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Small Unmanned Aerial Systems for High-throughput Phenotyping

机译:小型无人机系统用于高通量表型分析

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摘要

Phenotyping is an important agricultural approach that combines methodologies and protocols to quantify plant growth, structure and composition at different scales of organisation, from organs to canopies. The interest in accurate phenotyping is motivated by the global challenge of ensuring crop yield for food and fuel generation using fewer resources while reducing the environmental impact (e.g., diminishing agricultural land, efficient use of nitrogen fertilisers, and correlating genotype and phenotype traits for advancing crop yield). Traditional phenotyping is based on destructive, labour-intensive and time-consuming approaches that are mainly conducted in a controlled environment with limited coverage such as greenhouses. Such limitations are the cause of phenotyping being the bottleneck of advancing crop yield while reducing the impact of agricultural practices on the environment (e.g. reducing the use of nitrogen-based fertilisers). Therefore, novel phenotyping techniques are critical to alleviate the above-mentioned challenges. In this regard, directly georef-erenced passive and active imaging sensors operating in different portions of the electromagnetic spectrum could be utilised for innovative high-throughput phenotyping, which is non-destructive and non-invasive while being capable of providing abundant and diversified information. Mobile sensor systems onboard tractors, tethered balloons, manned aircrafts, and unmanned aerial systems (LJASs) are becoming popular thanks to their potential for collecting data that is conducive to automated phenotyping. Among the above-mentioned mobile platforms, small unmanned aerial systems (sUASs) are rapidly gaining momentum as one of the best approaches for high-throughput plant phenotyping. The ability of sUASs to fly lower compared with manned aerial systems allows them to collect geospatial data at high resolution while covering larger areas than wheel-based systems. The low cost of consumer-grade sUASs is another advantage that makes them more appealing.
机译:表型分析是一种重要的农业方法,它结合了方法学和协议以量化从器官到冠层的不同组织规模的植物生长,结构和组成。对精确表型的兴趣来自于全球挑战,即使用较少的资源确保作物产量用于粮食和燃料生产,同时减少对环境的影响(例如减少耕地,有效利用氮肥以及将基因型和表型性状相互关联以促进作物生长)让)。传统的表型是基于破坏性,劳动密集型和耗时的方法,这些方法主要在覆盖范围有限的受控环境(例如温室)中进行。这种局限性是表型化的原因,这是提高农作物产量的瓶颈,同时又减少了农业实践对环境的影响(例如,减少了氮基肥料的使用)。因此,新颖的表型分析技术对于缓解上述挑战至关重要。在这方面,可以将在电磁波谱的不同部分中运行的直接地理参考被动和主动成像传感器用于创新的高通量表型分析,该表型是非破坏性和非侵入性的,同时能够提供丰富多样的信息。拖拉机,系留气球,有人驾驶飞机和无人航空系统(LJAS)上的移动传感器系统由于其收集有助于自动表型分析的数据的潜力而变得越来越流行。在上述移动平台中,小型无人机系统(sUAS)迅速获得发展势头,成为高通量植物表型的最佳方法之一。与载人航空系统相比,sUAS具有更低的飞行能力,这使它们能够以高分辨率采集地理空间数据,同时比基于轮机的系统覆盖更大的区域。消费级sUAS的低成本是另一个优势,使其更具吸引力。

著录项

  • 来源
    《GIM international》 |2016年第6期|6-6|共1页
  • 作者

    AYMAN HABIB;

  • 作者单位

    LYLES SCHOOL OF CIVIL ENGINEERING, PURDUE UNIVERSITY, USA;

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  • 原文格式 PDF
  • 正文语种 eng
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