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Vinobot和Vinoculer:用于高通量田间表型分析的两个机器人平台。

Vinobot and Vinoculer: Two Robotic Platforms for High-Throughput Field Phenotyping.

作者信息

Shafiekhani Ali, Kadam Suhas, Fritschi Felix B, DeSouza Guilherme N

机构信息

ViGIR Lab, Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.

Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA.

出版信息

Sensors (Basel). 2017 Jan 23;17(1):214. doi: 10.3390/s17010214.

Abstract

In this paper, a new robotic architecture for plant phenotyping is being introduced. The architecture consists of two robotic platforms: an autonomous ground vehicle (Vinobot) and a mobile observation tower (Vinoculer). The ground vehicle collects data from individual plants, while the observation tower oversees an entire field, identifying specific plants for further inspection by the Vinobot. The advantage of this architecture is threefold: first, it allows the system to inspect large areas of a field at any time, during the day and night, while identifying specific regions affected by biotic and/or abiotic stresses; second, it provides high-throughput plant phenotyping in the field by either comprehensive or selective acquisition of accurate and detailed data from groups or individual plants; and third, it eliminates the need for expensive and cumbersome aerial vehicles or similarly expensive and confined field platforms. As the preliminary results from our algorithms for data collection and 3D image processing, as well as the data analysis and comparison with phenotype data collected by hand demonstrate, the proposed architecture is cost effective, reliable, versatile, and extendable.

摘要

本文介绍了一种用于植物表型分析的新型机器人架构。该架构由两个机器人平台组成:一个自主地面车辆(Vinobot)和一个移动观测塔(Vinoculer)。地面车辆从单株植物收集数据,而观测塔则俯瞰整个田地,识别特定植物以便Vinobot进行进一步检查。这种架构的优势有三个方面:第一,它使系统能够在白天和夜晚的任何时间检查大片田地,同时识别受生物和/或非生物胁迫影响的特定区域;第二,它通过全面或有选择地从群体或单株植物获取准确详细的数据,在田间提供高通量植物表型分析;第三,它无需昂贵且笨重的飞行器或同样昂贵且受限的田间平台。正如我们的数据收集和三维图像处理算法的初步结果,以及与手工收集的表型数据的数据分析和比较所表明的那样,所提出的架构具有成本效益、可靠、通用且可扩展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95c7/5298785/6d44b56b87ce/sensors-17-00214-g001.jpg

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