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

机器学习作为政府产业战略：重大挑战政策文件（2017年）中人工智能的一部分被强调为优先事项。深度机器学习（一种允许计算机在没有明确编程的情况下从大型数据集快速学习的人工智能），特别是卷积神经网络（cnn），近年来也同样彻底改变了计算机视觉，提供了能够从复杂图像中进行高级模式识别的工具。cnn成功的关键在于他们能够同时学习如何识别关键图像特征，并说明它们在整个模式中的重要性。CNN的一个关键特征是能够识别CNN学会使用的图像中的形态学特征来生成所需的网络。类似地，x射线成像在土壤和农业科学中的应用已经彻底改变了我们以3D方式探索土壤结构的能力，因为它存在于该领域。孔隙连通性等重要特性可以从图像中获得，并用于解释关键的土壤功能，如保水（即干旱）和水力传导（即洪水风险）。目前阻碍进步的一个因素是，收集的大量数据往往超过了本地计算能力，因此，通常只有一小部分可用数据被利用。与先正达（世界上最大的农用化学品公司之一）合作，我们将使用从x射线图像开发的cnn来显示土壤结构对农用化学品在土壤中的运输的影响。这将使我们能够告知管理实践，可以用来确保更多的化肥和农药保留在土壤中（目前75%的化肥和农药流失到环境中，每年花费10亿英镑）。对英国公司和公民来说，更有针对性地在土壤中使用农用化学品的好处包括更清洁的水，更少的用于环境修复的资金，以及改善土壤健康，从而提高作物产量。

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.