EXC 2070: PhenoRob - Robotics and Phenotyping for Sustainable Crop Production

EXC 2070：PhenoRob - 可持续作物生产的机器人技术和表型分析

• 批准号: 390732324
• 金额: --
• 依托单位: Rheinische Friedrich-Wilhelms-Universität Bonn
• 依托单位国家: 德国
• 项目类别: Clusters of Excellence (ExStra)
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/390732324?language=en
• 关键词: EXC; 2070; PhenoRob; Robotics; Phenotyping

One of the greatest challenges for humanity is to produce sufficient food, feed, fiber, and fuel for an ever-growing world population while simultaneously reducing the environmental footprint of agricultural production. Productive arable land is limited, and the input of agro-chemicals needs to be reduced to curb environmental pollution and halt the decline in biodiversity. Climate change poses additional constraints on crop farming. Achieving sustainable crop production with limited resources is, thus, a task of immense proportions. Our main hypothesis is that a major shift toward sustainable crop production can be achieved via two approaches: (1) multi-scale monitoring of plants and their environment using autonomous robots with automated and individualized intervention and big data analytics combined with machine learning to improve our understanding of the relation between input and output parameters of crop production, and (2) assessing, modeling, and optimizing the implications of the developed technical innovations in a systemic manner.To realize our vision, we will take a technology-driven approach to address the challenging scientific objectives. We foresee novel ways of growing crops and managing fields, and aim at reducing the environmental footprint of crop production, maintaining the quality of soil and arable land, and analyzing the best routes to improve the adoption of technology. The novel approach of PhenoRob is characterized by the integration of robotics, digitalization, and machine learning on one hand, and modern phenotyping, modeling, and crop production on the other. First, we will systematically monitor all essential aspects of crop production using sensor networks as well as ground and aerial robots. This is expected to provide detailed spatially and temporally aligned information at the level of individual plants, nutrient and disease status, soil information as well as ecosystem parameters, such as vegetation diversity. This will enable a more targeted management of inputs (genetic resources, crop protection, fertilization) for optimizing outputs (yield, growth, environmental impact). Second, we will develop novel technologies to enable real-time control of weeds and selective spraying and fertilization of individual plants in field stands. This will help reduce the environmental footprint by reducing chemical input. Third, machine learning applied to crop data will improve our understanding and modeling of plant growth and resource efficiencies and will further assist in the identification of correlations. Furthermore, we will develop integrated multi-scale models for the soil-crop-atmosphere system. These technologies and the gained knowledge will change crop production on all levels. Fourth, in addition to the impact on management decisions at the farm level, we will investigate the requirements for technology adoption as well as socioeconomic and environmental impact of the innovations resulting from upscaling.

人类面临的最大挑战之一是为不断增长的世界人口生产足够的粮食、饲料、纤维和燃料，同时减少农业生产的环境足迹。生产性可耕地有限，需要减少农用化学品的投入，以遏制环境污染，制止生物多样性的下降。气候变化对作物种植造成了更多的限制。因此，以有限的资源实现可持续的作物生产是一项艰巨的任务。我们的主要假设是，向可持续作物生产的重大转变可以通过两种方法实现：（1）使用自动化机器人对植物及其环境进行多尺度监测，自动化和个性化干预以及大数据分析与机器学习相结合，以提高我们对作物生产输入和输出参数之间关系的理解，以及（2）评估，建模，并系统地优化已开发的技术创新的影响。为了实现我们的愿景，我们将采取技术驱动的方法来应对具有挑战性的科学目标。我们预见了种植作物和管理田地的新方法，旨在减少作物生产的环境足迹，保持土壤和耕地质量，并分析提高技术采用率的最佳途径。PhenoRob的新方法的特点是一方面集成了机器人技术、数字化和机器学习，另一方面集成了现代表型分析、建模和作物生产。首先，我们将使用传感器网络以及地面和空中机器人系统地监测作物生产的所有重要方面。预计这将提供在单个植物、营养和疾病状况、土壤信息以及植被多样性等生态系统参数方面的详细的时空一致信息。这将有助于更有针对性地管理投入（遗传资源、作物保护、施肥），以优化产出（产量、生长、环境影响）。第二，我们将开发新技术，以实现实时控制杂草，并在田间对单株植物进行选择性喷洒和施肥。这将有助于通过减少化学品投入来减少环境足迹。第三，应用于作物数据的机器学习将提高我们对植物生长和资源效率的理解和建模，并将进一步帮助识别相关性。此外，我们将开发土壤-作物-大气系统的综合多尺度模型。这些技术和获得的知识将在各个层面上改变作物生产。第四，除了对农场层面的管理决策的影响外，我们还将调查技术采用的要求以及升级所带来的创新的社会经济和环境影响。