Dr Kwok Chun Kwok.Chun@uwe.ac.uk
Lecturer in Environmental Managment
Abstract
Accurate identification of the black soil thickness from soil profiling is usually time-consuming and labor-intensive, while the on-site identification of black soil thickness by experts is challenging due to the notable transitional layer in the thick black soil profiles. This study proposes a framework for efficient identification of black soil thickness from soil profiling based on drill core imaging and deep learning. Without excavating a soil profile, drill core images from a carry-on soil sampler can be used to identify the black soil horizon using a trained deep learning model of the VGG-16 backbone U-net algorithm. The approach was tested with a limited dataset obtained from field sites in the black soils of northeast China and the results show that it can efficiently identify the black soil horizon on site. A good accuracy was obtained, with R2=0.95 and RMSE = 0.07 m for the estimates of black soil thickness. Overall, the proposed methodology offers the possibility of efficiently identifying black soil thickness on a large scale, thus accurately quantifying regional black soil degradation.
Journal Article Type
Article
Acceptance Date
Mar 5, 2025
Deposit Date
Mar 15, 2025
Print ISSN
0341-8162
Publisher
Elsevier
Peer Reviewed
Peer Reviewed
Keywords
Deep learning, AI, Climate action, Water, Food security, Soil
