A machine learning approach to predicting agro-chemical transport in soil using X-ray imaging

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Machine Learning is highlighted as a priority as part of Artificial Intelligence in Government's Industrial Strategy: Grand Challenges Policy Paper (2017). Deep machine learning (a type of AI that allows computers to learn rapidly from large datasets without being explicitly programmed), and convolutional neural networks (CNNs) in particular, have similarly revolutionised computer vision in recent years, providing tools capable of high level pattern recognition from complex images. The key to CNNs success is their ability to simultaneously learn both how to identify key image features and account for their importance within the pattern as a whole. A key feature of CNNs is the ability to identify what morphological features within an image the CNN learned to use to generate the required network. In a similar way the application of X-ray imaging to soil and agricultural sciences has revolutionised our ability to explore soil structure in 3D, as it exists in the field. Important properties such as pore connectivity can be derived from images and used to account for crucial soil functions such as water retention (i.e. drought) and hydraulic conductivity (i.e. flood risk). A current impediment to progress is the vast amount of data collected often exceeds local computing capacity, and as such, it is common that only a small amount of the available data is exploited. Partnering with Syngenta (one of the World's largest agro-chemical companies) we will use CNNs developed from X-ray images to show soil structure influences transport of agrochemicals through soils. This will enable us to inform management practices that could be used to ensure more fertiliser and pesticide is retained in soil (>75% is currently lost to the environment costing >£1B/year). The benefits to UK plc and citizens from a more targeted application of agro-chemicals to soil would include cleaner water, less £ spent on environmental remediation and improved soil health leading to higher crop yields.