Andy Blunk

Andy Blunk

Jan 15, 2025

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pH results are beginning to roll in...

Part of the rationale for this project was that soils in the rural areas of Gambia are generally considered acidic, and to help farmers know about their soils, and make decisions it's important to both have a tool to analyze the soil, and for that information to be accessible. The first round of our data (from a week ago) already affirms the importance of soil pH management in this study region- the majority of farms are well below the optimal pH for groundnut production (the staple crop in the gambia). Through talks with these farmers, soil acidity management is not a part of their considerations, while fertilizer application is. These results show, that regardless of fertilizer application, crop yields are going to be limited due to soil acidity. It's a foundational issue, and we're excited to see if this study results in a usable tool!


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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.

Blast off!

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