Using Artificial Intelligence to Promote Food Security

利用人工智能促进粮食安全

• 批准号: 2856881
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2856881
• 关键词: Using; Artificial; Intelligence; Promote; Food

Maintaining food security, with growing populations and changing climates, is one of the most important challenges facing humanity. In agriculture, this requires us to increase crop yields whilst reducing chemical inputs. Precision agriculture is one of the most promising and exciting approaches to address this, allowing targeted application of chemicals to minimise environmental impacts. This PhD project applies cutting edge AI approaches to understand plant nutrition and stress, using a novel sensor technology developed in Manchester - Sinusoidally Modulated Fluorescence Imaging (SMFI). SMFI generates big datasets (T-bytes) for every plant within a study comprised of a series of complex (phasors) harmonic parameters for every pixel at every fluorescence excitation period within an imaged crop sample. The student will test the hypothesis that SMFI can be data-driven to identify early impacts of environmental stress, allowing it to be applied to problems in agriculture. The initial exemplar system will be based on winter wheat species and abiotic stresses associated with nutrient deficiencies. The first aim is to deliver Artificial Intelligence (AI) or Machine Learning (ML) approaches that quantitatively relate SMFI imaging to early-stage nutrient deficiency in plants. A second aim is to link that information to the underlying photosynthesis, and primary and secondary metabolism within plants. This project has both practical and theoretical aspects. The student will design and execute a series of controlled plant stress trials, using prototype SMFI sensor and equipment developed in on-going research programmes. The student will work alongside biologists, gaining practical skills in plant physiology. Data collection will be performed iteratively for modelling. The student will research and deliver new model-based AI methodologies that can accommodate and deconvolute interacting factors as well as to manipulate the modulation of actinic (growth) light in a closed-loop manner, to maximise the information content of the data-arising. AI/ML methods are becoming an appealing alternative to solving many physical modelling or data driven inverse problems. Deep neural networks (DNNs) constructure a complex relationship using a large number of simple functions arranged in multiple scales or levels, further coupled with supressing or subsampling to achieve flexibility and stability. Generative adversarial networks (GANs) are an adaptive framework for learning universal functionals. Injecting biological models into deep learning will make deep learning more focused and stable. This project will explore the best ways to integrate biological mechanisms in plant growth such as photosynthesis, minerals, nutrients and water, into DNNs for modelling their complex growth patterns and functions. Temporal models or mechanisms will also be integrated to the modelling networks via constraints or adding temporal features. Controlled trials will be conducted for data collection, model development and verification. The predictive and diagnostic power of modelling based on laboratory studies will be tested under greenhouse and then field conditions.This research will bridge the gaps of information science, applied biology and sensor systems research. Through utilising knowledge of techniques and approaches from across all three disciplines it is anticipated that the student will develop a 'common language' allowing them to interact with and influence PhD and Early Career Researchers across the UoM community in those disciplines.

随着人口增长和气候变化，维持粮食安全是人类面临的最重要挑战之一。在农业中，这要求我们提高作物产量，同时减少化学品投入。精准农业是解决这一问题最有前途和最令人兴奋的方法之一，它允许有针对性地应用化学品，以尽量减少对环境的影响。该博士项目利用曼彻斯特开发的新型传感器技术——正弦调制荧光成像（SMFI），应用最先进的人工智能方法来了解植物营养和压力。 SMFI 为研究中的每种植物生成大数据集（T 字节），其中包含成像作物样本中每个荧光激发周期每个像素的一系列复杂（相量）谐波参数。学生将测试 SMFI 可以通过数据驱动来识别环境压力的早期影响的假设，从而将其应用于农业问题。最初的示范系统将基于冬小麦品种和与营养缺乏相关的非生物胁迫。第一个目标是提供人工智能 (AI) 或机器学习 (ML) 方法，将 SMFI 成像与植物早期营养缺乏定量关联。第二个目标是将这些信息与植物内潜在的光合作用以及初级和次级代谢联系起来。该项目具有实践和理论两个方面。学生将使用原型 SMFI 传感器和正在进行的研究项目中开发的设备，设计并执行一系列受控植物胁迫试验。学生将与生物学家一起工作，获得植物生理学的实用技能。将迭代执行数据收集以进行建模。学生将研究和交付基于模型的新人工智能方法，该方法可以适应和解卷积相互作用的因素，并以闭环方式操纵光化（生长）光的调制，以最大化所产生的数据的信息内容。 AI/ML 方法正在成为解决许多物理建模或数据驱动的逆问题的有吸引力的替代方案。深度神经网络（DNN）使用大量以多个尺度或级别排列的简单函数来构建复杂的关系，并进一步结合抑制或子采样以实现灵活性和稳定性。生成对抗网络（GAN）是一种用于学习通用泛函的自适应框架。将生物模型注入深度学习，将使深度学习更加专注和稳定。该项目将探索将植物生长中的生物机制（如光合作用、矿物质、营养物质和水）整合到 DNN 中的最佳方法，以对其复杂的生长模式和功能进行建模。 Temporal models or mechanisms will also be integrated to the modelling networks via constraints or adding temporal features.将进行数据收集、模型开发和验证的对照试验。基于实验室研究的建模的预测和诊断能力将在温室和现场条件下进行测试。这项研究将弥补信息科学、应用生物学和传感器系统研究的空白。通过利用所有三个学科的技术和方法知识，预计学生将发展出一种“共同语言”，使他们能够与密歇根大学社区中这些学科的博士和早期职业研究人员互动并影响他们。