Miscanthus AI- Plant selection and breeding for Net Zero

人类为食物、纤维和燃料等特殊目的选择农作物已经改变了我们的世界，在20世纪极大地缓解了全球饥饿，但可以说，由于农业土地利用的变化，能源投入和土壤释放二氧化碳的负面影响对全球环境造成了非常大的代价。然而，对植物多样性的智能和快速开发，无论是作为集约化管理农业中的作物，还是作为更自然生态系统的组成部分，都有望解决NetZero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。基因变化是一项极好的投资，其性能收益会随着时间的推移呈指数级积累。基于多种时空数据来源(基因组学结合表型组学和跨越时间和空间的环境信息)的预测性遗传x环境交互作用模型为生物燃料作物的加速育种带来了巨大的希望，其中许多作物在历史上从未进行过选择和育种。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还相对未被探索。该项目旨在以芒属为关键用例，在植物育种系统中整合一套人工智能技术。我们将探索源于化学中的科学发现的新的机器学习模型，以改进与生物量积累相关的核心性状的遗传预测和选择。这些模型将用从BBSRC-IBERS(单株)的高通量表型中心和林肯(整株作物)的田间机器人获得的新的纵向数据集进行训练。此外，我们还将探索人工智能如何在系统内增强人类植物育种者的决策能力。我们的方法将专注于计算论证的新使用，以提供一个人工智能训练的逻辑框架，促进基于解释的决策支持。这种方法不仅能够随着时间的推移获得知识，还可以向人类操作员解释决策，产生一个机器人植物繁殖者。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/或自主行为者对数据的分析和解释现在是开发和科学发现的瓶颈。在这个项目中，我们将把计算机科学家、遗传学家和工程师聚集在一起，创建一个人工智能促进的数据分析管道，可以快速评估、预测和解释植物表现。这些成果将提供一条管道，加快选择具有气候适应性并将对环境影响降至最低的高产生物燃料作物。

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.