Synergising Process-Based and Machine Learning Models for Accurate and Explainable Crop Yield Prediction along with Environmental Impact Assessment

协同基于流程和机器学习模型，实现准确且可解释的作物产量预测以及环境影响评估

• 批准号: BB/Y513763/1
• 负责人: Liangxiu Han
• 金额: $ 31.02万
• 依托单位: Manchester Metropolitan University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY513763%2F1
• 关键词: Synergising; Process; Based; Machine; Learning; Synergising; Process; Based; Machine; Learning

The world's rapid population growth and climate change pose challenges to sustainable food production. Agricultural crop production has long relied on Process-based models (PBMs) to forecast yields and understand how plant physiological processes interact with the environment, influencing crop growth and development. However, the PBMs suffer limitations in making accurate predictions due to complex weather/plant interactions. This is especially true for extreme events (drought, heat waves), pests, diseases, and stresses not accounted for. Process-based models' predictive abilities are hindered by uncertainties in structure, inputs, and parameters, exceeding observed yield variations over time/space.Machine Learning (ML) offers quick crop yield prediction by learning from data, but it's often a black box needing explanations. Integrating PBMs and ML has shown promise in improving predictions. Challenges remain in effective integration: choosing the right ML for accurate simulation, balancing interpretability and uncertainty. Environmental impact assessment is often overlooked.Building on our existing foundations, this partnership brings together leading researchers in agri-environment sciences, crop modelling from Germany and computer science (big data/machine learning/AI) from UK, and aims to develop an innovative AI framework by synergising process-based and machine learning models for accurate and explainable crop yield prediction coupling with environmental impact assessment. The overarching aim is to build and foster a long-term partnership between UK and Germany's top research groups to address the call theme- AI in sustainable agriculture and food and provides the added value to our ongoing research in climate-smart agriculture solutions. To achieve this, we will conduct a series of research activities including feasibility study, staff exchanges/early career researchers (ECRs) visits, facility and data access, workshops, and joint publications/funding applications. The integration of AI with agricultural modeling represents an emerging paradigm that pushes the boundaries of agricultural research. It not only offers improved crop yield predictions and climate change impact mitigation but also opens up new avenues for understanding crop dynamics, resource optimization, and sustainable farming practices. The proposed approach has the potential to be applied at different scales, ranging from individual farm fields to regional and global levels. This scalability and generalization make the AI-driven synergy suitable for addressing complex agricultural challenges and adapting to diverse environmental conditions. It has the capacity to revolutionize agriculture, leading to more efficient, sustainable, and resilient food production systems.This research offers potential benefits to farmers, consumers, policymakers, and the environment. Improved predictions will enhance agricultural decision-making, increase food security, promote climate change adaptation and mitigation, and optimize resource utilization. Additionally, the research will advance scientific knowledge and benefit industry and academic institutions.

世界快速的人口增长和气候变化对可持续粮食生产构成了挑战。农作物的生产长期以来一直依赖于基于过程的模型（PBM）来预测产量，并了解植物生理过程如何与环境相互作用，从而影响农作物的生长和发展。但是，由于天气复杂/植物相互作用，PBM在做出准确的预测时遭受了限制。对于极端事件（干旱，热浪），害虫，疾病和不考虑的压力尤其如此。基于过程的模型的预测能力受到结构，输入和参数的不确定性的阻碍，超过了随时间/空间的观察到的收益率变化。机器学习（ML）通过从数据中学习来提供快速的作物产量预测，但通常是一个黑匣子，需要说明。集成PBM和ML在改善预测方面已显示出希望。挑战仍然存在于有效的整合中：选择正确的ML以进行准确的模拟，平衡解释性和不确定性。环境影响评估经常被忽视。建立我们现有的基础，这种伙伴关系融合了农业环境科学领域的领先研究人员，来自英国的作物模型（大数据/机器学习/AI），旨在通过对基于基于过程和机器的机器学习模型进行创新的AI框架，以对环境的共同构图进行创新的AI框架。总体目的是建立和促进英国和德国顶级研究小组之间的长期合作伙伴关系，以解决可持续农业和食品中的呼叫主题AI，并为我们在气候智能农业解决方案中正在进行的研究带来额外的价值。为了实现这一目标，我们将进行一系列研究活动，包括可行性研究，员工交流/早期职业研究人员（ECRS）访问，设施和数据访问，研讨会以及联合出版物/资金申请。 AI与农业建模的整合代表了一种新兴的范式，它突破了农业研究的界限。它不仅提供了改进的作物收益预测和气候变化影响缓解的措施，而且还为了解农作物动态，资源优化和可持续的农业实践开辟了新的途径。所提出的方法有可能在不同的尺度上应用，从单个农场到区域和全球水平。这种可扩展性和概括使AI驱动的协同作用适合应对复杂的农业挑战并适应各种环境条件。它有能力彻底改变农业，从而导致更有效，可持续和韧性的粮食生产系统。这项研究为农民，消费者，决策者和环境提供了潜在的好处。改进的预测将增强农业决策，提高粮食安全，促进气候变化的适应和缓解，并优化资源利用。此外，该研究将推进科学知识和利益行业和学术机构。