Field-based high-throughput plant phenotyping (FB-HTPP) has been a primary focus for crop improvement to meet the demands of a growing population in a changing environment. Over the years, breeders, geneticists, physiologists, and agronomists have been able to improve the understanding between complex dynamic traits and plant response to changing environmental conditions using FB-HTPP. However, the volume, velocity, and variety of data captured by FB-HTPP can be problematic, requiring large data stores, databases, and computationally intensive data processing pipelines. To be fully effective, FB-HTTP data workflows including applications for database implementation, data processing, and data interpretation must be developed and optimized. At the US Arid Land Agricultural Center in Maricopa Arizona, USA a data workflow was developed for a terrestrial FB-HTPP platform that utilized a custom Python application and a PostgreSQL database. The workflow developed for the HTPP platform enables users to capture and organize data and verify data quality before statistical analysis. The data from this platform and workflow were used to identify plant lodging and heat tolerance, enhancing genetic gain by improving selection accuracy in an upland cotton breeding program. An advantage of this platform and workflow was the increased amount of data collected throughout the season, while a main limitation was the start-up cost.

中文翻译：

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通过基于陆地的高通量植物表型系统支持植物育种决策的数据工作流程。


田间高通量植物表型分析 (FB-HTPP) 一直是作物改良的主要焦点，以满足不断变化的环境中不断增长的人口的需求。多年来，育种家、遗传学家、生理学家和农学家已经能够使用 FB-HTPP 提高复杂动态性状和植物对环境条件变化的反应之间的理解。然而，FB-HTPP 捕获的数据量、速度和种类可能存在问题，需要大型数据存储、数据库和计算密集型数据处理管道。为了充分有效，必须开发和优化 FB-HTTP 数据工作流程，包括用于数据库实施、数据处理和数据解释的应用程序。在美国亚利桑那州马里科帕的美国干旱地区农业中心，为地面 FB-HTPP 平台开发了一个数据工作流程，该平台利用了自定义 Python 应用程序和 PostgreSQL 数据库。为 HTPP 平台开发的工作流程使用户能够在统计分析之前捕获和组织数据并验证数据质量。该平台和工作流程的数据用于识别植物倒伏和耐热性，通过提高陆地棉育种计划的选择准确性来提高遗传增益。该平台和工作流程的优点是整个赛季收集的数据量增加，而主要限制是启动成本。