Editorial: State-of-the-Art Technology and Applications in Crop Phenomics

Yang, Wanneng; Doonan, John; Hawkesford, Malcolm J.; Pridmore, Tony; Zhou, Ji

doi:10.3389/fpls.2021.767324

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Recently, several advances have been adopted by breeders and plant researchers, but many attempts remain at early stages (White et al, 2012;Juliana et al, 2019). New tools derived from some academic research have often worked at relatively small scale and with limited accessibility as a result of bespoke hardware, proprietary software and specialized packages, preventing them from being employed easily (Yang et al, 2020(Yang et al, , 2021. Furthermore, to exploit genomic resources, traits of interest and genetic diversity need to be assessed across sites and seasons, demanding accessible data collection and analysis toolkits (Naito et al, 2017;Atkinson et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

AirMeasurer: open‐source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice

Sun

Zhao

et al. 2022

New Phytologist

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Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging.Here, we present AIRMEASURER, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials.We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping.We found that the AIRMEASURER-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.

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Section: Introductionmentioning

confidence: 99%

AirMeasurer: open‐source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice

Sun

Zhao

et al. 2022

New Phytologist

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“…High‐throughput plant phenotyping via computer vision (CV) and machine learning (ML) can be used to address a diversity of phenotyping challenges in plant sciences, including increasing plant breeding efficiency and understanding the molecular underpinnings of traits of interest (Gaillard et al., 2020; Gehan et al., 2017; Shakoor et al., 2019). In the last decade, many high‐throughput plant phenotyping methods have improved phenotyping by increasing the number of individuals phenotyped, providing novel quantitative phenotyping to previously qualitative approaches, increasing the speed at which features are measured, and reducing subjectivity, time, and labor (Yang et al., 2021). Deployment of high‐throughput phenotyping applications can be split into collection, extraction, and modeling.…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods can generate massive amounts of data, which must be efficiently stored, processed, and managed to maximize its utility (Coppens et al., 2017). Image data can vary by type such as digital, near‐infrared, fluorescence, thermal, multi/hyperspectral, and 3D imaging (Yang et al., 2021). There are many methods used for data extraction, all of which must be developed for a specific data type.…”

Section: Introductionmentioning

confidence: 99%

“…This model is applied to new images to extract relevant information. For example, these models have been for semantic segmentation of maize ears, Arabidopsis leaves, maize kernels, and rice foliar diseases (S. Chen et al., 2021; Hüther et al., 2020; Shakoor et al., 2019; Warman & Fowler, 2021; Yang et al., 2021). Although high‐throughput plant phenotyping can address major phenotyping bottlenecks in plant science research, digital phenotyping comes with its own set of challenges that need to be addressed to obtain the desired information accurately and efficiently.…”

Section: Introductionmentioning

confidence: 99%

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EarCV: An open‐source, computer vision package for maize ear phenotyping

González

Ghosh

Colantonio

et al. 2022

The Plant Phenome Journal

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Fresh market sweet corn (Zea mays L.) is a row crop commercialized as a vegetable, resulting in strict expectations for ear size, color, and shape. Ear phenotyping in breeding programs is typically done manually and can be subjective, time consuming, and unreliable. Computer vision tools have enabled an inexpensive, high‐throughput, and quantitative alternative to phenotyping in agriculture. Here we present a computer vision tool using open‐source Python and OpenCV to measure yield component and quality traits relevant to sweet corn from photographs. This tool increases accuracy and efficiency in phenotyping through high‐throughput, quantitative feature extraction of traits typically measured qualitatively. EarCV worked in variable lighting and background conditions, such as under full sun and shade and against grass and dirt backgrounds. The package compares ears in images taken at varying distances and accurately measures ear length and ear width. It can measure traits that were previously difficult to quantify such as color, tip fill, taper, and curvature. EarCV allows users to phenotype any number of ears, dried or fresh, in any orientation while tolerating some debris and silk noise. The tool can categorize husked ears according to the predefined USDA quality grades based on length and tip fill. We show that the information generated from this computer vision approach can be incorporated into breeding programs by analyzing hybrid ears, capturing heritability of yield component traits, and detecting phenotypic differences between cultivars that conventional yield measurements cannot. Ultimately, computer vision can reduce the cost and resources dedicated to phenotyping in breeding programs.

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An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management

Yang,

Feng,

et al. 2024

Methods in Molecular Biology

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Editorial: State-of-the-Art Technology and Applications in Crop Phenomics

Cited by 7 publications

References 7 publications

AirMeasurer: open‐source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice

AirMeasurer: open‐source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice

EarCV: An open‐source, computer vision package for maize ear phenotyping

An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management

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