Soil type recognition as improved by genetic algorithm-based variable selection using near infrared spectroscopy and partial least squares discriminant analysis
文献类型：期刊论文
| 作者 | Liang, Chao2,8; Xie, Hongtu2,3; Zhang, Xudong1,2; Zhao, Jinsong4; Wang, Qiubing5; Sui, Yueyu6; Wang, Jingkuan5; Yang, Xueming7 |
| 刊名 | SCIENTIFIC REPORTS
 |
| 出版日期 | 2015-06-18 |
| 卷号 | 5 |
| ISSN号 | 2045-2322 |
| DOI | 10.1038/srep10930 |
| 英文摘要 | Soil types have traditionally been determined by soil physical and chemical properties, diagnostic horizons and pedogenic processes based on a given classification system. This is a laborious and time consuming process. Near infrared (NIR) spectroscopy can comprehensively characterize soil properties, and may provide a viable alternative method for soil type recognition. Here, we presented a partial least squares discriminant analysis (PLSDA) method based on the NIR spectra for the accurate recognition of the types of 230 soil samples collected from farmland topsoils (0-10 cm), representing 5 different soil classes (Albic Luvisols, Haplic Luvisols, Chernozems, Eutric Cambisols and Phaeozems) in northeast China. We found that the PLSDA had an internal validation accuracy of 89% and external validation accuracy of 83% on average, while variable selection with the genetic algorithm (GA and GA-PLSDA) improved this to 92% and 93%. Our results indicate that the GA variable selection technique can significantly improve the accuracy rate of soil type recognition using NIR spectroscopy, suggesting that the proposed methodology is a promising alternative for recognizing soil types using NIR spectroscopy. |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:000356525100001 |
| 出版者 | NATURE PUBLISHING GROUP |
| 源URL | [http://210.72.129.5/handle/321005/122073]  |
| 专题 | 中国科学院沈阳应用生态研究所 |
| 通讯作者 | Liang, Chao |
| 作者单位 | 1.Natl Field Res Stn Shenyang Agroecosyst, Shenyang 110016, Peoples R China 2.Chinese Acad Sci, Inst Appl Ecol, State Key Lab Forest & Soil Ecol, Shenyang 110164, Peoples R China 3.Chinese Acad Sci, Inst Appl Ecol, Key Lab Pollut Ecol & Environm Engn, Shenyang 110164, Peoples R China 4.Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China 5.Shenyang Agr Univ, Coll Land & Environm, Shenyang 110866, Peoples R China 6.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Mollisols Agroecol, Haerbin 150081, Peoples R China 7.Agr & Agri Food Canada, Greenhouse & Proc Crops Res Ctr, Harrow, ON N0R 1G0, Canada 8.Univ Illinois, Inst Genom Biol, Urbana, IL 61801 USA |
| 推荐引用方式 GB/T 7714 | Liang, Chao,Xie, Hongtu,Zhang, Xudong,et al. Soil type recognition as improved by genetic algorithm-based variable selection using near infrared spectroscopy and partial least squares discriminant analysis[J]. SCIENTIFIC REPORTS,2015,5. |
| APA | Liang, Chao.,Xie, Hongtu.,Zhang, Xudong.,Zhao, Jinsong.,Wang, Qiubing.,...&Yang, Xueming.(2015).Soil type recognition as improved by genetic algorithm-based variable selection using near infrared spectroscopy and partial least squares discriminant analysis.SCIENTIFIC REPORTS,5. |
| MLA | Liang, Chao,et al."Soil type recognition as improved by genetic algorithm-based variable selection using near infrared spectroscopy and partial least squares discriminant analysis".SCIENTIFIC REPORTS 5(2015). |
入库方式： OAI收割
来源：沈阳应用生态研究所
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