[37] Bayer A, Bachmann M, Müller A, et al. A comparison of feature-based MLR and PLS regression techniques for the prediction of three soil constituents in a degraded South African ecosystem[J]. Applied and Environmental Soil Science, 2012, 2012: 971252.

[38] Rossel R A V, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra[J]. Geoderma, 2010, 158(1/2): 46-54.

[39] 彭杰, 张杨珠, 周清, 等. 土壤理化特性与土壤光谱特征关系的研究进展[J]. 土壤通报, 2009, 40(5): 1204-1208.

    Peng J, Zhang Y Z, Zhou Q, et al. The progress on the relationship physics-chemistry properties with spectrum characteristic of the soil[J]. Chinese Journal of Soil Science, 2009, 40(5): 1204-1208.

[40] Ji W. Viscarra Rossel R A, Shi Z. Accounting for the effects of water and the environment on proximally sensed Vis-NIR soil spectra and their calibrations[J]. European Journal of Soil Science, 2015, 66(3): 555-565.

金秀, 朱先志, 李绍稳, 王文才, 齐海军. 基于梯度提升树的土壤速效磷高光谱回归预测方法[J]. 激光与光电子学进展, 2019, 56(13): 131102. Xiu Jin, Xianzhi Zhu, Shaowen Li, Wencai Wang, Haijun Qi. Predicting Soil Available Phosphorus by Hyperspectral Regression Method Based on Gradient Boosting Decision Tree[J]. Laser & Optoelectronics Progress, 2019, 56(13): 131102.

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