title: Soil type recognition as improved by genetic algorithm-based variable selection using near infrared spectroscopy and partial least squares discriminant analysis.
abstract: 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.
authors.affiliation: 1] State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China [2] Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China.
authors.forename: Hongtu
authors.initials: H
authors.lastname: Xie
authors.affiliation: College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China.
authors.forename: Jinsong
authors.initials: J
authors.lastname: Zhao
authors.affiliation: College of Land &Environment, Shenyang Agricultural University, Shenyang 110866, China.
authors.forename: Qiubing
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Key Laboratory of Mollisols Agroecology, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Haerbin 150081, China.
authors.forename: Yueyu
authors.initials: Y
authors.lastname: Sui
authors.affiliation: College of Land &Environment, Shenyang Agricultural University, Shenyang 110866, China.
authors.forename: Jingkuan
authors.initials: J
authors.lastname: Wang
authors.affiliation: Greenhouse and Processing Crops Research Centre, Agriculture &Agri-Food Canada, Harrow, Ontario N0R 1G0, Canada.
authors.forename: Xueming
authors.initials: X
authors.lastname: Yang
authors.affiliation: 1] State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China [2] National Field Research Station of Shenyang Agroecosystems, Shenyang 110016, China.
authors.forename: Xudong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: 1] State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China [2] Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
authors.forename: Chao
authors.initials: C
authors.lastname: Liang