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首页> 外文会议>Autonomous air and ground sensing systems for agricultural optimization and phenotyping II >Scaling up high throughput field phenotyping of corn and soy research plots using ground rovers
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Scaling up high throughput field phenotyping of corn and soy research plots using ground rovers

机译：使用地面流动站扩大玉米和大豆研究区的高通量田间表型

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摘要

Crop improvement programs require large and meticulous selection processes that effectively and accurately collect and analyze data to generate quality plant products as efficiently as possible, develop superior cropping and/or crop improvement methods. Typically, data collection for such testing is performed by field teams using hand-held instruments or manually-controlled devices. Although steps are taken to reduce error, the data collected in such manner can be unreliable due to human error and fatigue, which reduces the ability to make accurate selection decisions. Monsanto engineering teams have developed a high-clearance mobile platform (Rover) as a step towards high throughput and high accuracy phenotyping at an industrial scale. The rovers are equipped with GPS navigation, multiple cameras and sensors and on-board computers to acquire data and compute plant vigor metrics per plot. The supporting IT systems enable automatic path planning, plot identification, image and point cloud data QA/QC and near real-time analysis where results are streamed to enterprise databases for additional statistical analysis and product advancement decisions. Since the rover program was launched in North America in 2013, the number of research plots we can analyze in a growing season has expanded dramatically. This work describes some of the successes and challenges in scaling up of the rover platform for automated phenotyping to enable science at scale.
机译：作物改良计划需要大而细致的选择过程，这些过程必须有效，准确地收集和分析数据，以尽可能高效地生产优质植物产品，开发出优质的作物和/或作物改良方法。通常，此类测试的数据收集是由现场团队使用手持仪器或手动控制的设备执行的。尽管已采取措施来减少错误，但由于人为错误和疲劳，以这种方式收集的数据可能不可靠，这降低了做出准确选择决策的能力。孟山都的工程团队已经开发出一种高净度移动平台（Rover），以迈向工业规模的高吞吐量和高精度表型化的一步。流动站配备了GPS导航，多个摄像头和传感器以及车载计算机，以获取数据并计算每个地块的植物活力指标。支持性IT系统支持自动路径规划，地块识别，图像和点云数据QA / QC以及近乎实时的分析，其中将结果流式传输到企业数据库，以进行其他统计分析和产品开发决策。自从2013年在北美启动漫游者计划以来，我们可以在一个生长季节分析的研究地块数量急剧增加。这项工作描述了扩大漫游器平台以进行自动表型化以实现大规模科学的一些成功和挑战。

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