TY - JOUR
T1 - Towards a dynamic soil survey
T2 - Identifying and delineating soil horizons in-situ using deep learning
AU - Jiang, Zhuo Dong
AU - Owens, Phillip R.
AU - Zhang, Chun Liang
AU - Brye, Kristofor R.
AU - Weindorf, David C.
AU - Adhikari, Kabindra
AU - Sun, Zhong Xiu
AU - Sun, Fu Jun
AU - Wang, Qiu Bing
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - Soil horizons are the manifestation of pedogenesis and contain the basic morphologic indicators of soil formation. Accurate and quantitative tools for delineating soil horizons in-situ can assist soil scientists towards rapid and dynamic soil survey information. The objective of this study was to develop a deep-learning-based, soil-profile-imaging method for identifying and delineating soil master horizons to assist in digital soil descriptions. A total of 160 soil profile images from four soil orders (Alfisols, Entisols, Inceptisols, and Mollisols) were collected from north China in the Inner Mongolia and Liaoning regions. The 160 profile images were amplified to 2400 individual images for model building using data-augmentation procedures. The augmented profile image dataset was divided into training (70%), validation (15%), and test (15%) datasets. The training and validation datasets were imported into a nested, U-net network for model building. The proposed deep-learning (DL) model classified soil profiles into A, B, and C horizons. The mean pixel accuracy of the DL model was 0.86 with the training dataset, 0.82 with the validation dataset, and 0.83 for the test dataset. Results showed that the DL model was judged to be accurate enough for identifying and delineating soil master horizons from profile images in practice. Based on the proposed DL model, a smartphone application was subsequently developed to digitalize the soil profile and assist field evaluation of soil profile horizonation. The smartphone application, for the Android operating system (version 5.0 and greater), featured a response time of < 5 s. This study demonstrated that the proposed DL model and smartphone application could be a simple, fast, and digital tool for quickly identifying and delineating in-situ soil horizons. These tools allow for rapid data collection, which can be used for future artificial intelligence development and application in soil science towards digital soil profile description and dynamic soil survey.
AB - Soil horizons are the manifestation of pedogenesis and contain the basic morphologic indicators of soil formation. Accurate and quantitative tools for delineating soil horizons in-situ can assist soil scientists towards rapid and dynamic soil survey information. The objective of this study was to develop a deep-learning-based, soil-profile-imaging method for identifying and delineating soil master horizons to assist in digital soil descriptions. A total of 160 soil profile images from four soil orders (Alfisols, Entisols, Inceptisols, and Mollisols) were collected from north China in the Inner Mongolia and Liaoning regions. The 160 profile images were amplified to 2400 individual images for model building using data-augmentation procedures. The augmented profile image dataset was divided into training (70%), validation (15%), and test (15%) datasets. The training and validation datasets were imported into a nested, U-net network for model building. The proposed deep-learning (DL) model classified soil profiles into A, B, and C horizons. The mean pixel accuracy of the DL model was 0.86 with the training dataset, 0.82 with the validation dataset, and 0.83 for the test dataset. Results showed that the DL model was judged to be accurate enough for identifying and delineating soil master horizons from profile images in practice. Based on the proposed DL model, a smartphone application was subsequently developed to digitalize the soil profile and assist field evaluation of soil profile horizonation. The smartphone application, for the Android operating system (version 5.0 and greater), featured a response time of < 5 s. This study demonstrated that the proposed DL model and smartphone application could be a simple, fast, and digital tool for quickly identifying and delineating in-situ soil horizons. These tools allow for rapid data collection, which can be used for future artificial intelligence development and application in soil science towards digital soil profile description and dynamic soil survey.
KW - Digital soil description
KW - Dynamic soil survey
KW - Profile imaging method
KW - Smartphone application
KW - Soil horizons
UR - http://www.scopus.com/inward/record.url?scp=85110376560&partnerID=8YFLogxK
U2 - 10.1016/j.geoderma.2021.115341
DO - 10.1016/j.geoderma.2021.115341
M3 - Article
AN - SCOPUS:85110376560
SN - 0016-7061
VL - 401
JO - Geoderma
JF - Geoderma
M1 - 115341
ER -