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一种高通量集成高光谱成像与三维测量系统。

A High Throughput Integrated Hyperspectral Imaging and 3D Measurement System.

作者信息

Zhao Huijie, Xu Lunbao, Shi Shaoguang, Jiang Hongzhi, Chen Da

机构信息

School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China.

出版信息

Sensors (Basel). 2018 Apr 2;18(4):1068. doi: 10.3390/s18041068.

DOI:10.3390/s18041068
PMID:29614839
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5948655/
Abstract

Hyperspectral and three-dimensional measurements can obtain the intrinsic physicochemical properties and external geometrical characteristics of objects, respectively. The combination of these two kinds of data can provide new insights into objects, which has gained attention in the fields of agricultural management, plant phenotyping, cultural heritage conservation, and food production. Currently, a variety of sensors are integrated into a system to collect spectral and morphological information in agriculture. However, previous experiments were usually performed with several commercial devices on a single platform. Inadequate registration and synchronization among instruments often resulted in mismatch between spectral and 3D information of the same target. In addition, using slit-based spectrometers and point-based 3D sensors extends the working hours in farms due to the narrow field of view (FOV). Therefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information. Furthermore, fiber-reformatting imaging spectrometry (FRIS) is adopted to acquire the hyperspectral images. Test experiments are conducted for the verification of the system accuracy, and vegetation measurements are carried out to demonstrate its feasibility. The proposed system is an improvement in multiple data acquisition and has the potential to improve plant phenotyping.

摘要

高光谱测量和三维测量可以分别获取物体的内在物理化学性质和外部几何特征。这两类数据的结合能够为物体提供新的见解,在农业管理、植物表型分析、文化遗产保护和食品生产等领域受到了关注。目前,在农业中,多种传感器被集成到一个系统中以收集光谱和形态信息。然而,以往的实验通常是在单个平台上使用几种商用设备进行的。仪器之间登记和同步不足常常导致同一目标的光谱信息和三维信息不匹配。此外,由于视场(FOV)狭窄,使用基于狭缝的光谱仪和基于点的三维传感器会延长在农场的工作时间。因此,我们提出了一种结合立体视觉和光栅色散以同时获取高光谱和三维信息的高通量原型。此外,采用光纤重排成像光谱法(FRIS)来获取高光谱图像。进行测试实验以验证系统精度,并开展植被测量以证明其可行性。所提出的系统在多数据采集方面有所改进,并且具有改善植物表型分析的潜力。

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本文引用的文献

1
Development and evaluation of a field-based high-throughput phenotyping platform.基于田间的高通量表型分析平台的开发与评估
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2
A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results.一种用于在田间条件下对小麦品种进行高通量表型分析的半自动系统:描述与初步结果
Funct Plant Biol. 2012 Nov;39(11):914-924. doi: 10.1071/FP12065.
3
Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring.
田间分析仪:一种用于详细作物监测的自动化机器人田间表型分析平台。
Funct Plant Biol. 2016 Feb;44(1):143-153. doi: 10.1071/FP16163.
4
High Throughput Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging.利用高光谱成像技术对植物叶片化学性质进行高通量分析。
Front Plant Sci. 2017 Aug 3;8:1348. doi: 10.3389/fpls.2017.01348. eCollection 2017.
5
Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis.通过三维成像和短波近红外光谱分析的新型组合技术预测金头鲷(Sparus aurata)的新鲜度
Sensors (Basel). 2016 Oct 19;16(10):1735. doi: 10.3390/s16101735.
6
Holographic fabrication of large-constant concave gratings for wide-range flat-field spectrometers with the addition of a concave lens.通过添加凹透镜用于宽范围平场光谱仪的大常数凹面光栅的全息制造。
Opt Express. 2016 Jan 25;24(2):732-8. doi: 10.1364/OE.24.000732.
7
Hybrid-resolution spectral video system using low-resolution spectral sensor.
Opt Express. 2014 Aug 25;22(17):20311-25. doi: 10.1364/OE.22.020311.
8
Low-cost 3D systems: suitable tools for plant phenotyping.低成本 3D 系统:植物表型分析的适用工具。
Sensors (Basel). 2014 Feb 14;14(2):3001-18. doi: 10.3390/s140203001.
9
Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains.通过空间和光谱域中的随机可分投影实现的压缩高光谱成像。
Appl Opt. 2013 Apr 1;52(10):D46-54. doi: 10.1364/AO.52.000D46.
10
BreedVision--a multi-sensor platform for non-destructive field-based phenotyping in plant breeding.BreedVision--一种用于植物育种中基于现场的无损表型分析的多传感器平台。
Sensors (Basel). 2013 Feb 27;13(3):2830-47. doi: 10.3390/s130302830.