A Review of Imaging Techniques for Plant Phenotyping

Li, Lei; Zhang, Qin; Huang, Danfeng

doi:10.3390/s141120078

Cited by 842 publications

(666 citation statements)

References 190 publications

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“…The diversity of crop species and the variety of traits of interest have resulted in the development of a number of different platforms for plant phenotyping (Cobb et al, 2013;Li et al, 2014). Commercial platforms, including the Scanalyzer series from Lemnatec (http:// www.lemnatec.com/products/; accessed February 2016) and the Traitmill platform from CropDesign (http:// www.cropdesign.com/general.php; accessed February 2016), have gained adoption in the research community and have promoted the development of additional software (beyond that which the respective companies provide) to analyze the images produced by the platform (Reuzeau, 2007;Hartmann et al, 2011;Fahlgren et al, 2015a).…”

mentioning

confidence: 99%

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

2016

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Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.

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confidence: 99%

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

2016

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“…Already, there is considerable experimentation with large, tractor-based platforms that carry a set of sensors; traditional remote sensing; and aerial vehicles [6,15,30,31]. Robots offer a wider range of imaging frequencies and techniques, opening new algorithmic possibilities [15,32].…”

Section: Constraints On Image Collection and Processingmentioning

confidence: 99%

An opinion on imaging challenges in phenotyping field crops

Kelly

Vatsa

Mayham

et al. 2015

Machine Vision and Applications

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Almost all the world's food is grown in open fields, where plant phenotypes can be very different from those observed in greenhouses. Geneticists and agronomists studying food crops routinely detect, measure, and classify a wide variety of phenotypes in fields that contain many visually distinct types of a single crop. Augmenting humans in these tasks by automatically interpreting images raises some important and nontrivial challenges for research in computer vision. Nonetheless, the rewards for overcoming these obstacles could be exceptionally high for today's 7 billion people, let alone the 9.6 billion projected by 2050 (United Nations Department of Economic and Social Affairs, Population Division, World Population Prospects: The 2012 Revision). To stimulate dialog between researchers in computer vision and those in genetics and agronomy, we offer our views on three computational challenges that are central to many phenotyping tasks. These are disambiguating one plant from another; assigning an individual plant's organs to it; and identifying field phenotypes from those shown in archival images. We illustrate these challenges with annotated photographs of maize highlighting the regions of interest. We also describe some of the experimental, logistical, and photographic constraints on image collection and processing. While collecting the data sets needed for algorithmic experiments requires sustained collaboration and funding, the images we show and have posted should allow one to consider the problems, think of possible approaches, and decide on the next steps.

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“…Until now, plant phenotyping applications for indoor field such as plant factory and greenhouse have been developed using current technologies of sensor devices and image analysis (Hartmann et al, 2011;Constantino et al, 2015). On the other hand, plant phenotyping for outdoor field, which is laborious, time-consuming and often destructive when manually methods are used, applications are still under development (Araus and Cairns, 2014;Friedli et al, 2016;Schima et al, 2016), and less-laborious and -time-consuming, and non-destructive phenotyping methods are required and need to be developed (Li et al, 2014;Minervini et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Nondestructive Method to Estimate Plant Height, Stem Diameter and Biomass of Rice under Field Conditions Using Digital Image Analysis

Mano

Igawa

2017

J. Environ. Sci. & Natural Resources

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Plant phenotyping intends measuring complex plant traits, and is important in agricultural research for enhancing yield improvement. Manual plant phenotyping is laborious and destructive, and hence a less-laborious and non-destructive method is required. Here, we proposed a nondestructive method to estimate continuous data of plant traits such as height, stem diameter and biomass using a low cost time-lapse camera. The camera was installed at a rice field in Japan, and captured images for four target plants every three hour. The plant height and stem diameter were determined from the images by referencing scale bars that were placed next to the target plants and above the ground surface. Both the height and the diameter were compared to directly measured ones, and the relationships between those were in good agreement. Plant volumes were estimated from the height and stem diameter assuming a shape of rice plant is cylindrical. Above ground biomass without panicles was determined by rice plants sampled from the field. The determined biomass increased in proportion to the plant volume, and its relationship used to produce continuous data of the rice biomass. The results suggest that the proposed method can be considered as a useful tool of the plant phenotyping.

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A Review of Imaging Techniques for Plant Phenotyping

Cited by 842 publications

References 190 publications

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

An opinion on imaging challenges in phenotyping field crops

A Nondestructive Method to Estimate Plant Height, Stem Diameter and Biomass of Rice under Field Conditions Using Digital Image Analysis

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