Predicting soil properties using machine learning: a smartphone solution for stakeholders

Soil health is foundational to agricultural productivity, ecosystem sustainability, and climate regulation, yet traditional methods for assessing soil properties are labour-intensive, costly, and limited in spatial resolution. This thesis addresses these limitations by integrating machine learning and Digital Soil Mapping (DSM) with smartphone-based imagery to enhance the accuracy, accessibility, and affordability of soil property assessment. Using strategically sampled field data from diverse Scottish environments, detailed laboratory analysis established a robust baseline dataset for key soil attributes, including pH, organic carbon, moisture content, bulk density, and texture.<br/><br/>Advanced machine learning algorithms were developed and optimised to predict soil properties across Scotland, significantly increasing spatial resolution and capturing micro-scale variability. Incorporation of smartphone-captured soil images analysed through Convolutional Neural Networks (CNNs) further improved prediction accuracy, demonstrating the substantial value of visual soil characteristics in modelling efforts. The suitability of CNN-derived features was rigorously tested against traditional features, both with and without calibration, confirming that CNN features provided superior performance in predictive accuracy. The resulting high-resolution soil property maps, with quantified uncertainty intervals, represent a transformative improvement over conventional soil maps, enabling targeted agricultural and conservation strategies.<br/><br/>A user-friendly smartphone application was created to operationalise these advancements, empowering farmers, policymakers, and land managers with instant, site-specific soil information. Users can obtain site-specific soil property predictions by simply capturing a geotagged soil image. In cross validated tests, our best models achieved R² of 0.64 for bulk density, 0.61 for moisture content, 0.39 for pH, and 0.50 for LOI. <br/><br/>This research advances the science of digital soil mapping and directly contributes to sustainable land management practices, precision agriculture, and environmental conservation, offering a replicable and scalable framework suitable for global adoption in diverse geographical settings.

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

Abertay University

Year dc:date.issued

2025

Author dc:creator

• Khan, Adnan

Advisors dc:contributor.advisor

• Jorat, Mohammadehsan
• Howson, Thomas

• Digital soil mapping
• Convolutional neural network
• Machine learning
• Smartphone
• Soil health

Language dc:language

eng

Identifier

oai:rke.abertay.ac.uk:studenttheses/24eb5811-3be1-44b0-8c53-4688590850cc

OAI identifier oai:identifier

oai:rke.abertay.ac.uk:studenttheses/24eb5811-3be1-44b0-8c53-4688590850cc