浏览全部资源
扫码关注微信
1. 中国科学院 合肥智能机械研究所,安徽 合肥,230031
2. 中国科学技术大学 自动化系,安徽 合肥,230027
纸质出版日期:2018-10-5,
网络出版日期:2018-4-16,
收稿日期:2017-12-20,
修回日期:2018-3-6,
扫 描 看 全 文
史杨, 王儒敬, 汪玉冰. 利用改进自动编码器光谱法预测土壤有机质[J]. 发光学报, 2018,39(10): 1458-1465
SHI Yang, WANG Ru-jing, WANG Yu-bing. Prediction of Soil Organic Matter by Improved Auto Encoder Based on Near-infrared Spectroscopy[J]. Chinese Journal of Luminescence, 2018,39(10): 1458-1465
史杨, 王儒敬, 汪玉冰. 利用改进自动编码器光谱法预测土壤有机质[J]. 发光学报, 2018,39(10): 1458-1465 DOI: 10.3788/fgxb20183910.1458.
SHI Yang, WANG Ru-jing, WANG Yu-bing. Prediction of Soil Organic Matter by Improved Auto Encoder Based on Near-infrared Spectroscopy[J]. Chinese Journal of Luminescence, 2018,39(10): 1458-1465 DOI: 10.3788/fgxb20183910.1458.
提出一种改进自动编码器方法,用于利用近红外光谱预测大尺度下土壤有机质含量等级。首先,提出改进自动编码器算法框架,将传统的用于重建输出的自动编码器与分类器相结合;对改进自动编码器中的损失函数进行定义。然后,将改进自动编码器应用于预测土壤有机质含量等级的近红外光谱分析建模问题中,使用双层前馈神经网络实现了改进自动编码器的编码器、解码器和分类器。最后,使用大尺度土壤光谱数据集对模型进行训练,预测土壤有机质含量等级,并与主成分回归、支持向量机等方法的效果进行对比。实验结果表明,基于改进自动编码器的土壤有机质含量等级分类准确率为63.05%,高于其他方法。利用该模型预测大尺度下土壤有机质含量等级有较好的表现。
This paper presents a calibration model
namely
improved auto encoder
which can be used to predict the grade of soil organic matter content in large scale based on near infrared spectroscopy. First
the framework of improved auto encoder model was proposed
which combined traditional auto encoder and classifier
and loss function of the model is defined. Then
the proposed improved auto encoder was applied to predict the grade of soil organic matter content based on near infrared spectroscopy. The encoder
decoder and classifier were implemented with two-layer feed-forward neural networks. Finally
a large scale soil spectral dataset was used to train the model for predicting the grade of soil organic matter content. The performance was compared with the results of principal component regression and support vector machine. The results show that the classification accuracy of soil organic matter content grades based on the proposed improved auto encoder model is 63.05%
which is better than other methods. This model can be used to predict the grades of soil organic matter content in a large scale.
近红外光谱自动编码器土壤有机质建模方法
NIR spectroscopyauto encodersoil organic mattermodeling
宋海燕. 土壤近红外光谱检测[M]. 北京:化学工业出版社, 2013. SONG H Y. Soil Near Infrared Spectroscopy Test[M]. Beijing:Chemical Industry Press, 2013. (in Chinese)
褚小立, 陆婉珍. 近五年我国近红外光谱分析技术研究与应用进展[J]. 光谱学与光谱分析, 2014, 34(10):2595-2605. CHU X L, LU W Z. Research and application progress of near infrared spectroscopy analytical technology in China in the past five years[J]. Spectrosc. Spect. Anal., 2014, 34(10):2595-2605. (in Chinese)
RAV R, WEBSTER R. Predicting soil properties from the Australian soil visible-near infrared spectroscopic database[J]. Eur. J. Soil Sci., 2012, 63(6):848-860.
胡晓艳, 宋海燕. 基于支持向量机和近红外光谱特性的土壤质地分类[J]. 山西农业科学, 2017, 45(10):1643-1645. HU X, SONG H Y. Soil texture classification based on support vector machine and near infrared spectral characteristics[J]. J. Shanxi Agricult. Sci., 2017, 45(10):1643-1645. (in Chinese)
刘广霖, 郭焱, 劳彩莲, 等. 基于田间原位土壤含水量估测的可见/近红外光谱建模方法[J]. 中国农业大学学报, 2016, 21(8):125-131. LIU G L, GUO Y, LAO C L, et al.. Esimation of soil water content in situ by using visible/near infrared spectrum modeling[J]. J. China Agricult. Univ., 2016, 21(8):125-131. (in Chinese)
吴龙国, 王松磊, 何建国, 等. 基于高光谱成像技术的土壤水分机理研究及模型建立[J]. 发光学报, 2017, 38(10):1366-1376. WU L G, WANG S L, HE J G, et al.. Soil moisture mechanism and establishment of model based on hyperspectral imaging technique[J]. Chin. J. Lumin., 2017, 38(10):1366-1376. (in Chinese)
张瑶, 李民赞, 郑立华, 等. 基于近红外光谱分析的土壤分层氮素含量预测[J]. 农业工程学报, 2015, 31(9):121-126. ZHANG Y, LI M Z, ZHENG L H, et al.. Prediction of soil total nitrogen content in different layers based on near infrared spectral analysis[J]. Trans. Chin. Soc. Agricult. Eng., 2015, 31(9):121-126. (in Chinese)
ZHANG Y, LI M Z, ZHENG L H, et al.. Soil nitrogen content forecasting based on real-time NIR spectroscopy[J]. Comput. Electron. Agricult., 2016, 124(C):29-36.
NAWAR S, MOUAZEN A M. Predictive performance of mobile Vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques[J]. Catena, 2017, 151:118-129.
王儒敬, 陈天娇, 汪玉冰, 等. 基于深度稀疏学习的土壤近红外光谱分析预测模型[J]. 发光学报, 2017, 38(1):109-116. WANG R J, CHEN T J, WANG Y B, et al.. Soil near-infrared spectroscopy prediction model based on deep sparse learning[J]. Chin. J. Lumin., 2017, 38(1):109-116. (in Chinese)
褚小立. 近红外光谱分析技术实用手册[M]. 北京:机械工业出版社, 2016. CHU X L. Practical Handbook for Near Infrared Spectroscopy[M]. Beijing:China Machine Press, 2016. (in Chinese)
NAWAR S, BUDDENBAUM H, HILL J, et al.. Estimating the soil clay content and organic matter by means of different calibration methods of Vis-NIR diffuse reflectance spectroscopy[J]. Soil Tillage Res., 2016, 155:510-522.
NAWAR S, MOUAZEN A M. Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line Vis-NIR spectroscopy measurements of soil total nitrogen and total carbon[J]. Sensors, 2017, 17(11):2428.
纪文君, 李曦, 李成学, 等. 基于全谱数据挖掘技术的土壤有机质高光谱预测建模研究[J]. 光谱学与光谱分析, 2012, 32(9):2393-2398. JI W J, LI X, LI C X, et al.. Using different data mining algorithms to predict soil organic matter based on visible-near infrared spectroscopy[J]. Spectros. Spect. Anal., 2012, 32(9):2393-2398. (in Chinese)
陈颂超, 冯来磊, 李硕, 等. 基于局部加权回归的土壤全氮含量可见-近红外光谱反演[J]. 土壤学报, 2015, 52(2):312-320. CHEN S C, FENG L L, LI S, et al.. Vis-NIR spectral inversion for prediction of soil total nitrogen content in laboratory based on locally weighted regression[J]. Acta Pedolog. Sinica, 2015, 52(2):312-320. (in Chinese)
HINTON G E, SALAKHUTDINOV R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
IAN G, YOSHUA B, AARON C. Deep Learning[M]. Cambridge:MIT Press, 2016.
STEVENS A, NOCITA M, TOTH G, et al.. Prediction of soil organic carbon at the European scale by visible and near infrared reflectance spectroscopy[J]. Plos One, 2013, 8(6):e66409.
ORGIAZZI A, BALLABIO C, PANAGOS P, et al.. LUCAS soil, the largest expandable soil dataset for Europe:a review[J]. Eur. J. Soil Sci., 2017, doi:10.1111/ejss.12499.
ALEX K, ILYA S, GEOFFREY E H. Image net classification with deep convolutional neural networks[J]. Adv. Neur. Inform. Proc. Syst. 25, 2012:1097-1105.
HE K M, ZHANG X Y, REN S Q, et al.. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016:770-778.
SZEGEDY C, IOFFE S, VANHOUCKE V, et al.. Inception-v4, Inception-ResNet and the impact of residual connections on learning[C]. Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, 2017:4278-4284.
0
浏览量
72
下载量
4
CSCD
关联资源
相关文章
相关作者
相关机构