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Predicting soil properties using machine learning and mobile phone Khan, A., Jorat, E. J., Aitkenhead, M.J.1, Stark, C. 1The James Hutton Institute, Craigiebuckler, Aberdeen AB158QH Email: a.khan2000@uad.ac.uk
Introduction: The traditional ways of soil properties measurement are time, money, and labor-consuming. The technological advancement of decades giving quality images from mobile cameras, accurate Global Positioning System (GPS), exponential growth in machine learning algorithms, and computer vision have now made it possible to provide an alternative for the field measurement of soil health and quality indicators. Georeferenced point data representing field and/or laboratory measurements.
Figure 3: Sample distribution across the Finzean Estate boundary(a) Population sample (17932) (b) Random sample (50) (c) CLHS sample (50).
Terrain attributes Solar radiation Slope gradient, Mean rainfall slope, curvature, Uncertainty or error Mean temperature etc Age (Human impact)
S = f ( S, C, O, R, P, A, N) + (McBratney et al, 2003)
Parent material Either Soil Class (geology map) or soil property Organisms (vegetation or land cover digital data)
Location (x,y)
Figure 1: SCORPAN is a quantitative empirical function that estimates either soil class or soil property at a given point in space and time using 7 environment covariates: soil (s), climate (c), organisms (o), relief (r), parent material (p), age (a), and spatial location (n).
Figure 4: Density distribution plot of environmental covariates for different sampling techniques. It describes the graphical distribution curve for each sampling technique for all the continuous variables.
Case Study: Different sampling strategies.
The framework of the Project As a part of the Ph.D. studies, a sampling site of Finzean Soil Samples and observations
Estate with an area of 44.8 km2 is being studied, situated in the eastern half of the Finzean and lies in the territory of Birse Aberdeenshire, Scotland. The estate has a diverse landscape with various land cover, land capability, and soil classes shown in the figure below.
Topsoil photography (Color)
SCORPAN FEATURES
TOPOGRAPHIC Elevation Curvature Slope Bottom valley Aspect SOIL MAPS Land Cover Geological class
Laboratory measurements
Processing and feature selection OVERLAY
• • • • • •
Regression Decision Tree Random Forest Neural Networks ANN
Fit or Train a machine learning algorithm
Predict property at desired location and select the best model
Figure 2: Land, soil, and geology distribution in Finzean estate References: McBratney, A.B., Mendonc-a-Santos, M.L., Minasny, B., (2003). On digital soil mapping. Geoderma 117, 3–52. Minasny, B., & McBratney, A. B. (2006). A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences, 32(9), 1378 Aitkenhead, M., Coull, M., Gwatkin, R., & Donnelly, D. (2016a). Automated soil physical parameter assessment using smartphone and digital camera imagery. Journal of Imaging, 2(4).
Model Parameters
Real time testing
Integration of model into mobile application
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