Miscanthus AI- Plant selection and breeding for Net Zero

芒草 AI - 净零植物选择和育种

• 批准号: EP/Y005430/1
• 负责人: John Doonan
• 金额: $ 64.09万
• 依托单位: Aberystwyth University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY005430%2F1
• 关键词: Miscanthus; AI; Plant; selection; breeding

Human selection of crop plants for particular purposes such as food, fibre and fuel has already transformed our world, substantially relieving global hunger during the 20th century but arguably at very significant cost to the global environment from the negative impact of energy inputs and CO2 release from the soil due to changes in agricultural land use. However, intelligent and rapid exploitation of plant diversity, either as crops within intensively managed agriculture or as components of more natural ecosystems, holds promise in addressing NetZero. Breeding and technology play a major role, minimising inputs such as fertilisers while reducing handling costs in both food and biofuel crops. Genetic changes, whose performance benefits accumulate exponentially across time, represent an excellent investment. Predictive genetic x environment interaction modelling, based on multiple sources of spatio-temporal data (genomics combined with phenomics and environmental information across time and space) holds great promise for accelerated breeding in biofuel crops, many of which have not historically been subjected to selection and breeding. State-of-the-art plant breeding now interrogates vast quantities of data to understand how the plant genome leads to specific phenotypes or crop traits. However, the application of advanced artificial intelligence to this domain has been relatively unexplored. This project aims to integrate a suite of AI technologies across the plant breeding system, using Miscanthus as the key use case. We will explore novel machine learning models originating from science discovery within Chemistry to improve genetic prediction and selection for core traits associated with biomass accumulation. These models will be trained with new longitudinal data sets acquired from both the high-throughput phenotyping centre at BBSRC-IBERS (single plants) and with field robots at Lincoln (whole crops). In addition, we will explore how artificial intelligence can augment the decision-making of human plant breeders within the system. Our approach will focus on novel use of computational argumentation to provide an AI-trained logic framework that facilitates explanation-based decision support. This approach has the capacity to not only acquire knowledge over time but also explain decisions to human operators, producing a robotic plant breeder. Our approach bridges the gap between modern genomic selection and human plant breeders.Data analysis and interpretation by humans and/or autonomous actors is now a bottleneck to exploitation and science discovery. In this project, we will bring together computer scientists, geneticists, and engineers to create an AI-facilitated data analysis pipeline that can rapidly assess, predict and explain plant performance. The outputs will provide a pipeline to accelerate selection of biofuel crops with high yields that are climate resilient and minimise environmental impact.

人类为特定目的（如食物、纤维和燃料）选择农作物已经改变了我们的世界，大大缓解了世纪的全球饥饿，但可以说，由于农业土地使用的变化，能源投入和土壤中的二氧化碳释放对全球环境产生了非常重大的负面影响。然而，智能和快速地利用植物多样性，无论是作为集中管理的农业中的作物，还是作为更自然的生态系统的组成部分，都有望解决净零问题。育种和技术发挥了重要作用，最大限度地减少了化肥等投入，同时降低了粮食和生物燃料作物的处理成本。基因改变，其性能效益随着时间的推移呈指数级积累，是一项出色的投资。预测性遗传x环境相互作用模型，基于多个时空数据来源（基因组学结合表型组学和跨时间和空间的环境信息），为生物燃料作物的加速育种提供了巨大的希望，其中许多作物在历史上没有经过选择和育种。最先进的植物育种现在询问大量的数据，以了解植物基因组如何导致特定的表型或作物性状。然而，先进的人工智能在这一领域的应用相对较少。该项目旨在将一套人工智能技术集成到整个植物育种系统中，以芒草为主要用例。我们将探索源于化学领域科学发现的新型机器学习模型，以改善与生物量积累相关的核心性状的遗传预测和选择。这些模型将使用从BBSRC-IBERS的高通量表型中心（单株植物）和林肯的田间机器人（整株作物）获得的新的纵向数据集进行训练。此外，我们将探索人工智能如何增强系统内人类植物育种者的决策。我们的方法将专注于计算论证的新用途，以提供一个人工智能训练的逻辑框架，促进基于推理的决策支持。这种方法不仅能够随着时间的推移获得知识，而且还能够向人类操作员解释决策，从而产生机器人植物育种器。我们的方法弥合了现代基因组选择和人类植物育种者之间的差距。人类和/或自主行为者的数据分析和解释现在是开发和科学发现的瓶颈。在这个项目中，我们将汇集计算机科学家、遗传学家和工程师，创建一个人工智能辅助的数据分析管道，可以快速评估、预测和解释植物性能。这些产出将提供一个管道，以加速选择具有高产量的生物燃料作物，这些作物具有气候适应能力，并将环境影响降至最低。