DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics
Abstract Background Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep lea...
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doaj-9f6b83466925427494c26e1176cd2ca12021-08-22T11:13:06ZengBMCPlant Methods1746-48112021-08-0117111910.1186/s13007-021-00787-6DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomicsLydia Kienbaum0Miguel Correa Abondano1Raul Blas2Karl Schmid3Institute of Plant Breeding, Seed Science and Population Genetics, University of HohenheimInstitute of Plant Breeding, Seed Science and Population Genetics, University of HohenheimUniversidad National Agraria La Molina (UNALM)Institute of Plant Breeding, Seed Science and Population Genetics, University of HohenheimAbstract Background Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy ( $$r=0.99$$ r = 0.99 ). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10–20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. Conclusions Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.https://doi.org/10.1186/s13007-021-00787-6Maize cobDeep learningGenebank PhenomicsObject detectionHigh-throughput plant phenotypingImage analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lydia Kienbaum Miguel Correa Abondano Raul Blas Karl Schmid |
spellingShingle |
Lydia Kienbaum Miguel Correa Abondano Raul Blas Karl Schmid DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics Plant Methods Maize cob Deep learning Genebank Phenomics Object detection High-throughput plant phenotyping Image analysis |
author_facet |
Lydia Kienbaum Miguel Correa Abondano Raul Blas Karl Schmid |
author_sort |
Lydia Kienbaum |
title |
DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics |
title_short |
DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics |
title_full |
DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics |
title_fullStr |
DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics |
title_full_unstemmed |
DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics |
title_sort |
deepcob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics |
publisher |
BMC |
series |
Plant Methods |
issn |
1746-4811 |
publishDate |
2021-08-01 |
description |
Abstract Background Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy ( $$r=0.99$$ r = 0.99 ). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10–20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. Conclusions Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding. |
topic |
Maize cob Deep learning Genebank Phenomics Object detection High-throughput plant phenotyping Image analysis |
url |
https://doi.org/10.1186/s13007-021-00787-6 |
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