Explainable AI for UK agricultural land use decision-making

英国农业土地使用决策的可解释人工智能

• 批准号: NE/T004002/1
• 负责人: Christopher Nemeth
• 金额: $ 5.5万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT004002%2F1
• 关键词: Explainable; AI; UK; agricultural; land

Agricultural land use dynamics and their associated driving factors represent highly complex systems of flows that are subject to non-linearities, sensitivities, and uncertainties across spatial and temporal scales. They are therefore challenging to represent using traditional statistical modelling approaches. Existing process-based modelling has enabled advances in understanding of individual biophysical processes underpinning agricultural land use systems (e.g. crop, livestock and biogeochemical models). However, these tend to focus on individual processes in detail or link a limited number of processes at large scales, thereby mostly ignoring the complex interdependencies between the multiple interacting biophysical and socio-economic components of land use systems. Artificial intelligence (AI) techniques offer great potential to complement such modelling approaches by mining the deep knowledge (e.g. farming patterns and behaviours) encapsulated in 'big' data from ground-based sensors (such as frequently used for precision farming) and Earth Observation satellites. This will deliver enhanced insight on the past and current state and spatio-temporal dynamics of agricultural land use system flows and how they can be influenced by decisions on agricultural policies and related farm management practices. Our proposal aims to develop a novel explainable AI framework that is transparent, data-driven and spatially-explicit by using probabilistic inference and explicit "if-then" rules. We will demonstrate proof-of-concept for two pilot regions of the UK (Oxfordshire and Lincolnshire), and the framework will be set up in a way that can be readily expanded to the whole UK. Specifically, we will draw on time-series of agricultural land use and production datasets (in-kind support from industry project partner SOYL) to identify the key socio-economic and environmental driving factors that have led to historic agricultural land use changes in the pilot regions. We will then establish explainable AI-rules for the characterisation of these agricultural land use changes and refine them within the framework through machine learning and parameter optimisation.We will demonstrate and test the potential of the explainable AI framework for providing a new and robust method for predicting changing patterns of agricultural land use in the two pilot regions. This will include testing the ability of the AI framework for improving understanding of past and present agricultural land use dynamics across multiple temporal and spatial scales from 'big' data. It will also assess the potential for continually updating the predictions of land use dynamics in real-time using data from sensors. This could provide early warning when certain driving conditions are triggered or used to repeatedly refine short-term projections of land use change and their estimates of uncertainty.

农业土地利用动态及其相关驱动因素代表了高度复杂的流量系统，在空间和时间尺度上受到非线性、敏感性和不确定性的影响。因此，使用传统的统计建模方法来表示它们具有挑战性。现有的基于过程的建模促进了对支撑农业土地利用系统（例如作物、牲畜和生物地球化学模型）的个体生物物理过程的理解。然而，这些往往侧重于详细的单个过程或在大范围内链接有限数量的过程，从而大多忽略了土地利用系统的多个相互作用的生物物理和社会经济组成部分之间复杂的相互依赖性。人工智能（AI）技术通过挖掘来自地面传感器（例如经常用于精准农业）和地球观测卫星的“大”数据中封装的深层知识（例如农业模式和行为），为补充此类建模方法提供了巨大的潜力。这将增强人们对农业土地利用系统流动的过去和现在的状态和时空动态以及它们如何受到农业政策和相关农场管理实践决策的影响的深入了解。我们的提案旨在通过使用概率推理和明确的“如果-那么”规则来开发一种新颖的可解释的人工智能框架，该框架是透明的、数据驱动的和空间明确的。我们将为英国的两个试点地区（牛津郡和林肯郡）展示概念验证，并且该框架将以可以轻松扩展到整个英国的方式建立。具体来说，我们将利用农业土地利用和生产数据集的时间序列（来自行业项目合作伙伴SOYL的实物支持）来确定导致试点地区历史性农业土地利用变化的关键社会经济和环境驱动因素。然后，我们将建立可解释的人工智能规则来描述这些农业土地利用变化的特征，并通过机器学习和参数优化在框架内完善它们。我们将展示和测试可解释的人工智能框架的潜力，为预测两个试点地区农业土地利用变化模式提供一种新的、稳健的方法。这将包括测试人工智能框架的能力，以提高对“大”数据跨多个时间和空间尺度的过去和现在农业土地利用动态的理解。它还将评估利用传感器数据实时不断更新土地利用动态预测的潜力。当某些驾驶条件被触发或用于反复完善土地利用变化的短期预测及其不确定性估计时，这可以提供早期预警。

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• 发表时间: 2023
• 期刊: SUSTAINABILITY SCIENCE
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• DOI: 10.1016/j.isprsjprs.2023.11.016
• 发表时间: 2023-12
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• 影响因子: 12.7
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