Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms

被引：112
|
作者：
Perez-Sanz, Fernando [1 ]
Navarro, Pedro J. [2 ]
Egea-Cortines, Marcos [1 ]
机构：
[1] Univ Politecn Cartagena, Inst Biotecnol Vegetal, ETSIA, Genet, Cartagena 30202, Spain
[2] Univ Politecn Cartagena, Inst Biotecnol Vegetal, Genet, Campus Muralla del Mar S-N, Cartagena 30202, Spain
来源：
GIGASCIENCE | 2017年 / 6卷 / 11期
关键词：
Algorithms; artificial vision; deep learning; hyperspectral cameras; machine learning; segmentation; LEAF-AREA INDEX; AUTOMATED CHARACTERIZATION; INFRARED THERMOGRAPHY; MULTISPECTRAL LIDAR; CHLOROPHYLL CONTENT; VEGETATION INDEXES; TERRESTRIAL LIDAR; DISEASE DETECTION; SPECTRAL INDEXES; CIRCADIAN-RHYTHM;
D O I：
10.1093/gigascience/gix092
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion.
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页数：18
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