Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology

Warman, Cedar; Fowler, John E.

doi:10.1007/s00497-021-00407-2

Cited by 2 publications

(2 citation statements)

References 47 publications

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“…A deep learning neural network was trained to segment the grains on ear masks. It is a powerful tool for high-throughput plant phenotyping, yielding valuable results when large datasets are available [ 25 ]. More specifically, the Mask-RCNN neural network is commonly used as a framework for instance segmentation [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

Oury¹,

Leroux²,

Turc

et al. 2022

Plant Methods

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Background Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

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Section: Methodsmentioning

confidence: 99%

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

Oury¹,

Leroux²,

Turc

et al. 2022

Plant Methods

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show abstract

“…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%

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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Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology

Cited by 2 publications

References 47 publications

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

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

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