About This Project

This project aims to democratize access to soil analysis by developing a machine-learning model to predict soil pH from photos, accessible to anyone with a smartphone. To conduct the study soil samples and images will be collected. Lab analysis data will then be used to train a machine-learning model. This will enable easy, quick, and inexpensive soil data collection, empowering anyone with a smartphone to study soil pH; especially valuable in regions lacking laboratory facilities.

Ask the Scientists

What is the context of this research?

This project emerges from the need for accessible and affordable soil analysis, crucial for effective validation and research on soil conservation practices. Traditional soil testing methods are often costly and require laboratory access, a barrier for many resource-limited settings, such as in West Africa-the location for this proposed project. By leveraging the ubiquity of smartphones and the power of machine learning (ML), this initiative aims to enable anyone with a smartphone to conduct soil pH studies. Studies have shown that with context-specific models, ML can reliably make accurate pH predictions. A model has yet to be investigated for development and application in West African soils.

What is the significance of this project?

Approximately 2.5 billion hectares of arable land (40–50% of the world's potentially arable land) are degraded (low soil pH and low organic matter) and much of this land is in regions that don't have access to soil testing facilities. At the same time, some local practices and amendments can be used to remediate these issues. By having a cost-effective, simple way of analyzing soil, farmers/land managers can design and conduct their own studies to improve these soil parameters. This approach not only democratizes scientific tools but also fosters a deeper understanding of soil health, crucial for sustainable agriculture and environmental stewardship. It represents a step forward in frugal science, promoting innovation and problem-solving in diverse contexts.

What are the goals of the project?

The fundamental goal of this project is to develop a reliable ML model that can accurately predict soil pH from smartphone images in West African Soils. This ML model will function as the foundation for a soil pH analysis app that can be used by anyone with a smartphone. Across the 2024 season, we plan to follow protocols developed by Gorthi et al., and Barman, - collect ~2000 soil samples for image data and pH data; for image data- dry samples, take pictures with the 3 most common smartphone cameras- and pH data - collect through verified handheld sensor analysis. Using image and lab data, train ML model with novel Tree-based Pipeline Optimization tool (TPOT), to create ANN regression to predict pH.