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

世界人口的快速增长和气候变化对可持续粮食生产提出了挑战。长期以来，农业作物生产一直依赖于基于过程的模型（PBMs）来预测产量，并了解植物生理过程如何与环境相互作用，从而影响作物的生长发育。然而，由于复杂的天气/植物相互作用，PBMs在做出准确预测方面受到限制。这对于极端事件（干旱、热浪）、害虫、疾病和没有考虑在内的压力来说尤其如此。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，超过了观测到的产量随时间/空间的变化。机器学习（ML）通过从数据中学习来快速预测作物产量，但它通常是一个需要解释的黑盒子。整合pbm和ML在改善预测方面显示出了希望。有效集成的挑战仍然存在：为准确的模拟选择正确的ML，平衡可解释性和不确定性。环境影响评价往往被忽视。在我们现有的基础上，这一合作伙伴关系汇集了农业环境科学、德国作物建模和英国计算机科学（大数据/机器学习/人工智能）领域的领先研究人员，旨在通过协同基于过程的模型和机器学习模型，开发一个创新的人工智能框架，以实现准确和可解释的作物产量预测，并结合环境影响评估。总体目标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，以解决呼叫主题-可持续农业和食品中的人工智能，并为我们正在进行的气候智能型农业解决方案研究提供附加值。为此，我们将进行一系列研究活动，包括可行性研究、员工交流/早期职业研究人员访问、设施和数据访问、研讨会，以及联合出版物/资助申请。人工智能与农业建模的整合代表了一种推动农业研究边界的新兴范式。它不仅提供了更好的作物产量预测和减轻气候变化影响，而且为了解作物动态、资源优化和可持续农业实践开辟了新的途径。拟议的方法有可能应用于不同的规模，从个别农田到区域和全球一级。这种可扩展性和通用性使人工智能驱动的协同作用适用于解决复杂的农业挑战和适应不同的环境条件。它有能力彻底改变农业，使粮食生产系统更加高效、可持续和有抵御力。这项研究为农民、消费者、政策制定者和环境提供了潜在的好处。改进预测将加强农业决策，增加粮食安全，促进气候变化适应和减缓，并优化资源利用。此外，这项研究将促进科学知识的发展，并使工业和学术机构受益。

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.