Phenomics: Technologies and Applications in Plant and Agriculture

Rahman, Hifzur; Ramanathan, V.; Jeyabalan, Nallathambi; Sasikala, R.; Sathishraj, Rajendran; Ramachandran, Mahendran; Sudheer, Pamidimarri D. V. N.; Chauhan, S. M. S.; Natesan, Senthil; Raveendran, M.

doi:10.1007/978-81-322-2172-2_13

Cited by 3 publications

(3 citation statements)

References 187 publications

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“…Here in controlled conditions either the plants can be moved to the sensors or sensors can be moved to the plants whereas in the field condition only sensors can be moved to the plants. Phenomics are of two types i.e., forward and reverse phenomics (Rahman et al, 2015) [49] . In forward phenomics, phenotyping is done for large number of plant population that helps to identify the trait/plant that are suitable for a particular situation.…”

Section: Fig 1: Phenotypementioning

confidence: 99%

High-throughput crop phenotyping in vegetable crops

Ilakiya¹,

Parameswari²,

Davamani³

et al. 2020

Pharma Innovation

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In the era of climate change with increasing global populace, there is a requisite for developing high yielding climate resilient varieties in a shorter span. Integrating the modern techniques of highthroughput phenomics with the gains of genomics would lead to increase in the efficiency of breeding techniques for the rapid development of such varieties. The development of plant phenomics facilities in vegetable breeding have been developing in recent years. High-throughput plant phenotyping involves the use of various imaging techniques, where the images are collected and are then analyzed in some specialized software's. Apart from getting the phenotype traits of the plant these techniques are used to analyze different biotic and abiotic stress traits as well as the nutrient status of vegetable crops. The highthroughput plant phenotyping platforms would help the vegetable breeder in saving their time, as the conventional phenotyping is a time-consuming process. HTTPs delivers more reliable and accurate results. It would also help to find more pertinent solutions for the serious issues that limits the vegetable crop production.

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Section: Fig 1: Phenotypementioning

confidence: 99%

High-throughput crop phenotyping in vegetable crops

Ilakiya¹,

Parameswari²,

Davamani³

et al. 2020

Pharma Innovation

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“…As documented in review papers, various software has been developed and used to analyze the locomotion of animals like zebrafish (Danio rerio) and C. elegans, as well as mice [21,22]. In addition, there is a variety of software that can detect plants, classify phenotypes [23,24], and classify cell types [25].…”

Section: Introductionmentioning

confidence: 99%

Image Processing and Analysis for Biotechnology and Bioprocess Engineering

Jung

2024

Applied Sciences

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“…Extending field-based studies and sample sizes is a widely shared goal for future phenotyping scenarios [ 15 , 16 ]. Indeed, phenotyping rather than genotyping is recognized as the bottleneck limiting advances [ 17 , 18 ], given inexpensive next-generation sequencing technologies that have paved the way for characterizing the genotypes of diversity panels of thousands of recombinant inbred lines [ 19 ]. In response, a number of national and international efforts, including the International Plant Phenotyping Network, have established “plant phenomics” centers to quantify plant phenotypes and their genetic origin [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics

et al. 2015

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BackgroundPlant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput.DescriptionHere, we present an open-source phenomics platform “DIRT”, as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute “commons” enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size.ConclusionDIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://dirt.iplantcollaborative.org/ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-015-0093-3) contains supplementary material, which is available to authorized users.

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Phenomics: Technologies and Applications in Plant and Agriculture

Cited by 3 publications

References 187 publications

High-throughput crop phenotyping in vegetable crops

High-throughput crop phenotyping in vegetable crops

Image Processing and Analysis for Biotechnology and Bioprocess Engineering

Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics

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